diff --git a/_site/background.html b/_site/background.html deleted file mode 100644 index df93e61..0000000 --- a/_site/background.html +++ /dev/null @@ -1,455 +0,0 @@ - - - - - - - - - -Gov 50 Final Project - Background - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Background

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On average, police in the United States shoot and kill more than 1,000 people every year, according to an ongoing analysis by The Washington Post.

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Proposal

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We propose a case study to explore the relationship between police residence and fatal police shootings, employing advanced data science methodologies. Focusing on officers residing in the cities they serve, our project aims to uncover insights and patterns that contribute to a nuanced understanding of this complex issue.

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Objectives:

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  1. Investigate the correlation between police residence and fatal police shootings.
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  3. Utilize a comprehensive dataset spanning 2015 to 2023, focusing on police agencies involved in at least one fatal shooting.
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  5. Apply advanced statistical methods and machine learning techniques to identify patterns and potential biases.
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  7. Examine disparities in incident rates based on officers’ residency status, considering demographic, socioeconomic, and policing variables.
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Methodology:

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    • Compile a dataset comprising information on police agencies involved in fatal police shootings.
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    • Compile a data set of census variables such as officer residency, race, community demographics, and departmental policies.
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    • Employ advanced statistical methods and machine learning techniques to discern patterns and correlations.
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    • Conduct a comprehensive exploration of variables influencing fatal police shootings.
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Hypothesis and Expected Outcomes:

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We will conduct two hypothesis tests to analyze both;

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  • the nominal relationship between an increasing proportion of in-city officer residency and number of fatal police shooting deaths and

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  • the categorical difference in fatal police shooting deaths between cities where a majority or or minority of police officers live in the city.

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  1. Inference for a Difference in Proportions

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    • \(H_0\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.

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    • \(H_A\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.

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      • \(H_0 : p\_{maj} − p\_{min} = 0\), or equivalently \(H_0 : p\_{maj} = p\_{min}\)
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      • \(H_A : p\_{maj} − p\_{min} < 0\), or equivalently \(H_A : p\_{maj} < p\_{min}\)
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  3. Inference for a Correlation

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    • \(H_O\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.

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    • \(H_A\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.

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      • \(H_0 : \rho = 0\)

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      • \(H_0 : \rho \neq 0\)

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The Washington Post Fatal Force Database

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In 2015, The Washington Post began tracking details about each police-involved killing in the United States — the race of the deceased, the circumstances of the shooting, whether the person was armed and whether the person was experiencing a mental-health crisis — by manually culling local news reports, collecting information from law enforcement websites and social media, and monitoring independent databases such as Fatal Encounters and the now-defunct Killed by Police project. In many cases, The Post conducts additional reporting.

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In 2022, The Post updated its database to standardize and publish the names of the police agencies involved in each shooting to better measure accountability at the department level.

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The 2014 killing of Michael Brown in Ferguson, Mo. began a protest movement culminating in the Black Lives Matter movement and an increased focus on police accountability nationwide. In this data set, The Post tracks only shootings with circumstances closely paralleling those like the killing of Brown — incidents in which a police officer, in the line of duty, shoots and kills a civilian. The Post is not tracking deaths of people in police custody, fatal shootings by off-duty officers or non-shooting deaths in this data set.

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The FBI and the Centers for Disease Control and Prevention log fatal shootings by police, but officials acknowledge that their data is incomplete. Since 2015, The Post has documented more than twice as many fatal shootings by police as recorded by federal officials on average annually. That gap has widened in recent years, as the FBI in 2021 tracked only a third of departments’ fatal shootings.

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Most Police Don’t Live In The Cities They Serve

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In Ferguson, Missouri, where protests lamented for months following the shooting of a teenager by a police officer this month, more than two-thirds of the civilian population is black. Only 11 percent of the police force is. The racial disparity is troubling enough on its own, but it’s also suggestive of another type of misrepresentation. Given Ferguson’s racial gap, it’s likely that many of its police officers live outside city limits.

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If so, Ferguson would have something in common with most major American cities. In about two-thirds of the U.S. cities with the largest police forces, the majority of police officers commute to work from another town.

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On average among the 75 American cities with the largest police forces, 49 percent of black police officers and 47 percent of Hispanic officers live within the city limits. But just 35 percent of white police officers do. The disparity is starkest in cities with largely black populations. In Detroit, for example, 57 percent of black police officers live in the city but just 8 percent of white ones do. Memphis, Tennessee; Baltimore; Birmingham, Alabama; and Jackson, Mississippi — also majority black — likewise have large racial gaps in where their police officers live.

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Codebook

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Explanatory Variables

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NameDescription
cityU.S. city
police_force_sizeNumber of police officers serving that city
allPercentage of the total police force that lives in the city
whitePercentage of white (non-Hispanic) police officers who live in the city
non-whitePercentage of non-white police officers who live in the city
blackPercentage of black police officers who live in the city
hispanicPercentage of Hispanic police officers who live in the city
asianPercentage of Asian police officers who live in the city
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Incident Information

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NameDescription
idA unique identifier for each fatal police shooting incident.
dateThe date of the fatal shooting.
body_cameraWhether news reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.
cityThe municipality where the fatal shooting took place
countyCounty where the fatal shooting took place.
stateThe two-letter postal code abbreviation for the state in which the fatal shooting took place.
latitudeThe latitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses. Please note that the precision and accuracy of incident coordinates varies depending on the precision of the input address which is often only available at the block level.
longitudeThe longitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses.
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Agency Information

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Description
idDepartment Database Id
nameDepartment Name
stateState in which the agency is located.
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Project thoughts

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I am interested in exploring data related to…

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  • Political Extremism
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  • Black American Opinion
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Data

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library(tidyverse)
-library(usmap)
-library(sf)
-library(infer)
-library(moderndive)
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##Tidying Data
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-#creating dfs from .csv files
-police_locals <- read_csv("data/police-locals.csv")
-agencies <- read_csv("data/fatal-police-shootings-agencies.csv")
-shootings <- read_csv("data/fatal-police-shootings-data.csv")
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-#removing old `city` tag from data set that we created when decatenated the city names
-police_locals <- police_locals |>
-  select(-city_old)
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-# creating `agencies` df with just police departments
-agencies <- agencies |>
-  filter(grepl("department", tolower(name))) |>
-  filter(!grepl("county", tolower(name)))
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-#creating binned categorical account of if shooting victim was `armed`
-shootings <- shootings |>
-  mutate(armed = case_when(is.na(armed_with) ~ "NO",
-                           armed_with == "unarmed" ~ "NO",
-                           armed_with == "unknown" ~ "NO",
-                           armed_with == "undetermined" ~ "NO",
-                           armed_with == "gun" ~ "YES",
-                           armed_with == "knife" ~ "YES",
-                           armed_with == "blunt_object" ~ "YES",
-                           armed_with == "other" ~ "YES",
-                           armed_with == "replica" ~ "YES",
-                           armed_with == "vehicle" ~ "YES"))
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-#creating df with only agency `names`, `id`, and `state`
-agencies_ids <- agencies |>
-  select(name, id, state)
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-#creating df with `city`, `agency`, and `state` info for each shooting
-shooting_agencies <- shootings |>
-  select(city, agency_ids, state)
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-#changing `shooting` var in `shooting_agencies` df to numeric
-shooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)
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-#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`
-agencies_w_cities <- agencies_ids |>
-  left_join(shooting_agencies, by = c("id" = "agency_ids", "state" = "state")) |>
-  drop_na(city) |>
-  distinct(id, .keep_all = TRUE)
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-#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`
-agencies_census <- agencies_w_cities |>
-  full_join(police_locals, by = c("city" = "city", "state" = "state")) |>
-  drop_na(police_force_size) |>
-  distinct(id, .keep_all = TRUE) |>
-  mutate(majority = if_else(all >= 0.5, "TRUE", "FALSE"))
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-#creating df of only shootings involving agencies within `agencies` df
-shootings_case <- shootings |>
-  right_join(agencies_census, by = c("city" = "city", "state" = "state")) |>
-  select(-agency_ids) |>
-  rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>
-  select(-location_precision, -race_source)
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#count shootings by agency
-shootings_by_agency <- shootings_case |>
-  count(agency)
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-#find top 25 agencies with the most shootings
-top_25_agencies <- shootings_by_agency |>
-  slice_max(n, n = 25)
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-# visulize top 25 agencies with the most shootings
-ggplot(data = top_25_agencies,
-       mapping = aes(x = agency, y = n)) +
-  geom_col() +
-  theme(axis.text.x = element_text(angle = 75,
-                                    vjust = 1,
-                                    hjust = 1,
-                                    margin = margin(t = 5, b = 5)))
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#mapping Locations of Police-Involved Shootings between 2015 and 2023
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-#load geo-viz libraries
-library(ggmap)
-library(maps)
-library(mapdata)
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-#create blank map
-usa <- map_data("usa")
-states <- map_data("state")
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-#add locations of shootings to maps
-shot_map <- ggplot(data = states) + 
-  geom_polygon(aes(x = long, y = lat, fill = group, group = group), color = "white") + 
-  coord_fixed(1.3) +
-  guides(fill=FALSE) +  # do this to leave off the color legend
-  geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = "black", size = .2) +
-  geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = "red", size = .1) +
-  labs(title = "Locations of Police-Involved Shootings between 2015 and 2023",
-       captions = "This is only includes cities where we have agency census data.",
-       x = "Longitude",
-       y = "Latitude")
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#creating df with total shootings per agency and census data
-agencies_census <- agencies_census |>
-  left_join(shootings_by_agency, by = c("name" = "agency"))
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-#prelim visualization of relationship between percentage of officer residency and number of fatal shootings per agency
-agencies_census |>
-  ggplot(mapping = aes(x = all, y = n)) +
-  geom_point() +
-  geom_smooth(method = "lm", se = TRUE)
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#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside
-p0 <- shootings_case |>
-  ggplot(aes(x = majority, fill = armed)) +
-  geom_bar() + 
-  labs(title = "Shootings in Cities where a Majority of Officers Reside",
-       caption = "This is only includes shootings where we have agency census data.",
-       x = "Does a majority a of the total police force live in the city?",
-       y = "Number of fatal shootings",
-       fill = "Victim Armed?")
-p0
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#calculate mean number of shootings per agency in cities where a majority of officers reside in the city
-majority_mean <- shootings_case |>
-  filter(majority == TRUE) |>
-  count(agency) |>
-  summarize(maj_mean = mean(n))
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-#calculate mean number of shootings per agency in cities where a minority of officers reside in the city
-minority_mean <- shootings_case |>
-  filter(majority == FALSE) |>
-  count(agency) |>
-  summarize(min_mean = mean(n))
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-#calculate a difference in means between the `majority` and `minority`
-diff_in_means <- majority_mean - minority_mean
-diff_in_means
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  maj_mean
-1   -2.575
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#tidy table
-knitr::kable(head(diff_in_means))
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maj_mean
-2.575
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#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency
-fit <- lm(n ~ all, data = agencies_census)
-fit
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-Call:
-lm(formula = n ~ all, data = agencies_census)
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-Coefficients:
-(Intercept)          all  
-    35.7782      -0.5874  
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#tidy `fit`
-p1 <- get_regression_table(fit)
-knitr::kable(head(p1))
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termestimatestd_errorstatisticp_valuelower_ciupper_ci
intercept35.7786.8875.1950.00022.12649.430
all-0.58714.902-0.0390.969-30.13028.955
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#add `armed` and `majority` to `shootings_by_agency` df
-shootings_by_agency_census <- shootings_case |>
-  group_by(agency) |>
-  count(armed) |>
-  drop_na(n, armed) |>
-  right_join(agencies_census, by = c("agency" = "name")) |>
-  distinct(armed, .keep_all = TRUE)
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-shootings_by_agency_census <- shootings_by_agency_census |>
-  select(n.x, armed, all) 
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-#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency
-fit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)
-fit_multi
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-Call:
-lm(formula = n.x ~ all + armed, data = shootings_by_agency_census)
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-Coefficients:
-(Intercept)          all     armedYES  
-      4.117        1.211       24.921  
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#tidy `fit_multi`
-p2 <- get_regression_table(fit_multi)
-knitr::kable(head(p2))
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termestimatestd_errorstatisticp_valuelower_ciupper_ci
intercept4.1173.3621.2250.222-2.51210.747
all1.2116.3350.1910.849-11.28113.702
armed: YES24.9212.8918.6190.00019.21930.622
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#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency
-ggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +
-  geom_jitter(width = 0.10, height = 0, alpha = 0.45) +
-  geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
-  labs(title = "Number of Shootings on a Scale of Police Force Residency",
-       x = "Percentage of the total police force that lives in the city",
-       y = "Number of fatal shootings in that city")
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#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency
-ggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +
-  geom_jitter(width = 0.10, height = 0, alpha = 0.45) +
-  geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
-  labs(title = "Number of Shootings on a Scale of Police Force Residency",
-       x = "Percentage of the total police force that lives in the city",
-       y = "Number of fatal shootings in that city",
-       color = "Victim Armed?")
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The model equation for fit is:

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[ = 35.7782 - 0.5874 ]

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Interpretation:

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This model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.

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The model equation for fit_multi considering victim armament (armed) is:

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[ = 4.117 + 1.211 + 24.921 ]

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In summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.

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#generate null distribution
-null_dist <- agencies_census |>
-  specify(n ~ majority) |>
-  hypothesize(null = "independence") |>
-  generate(reps = 1000, type = "permute") |>
-  calculate(stat = "diff in means", order = c("TRUE", "FALSE"))
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-#compute observed test statistic
-test_stat <- agencies_census |>
-  specify(n ~ majority) |>
-  calculate(stat = "diff in means", order = c("TRUE", "FALSE"))
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-#visualize p-value
-null_dist |>
-  visualize() +
-  shade_p_value(obs_stat = test_stat, direction = "less")
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#compute p-value
-  null_dist |>
-  get_p_value(obs_stat = test_stat, direction = "less")
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# A tibble: 1 × 1
-  p_value
-    <dbl>
-1   0.268
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Inference for a Difference in Means

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\(H_0 : \mu_{maj} − \mu_{min} = 0\), or equivalently \(H_0 : \mu_{maj} = \mu_{min}\)\(H_A : \mu_{maj} − \mu_{min} < 0\), or equivalently \(H_A : \mu_{maj} < \mu_{min}\)

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#generate null distribution
-null_dist_cor <- agencies_census |>
-  specify(n ~ white) |>
-  hypothesize(null = "independence") |>
-  generate(reps = 1000, type = "permute") |>
-  calculate(stat = "correlation")
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-#compute observed test statistic
-test_stat_cor <- agencies_census |>
-  specify(n ~ white) |>
-  calculate(stat = "correlation")
-test_stat_cor
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Response: n (numeric)
-Explanatory: white (numeric)
-# A tibble: 1 × 1
-     stat
-    <dbl>
-1 -0.0470
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#visualize p-value
-null_dist_cor |>
-  visualize() +
-  shade_p_value(obs_stat = test_stat, direction = "two.sided")
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-

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#compute p-value
-null_dist_cor |>
-  get_p_value(obs_stat = test_stat, direction = "two.sided")
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# A tibble: 1 × 1
-  p_value
-    <dbl>
-1       0
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Inference for a Correlation

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Fatal Force Database (v2)

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About the data

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Each day researchers at The Post identify and manually record data for fatal police shootings in the United States. Each record requires at least two sources and must be approved by our editors before being publicly released.

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Data about each fatal shooting is provided via two comma-separated value (CSV) files:

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Death Record data for each victim and incident is included in /v2/fatal-police-shootings-data.csv.

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New in v2 is a second Agencies csv, /v2/fatal-police-shootings-agencies.csv, which contains data for police agencies involved in at least one fatal police shooting since 2015. The agencies csv has a field called agency_ids, which can be used to associate each death record with the agencies involved.

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To enable joining this dataset with other federal law enforcement datasets, in 2022 The Post undertook an effort to increase the coverage of federal Originating Agency Identifier (ORI) codes recorded for agencies in the database. Using agency data from the FBI and Department of Justice, reporters did a combination of automated and manual name and ORI code matching to fill in missing ORI code data. They also standardized department names. Agency naming, organization, hierarchy and classification varies from state to state. For this reason, sub-agencies and local troops, regions and posts have been aggregated into their parent agencies, meaning that in some cases an individual agency will have multiple associated ORI codes. For instance, a state police department may have the ORI codes of multiple barracks associated with it.

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Major data schema changes between version 1 and version 2

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The Post created version 2 of this dataset in 2022 to refine and better define data categories, as well as improve documentation about how the data is obtained and classified. In addition to the altering of fields and formatting, a separate agencies dataset has been added which includes information on the law enforcement agencies which have been involved in at least one fatal shooting since 2015.

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High-level summary of major changes to the data schema introduced in version 2:

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V1 fieldV2 FieldContext
manner_of_deathfield removedThis database tracks fatal police shootings. Other weapons (e.g. a Taser) may be involved in the encounter, but the manner of death for victims in these records is always a police shooting. Thus, the field was removed.
race (single)race (multiple)Field converted from a single option to a list of options. Previously multi-racial or multi-ethnic victims were tracked using the Other option. This update allows us to more accurately categorize shootings of individuals with multiple ethnic and racial backgrounds.
race_sourceThis field was added to identify the source from which race was determined, which can range from court documents to a researchers’ assessment based on a victim’s photograph.
threat_levelthreat_typeRenamed field to more accurately portray data we have collected, while removing implication of hierarchy.
signs_of_mental_illnesswas_mental_illness_relatedRenamed field to more accurately portray data we have collected when news accounts or police reports reflect a victim may have had a history of mental health issues or was experiencing mental distress at the time of the shooting.
fleeflee_statusRenamed field to more accurately portray data we collected
armedarmed_withThis field originally included the specific weapon if the victim had one and otherwise indicated unarmed, undetermined, or unknown. It has been updated to use federal classifications based on NIBRS, the national incident-level crime reporting system. toy category has been renamed to replica.
is_geocoding_exactfield removedLocation information for an incident is often only available at the block level (eg. 100 Block of Main St). Precision and accuracy of geocoded location information was not sufficiently represented by this field.
location_precisionNew field to indicate the precision level of the location data which was geocoded to generate the record’s coordinate data.
agency_idsList of agency ids to enable joining with the agencies csv.
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Death Records

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File: /v2/fatal-police-shootings-data.csv

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Summary: Death record data about each incident and victim. The file can be downloaded at this URL. Victim information is obtained from any available recordings of the incident, news accounts, court records and/or official statements.

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Fields:

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DescriptionTypeExample value
Incident information
idA unique identifier for each fatal police shooting incident.number2
dateThe date of the fatal shooting.string
YYYY-MM-DD
2020-06-25
body_cameraWhether news reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.boolean True/ FalseTrue
cityThe municipality where the fatal shooting took placestringNew Orleans
countyCounty where the fatal shooting took plce.stringBrown
stateThe two-letter postal code abbreviation for the state in which the fatal shooting took place.stringLA
latitudeThe latitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses. Please note that the precision and accuracy of incident coordinates varies depending on the precision of the input address which is often only available at the block level.decimal number47.246826
longitudeThe longitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses.decimal number-123.121592
location_precisionIndicates the precision level of the input which was geocoded to generate the coordinate data.string

options:
-address: Specific address, eg: 1 Main St.
-poi_small: A small point of interest, eg: Jim's Pizza.
-intersection: Intersection of two roads, eg: 6th Avenue and Izabel Street
-poi_large: A large point of interest, eg: Central Mall.
-block: Block level address, eg: 100 Block of Main St.
-road: Road name, eg: Glenwood Drive
-highway: Highway name, eg: Georgia Highway 11
-not_available: Location was entered before current methodology was established.
block
Victim information
nameThe name of the victim.stringJohn Doe
ageThe age of the victim at the time of the incident.number23
genderThe gender of the victim. The Post identifies victims by the gender they identified with if reports indicate that it differs from their biological sex.string

options:
- male
- female
- non-binary
- --: Unknown
male
raceThe race and ethnicity (where known) of the victim. May contain multiple values to accommodate for multi-racial or several racial and ethnic identifications. Race has been included where news accounts, police reports or other official documents specifically mention a victim’s race or where researchers were able to make a visual determination on racial identification through photos. With the introduction of v2 of the database, the Post has began tracking multiple race and ethnicity designations; prior to 2021, only one race or ethnicity was assigned to most victims.string

options:
- W: White
- B: Black
- A: Asian heritage
- N: Native American
- H: Hispanic
- O: Other
- --: Unknown
"B;H"
race_sourceSourcing methodology for victim race data.string

options:
- public_record
- clip
- photo
- other
- not_available: Indicates older records which have race populated but The Post does not have details on the methodology because it was collected before the current structure was introduced.
- undetermined: When The Post’s race research avenues have been exhausted and race is still unknown, this indicates that race is still unknown, but research is complete.
- null
public_record
was_mental_illness_relatedWhether news reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.boolean True/FalseTrue
threat_type*Actions the victim took leading up to the fatal shooting.string

options:
- shoot: The victim fired a weapon.
- point :The victim pointed a weapon at another individual.
- attack: The victim attacked with other weapons or physical force.
- threat: The victim had some kind of weapon visible to the officers on the scene.
- move: The victim was moving in a threatening way.
- flee: The victim was fleeing (see flee_status)
- accident: :question:
- undetermined: The threat type could not be determined from available evidence
point
armed_withWhat, if anything, was the victim armed with per federal classifications based on NIBRS, the national incident-level crime reporting system. These categories are roughly reflected in local police data and forms. A NIBRS manual is can be found here (see values for data element Type Weapon/Force involved, on PDF page 104/report page 94).string

options:
- gun: A firearm, handgun, shotgun, or other firearm
- knife : A knife or other cutting instrument (razors, hatches, axes, cleavers, scissors, broken bottles, ice picks, etc.)
- blunt_object: A blunt object (baseball bats, the butt of a handgun, clubs, bricks, tire irons, bottles, etc.)
- other: Any other weapon (BB guns, pellet guns, Tasers, pepper spray, stun guns, etc.)
- replica: A toy weapon, replica, or other non-functional firearm.
- undetermined: Whether the victim had a weapon could not be determined from available evidence.
- unknown: There was a weapon involved, but we do not know what kind.
- unarmed: The victim had no weapon according to available evidence.
- vehicle: A motor vehicle or vessel.
"gun;knife"
flee_status*How, if at all, was the victim moving relative to officers leading up to the shooting.string

options:
- foot : On foot
- car : Via car
- other: Via another vehicle
- not: Not fleeing
foot
agency_idsList of agency ids associated with the death record.string"1;2"
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Note: threat_type and flee_status are not necessarily related. For example, there may be an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer.

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Agencies

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File: /v2/fatal-police-shootings-agencies.csv

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Summary: Data for departments and agencies involved in a fatal police shooting in the database. This separate CSV can be joined with the death record CSV via the agency_ids value.

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Fields:

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DescriptionTypeExample value
idDepartment database idinteger1
nameDepartment namestringDavis County Sheriff's Office
typeDepartment typestring

options:
-local_police: County or other local police
-local_other: Other local department
-sheriff: Sheriffoffice
-other: Other non-local department
-state_police: State police
-state_other: Other statewide law enforcement
-federal: Federal law enforcement
state_other
stateDepartment stateState in which the agency is located.UT
oricodesDepartment ORI codes (federal identifiers).

Note that there can be multiple ORI codes for a single department due to aggregated sub-departments and/or differing values provided by the FBI vs DOJ.
string"ABC123;XYZ987"
total_shootingsTotal death records the agency has been involved with.integer2
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Police Residence

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This folder contains data behind the story Most Police Don’t Live In The Cities They Serve.

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Includes the cities with the 75 largest police forces, with the exception of Honolulu for which data is not available. All calculations are based on data from the U.S. Census.

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The Census Bureau numbers are potentially going to differ from other counts for three reasons:

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  1. The census category for police officers also includes sheriffs, transit police and others who might not be under the same jurisdiction as a city’s police department proper. The census category won’t include private security officers.
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  3. The census data is estimated from 2006 to 2010; police forces may have changed in size since then.
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  5. There is always a margin of error in census numbers; they are estimates, not complete counts.
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How to read police-locals.csv

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HeaderDefinition
cityU.S. city
police_force_sizeNumber of police officers serving that city
allPercentage of the total police force that lives in the city
whitePercentage of white (non-Hispanic) police officers who live in the city
non-whitePercentage of non-white police officers who live in the city
blackPercentage of black police officers who live in the city
hispanicPercentage of Hispanic police officers who live in the city
asianPercentage of Asian police officers who live in the city
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Note: When a cell contains ** it means that there are fewer than 100 police officers of that race serving that city.

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A Case Study on the Relationship between Police Residence and Fatal Police Shootings

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- Gabriel Brock | Fa23 | Harvard University -
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On average, police in the United States shoot and kill more than 1,000 people every year…and then they go home to their families

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Abstract

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This case study investigates the intricate relationship between police residence and fatal police shootings, employing a data science approach to uncover insights and patterns within the context of law enforcement agencies. Focused on police officers residing in the cities they serve, the study examines whether this residency factor correlates with the incidence of fatal police shootings. The data set, spanning the years 2015 to 2023, is composed of information on police agencies involved in at least one fatal shooting, and is subjected to rigorous analysis using advanced statistical methods and machine learning techniques.

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This study aims to discern patterns, trends, and potential biases associated with the geographical proximity of police officers to the communities they police. A comprehensive exploration of demographic, socioeconomic, and policing variables contributes to a nuanced understanding of the factors influencing fatal police shootings. Furthermore, the study seeks to identify any disparities in incident rates based on officers’ residency status, considering variables such as race, community demographics, and departmental policies.

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The insights derived from this case study bear substantial implications for informing public policy, refining police training protocols, and strengthening community relations. By unraveling the nuanced dynamics surrounding police residence and fatal police shootings, this case study aims to provide evidence-based recommendations to enhance transparency, accountability, and trust between law enforcement agencies and the communities they serve. In doing so, it contributes to the broader discourse on police reform, fostering a data-driven approach to address critical issues and promote safer, more resilient communities.

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Hypotheses

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We will conduct two hypothesis tests to analyze both;

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  1. The nominal relationship between an increasing proportion of in-city officer residency and number of fatal police shooting deaths

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    • \(H_0\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.

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    • \(H_A\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.

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      • \(H_0 : p\_{maj} − p\_{min} = 0\), or equivalently \(H_0 : p\_{maj} = p\_{min}\)
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      • \(H_A : p\_{maj} − p\_{min} < 0\), or equivalently \(H_A : p\_{maj} < p\_{min}\)
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  2. -
  3. The categorical difference in fatal police shooting deaths between cities where a majority or or minority of police officers live in the city.

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    • \(H_0\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.

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    • \(H_A\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.

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      • \(H_0 : \rho = 0\)

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      • \(H_0 : \rho \neq 0\)

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    • -
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Methods

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Tidying Data

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-Show the code -
##Tidying Data
-
-#creating dfs from .csv files
-police_locals <- read_csv("data/police-locals.csv")
-agencies <- read_csv("data/fatal-police-shootings-agencies.csv")
-shootings <- read_csv("data/fatal-police-shootings-data.csv")
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-#removing old `city` tag from data set that we created when decatenated the city names
-police_locals <- police_locals |>
-  select(-city_old)
-
-# creating `agencies` df with just police departments
-agencies <- agencies |>
-  filter(grepl("department", tolower(name))) |>
-  filter(!grepl("county", tolower(name)))
-
-#creating binned categorical account of if shooting victim was `armed`
-shootings <- shootings |>
-  mutate(armed = case_when(is.na(armed_with) ~ "NO",
-                           armed_with == "unarmed" ~ "NO",
-                           armed_with == "unknown" ~ "NO",
-                           armed_with == "undetermined" ~ "NO",
-                           armed_with == "gun" ~ "YES",
-                           armed_with == "knife" ~ "YES",
-                           armed_with == "blunt_object" ~ "YES",
-                           armed_with == "other" ~ "YES",
-                           armed_with == "replica" ~ "YES",
-                           armed_with == "vehicle" ~ "YES"))
-
-#creating df with only agency `names`, `id`, and `state`
-agencies_ids <- agencies |>
-  select(name, id, state)
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-#creating df with `city`, `agency`, and `state` info for each shooting
-shooting_agencies <- shootings |>
-  select(city, agency_ids, state)
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-#changing `shooting` var in `shooting_agencies` df to numeric
-shooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)
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-#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`
-agencies_w_cities <- agencies_ids |>
-  left_join(shooting_agencies, by = c("id" = "agency_ids", "state" = "state")) |>
-  drop_na(city) |>
-  distinct(id, .keep_all = TRUE)
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-#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`
-agencies_census <- agencies_w_cities |>
-  full_join(police_locals, by = c("city" = "city", "state" = "state")) |>
-  drop_na(police_force_size) |>
-  distinct(id, .keep_all = TRUE) |>
-  mutate(majority = if_else(all >= 0.5, "TRUE", "FALSE"))
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-#creating df of only shootings involving agencies within `agencies` df
-shootings_case <- shootings |>
-  right_join(agencies_census, by = c("city" = "city", "state" = "state")) |>
-  select(-agency_ids) |>
-  rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>
-  select(-location_precision, -race_source)
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Counting Shootings

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-Show the code -
#count shootings by agency
-shootings_by_agency <- shootings_case |>
-  count(agency)
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-#find top 25 agencies with the most shootings
-top_25_agencies <- shootings_by_agency |>
-  slice_max(n, n = 25)
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Mapping Locations of Police-Involved Shootings between 2015 and 2023

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-Show the code -
shot_map
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-Show the code -
#creating df with total shootings per agency and census data
-agencies_census <- agencies_census |>
-  left_join(shootings_by_agency, by = c("name" = "agency"))
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-#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside
-p0 <- shootings_case |>
-  ggplot(aes(x = majority, fill = armed)) +
-  geom_bar() + 
-  labs(title = "Shootings in Cities where a Majority of Officers Reside",
-       caption = "This is only includes shootings where we have agency census data.",
-       x = "Does a majority a of the total police force live in the city?",
-       y = "Number of fatal shootings",
-       fill = "Victim Armed?")
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-Show the code -
p0
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-Show the code -
#calculate mean number of shootings per agency in cities where a majority of officers reside in the city
-majority_mean <- shootings_case |>
-  filter(majority == TRUE) |>
-  count(agency) |>
-  summarize(maj_mean = mean(n))
-
-#calculate mean number of shootings per agency in cities where a minority of officers reside in the city
-minority_mean <- shootings_case |>
-  filter(majority == FALSE) |>
-  count(agency) |>
-  summarize(min_mean = mean(n))
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-#calculate a difference in means between the `majority` and `minority`
-diff_in_means <- majority_mean - minority_mean
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-Show the code -
#tidy table
-knitr::kable(head(diff_in_means))
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maj_mean
-2.575
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-Show the code -
#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency
-fit <- lm(n ~ all, data = agencies_census)
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-#add `armed` and `majority` to `shootings_by_agency` df
-shootings_by_agency_census <- shootings_case |>
-  group_by(agency) |>
-  count(armed) |>
-  drop_na(n, armed) |>
-  right_join(agencies_census, by = c("agency" = "name")) |>
-  distinct(armed, .keep_all = TRUE)
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-shootings_by_agency_census <- shootings_by_agency_census |>
-  select(n.x, armed, all) 
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-#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency
-fit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)
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Results

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Multiple Linear Regression of relationship between percentage of officer residency and number of fatal shootings per agency fit

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The model equation for fit is:

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\[ -\text{Number of Fatal Shootings (n)} = 35.7782 - 0.5874 \times \text{Percentage of Officer Residency (all)} -\]

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-Show the code -
#tidy `fit`
-p1 <- get_regression_table(fit)
-knitr::kable(head(p1))
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termestimatestd_errorstatisticp_valuelower_ciupper_ci
intercept35.7786.8875.1950.00022.12649.430
all-0.58714.902-0.0390.969-30.13028.955
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Interpretation:

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  • The intercept, \(35.7782\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \(0\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \(0.5874\) (\(-0.5874\)) units, assuming all other factors remain constant.
  • -
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This model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.

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#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency
-ggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +
-  geom_jitter(width = 0.10, height = 0, alpha = 0.45) +
-  geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
-  labs(title = "Number of Shootings on a Scale of Police Force Residency",
-       x = "Percentage of the total police force that lives in the city",
-       y = "Number of fatal shootings in that city")
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Multiple Linear Regression of relationship between percentage of officer residency/victim armament and number of fatal shootings per agency fit_multi

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The model equation for fit_multi considering victim armament (armed) is:

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\[ -\text{Number of Fatal Shootings (n.x)} = 4.117 + 1.211 \times \text{Percentage of Officer Residency (all)} + 24.921 \times \text{Armed (YES)} -\]

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-Show the code -
#tidy `fit_multi`
-p2 <- get_regression_table(fit_multi)
-knitr::kable(head(p2))
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- --------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
termestimatestd_errorstatisticp_valuelower_ciupper_ci
intercept4.1173.3621.2250.222-2.51210.747
all1.2116.3350.1910.849-11.28113.702
armed: YES24.9212.8918.6190.00019.21930.622
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  • The intercept, \(4.117\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \(0\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \(1.211\) fatal shootings, assuming the victim’s armament status (armed:YES) remains constant.

  • -
  • The coefficient for ‘armedYES’, \(24.921\), indicates that the victim is armed (armed:YES), we expect an increase of \(24.921\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.

  • -
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In summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.

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#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency
-ggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +
-  geom_jitter(width = 0.10, height = 0, alpha = 0.45) +
-  geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = TRUE) +
-  labs(title = "Number of Shootings on a Scale of Police Force Residency",
-       x = "Percentage of the total police force that lives in the city",
-       y = "Number of fatal shootings in that city",
-       color = "Victim Armed?")
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#generate null distribution
-null_dist <- agencies_census |>
-  specify(n ~ majority) |>
-  hypothesize(null = "independence") |>
-  generate(reps = 1000, type = "permute") |>
-  calculate(stat = "diff in means", order = c("TRUE", "FALSE"))
-
-#compute observed test statistic
-test_stat <- agencies_census |>
-  specify(n ~ majority) |>
-  calculate(stat = "diff in means", order = c("TRUE", "FALSE"))
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-#visualize p-value
-null_dist |>
-  visualize() +
-  shade_p_value(obs_stat = test_stat, direction = "less")
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#compute p-value
-  null_dist |>
-  get_p_value(obs_stat = test_stat, direction = "less")
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# A tibble: 1 × 1
-  p_value
-    <dbl>
-1   0.272
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At a significance level of \(\alpha = 0.05\), the p-value of \(0.248\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \(\mu_{maj} − \mu_{min}\)) is \(-4.92\) is around \(25\%\) (\(0.248\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.

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-Show the code -
#generate null distribution
-null_dist_cor <- agencies_census |>
-  specify(n ~ white) |>
-  hypothesize(null = "independence") |>
-  generate(reps = 1000, type = "permute") |>
-  calculate(stat = "correlation")
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-#compute observed test statistic
-test_stat_cor <- agencies_census |>
-  specify(n ~ white) |>
-  calculate(stat = "correlation")
-
-
-#visualize p-value
-null_dist_cor |>
-  visualize() +
-  shade_p_value(obs_stat = test_stat, direction = "two.sided")
-
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-

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-Show the code -
#compute p-value
-null_dist_cor |>
-  get_p_value(obs_stat = test_stat, direction = "two.sided")
-
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-
# A tibble: 1 × 1
-  p_value
-    <dbl>
-1       0
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At a significance level of \(\alpha = 0.05\), the p-value of \(0.248\) suggests that, there is sufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that there is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (correlation coefficient; \(\rho = 0\)) is \(-0.0470\) is around \(0\%\) (\(0\)). Meaning our observed correlation coefficient likely would not happen if there was no relationship between percentage of officer residency and number of fatal shootings for a given agency.

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Conclusion

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General Conclusions

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This comprehensive case study delves into the complex interplay between police force in-city residence and fatal police shootings, utilizing a data-driven approach to unearth patterns and insights within the realm of law enforcement agencies. Focused on the residency status of police officers in the cities they serve, the study scrutinizes whether this factor is correlated with the occurrence of fatal police shootings. Covering the years 2015 to 2023 and incorporating information on police agencies involved in at least one fatal shooting, the research employs advanced statistical methods and machine learning techniques to provide a nuanced understanding of the phenomenon.

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Key Findings

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The analysis reveals a negative association between the percentage of officer residency in a city and the number of fatal shootings, suggesting that, on average, a higher proportion of in-city officer residency is associated with fewer fatal police shootings. However, correlation is not equal to causation so we conducted a multiple linear regression. When controlling for victim armament, the study identifies a complex relationship. While an increase in the percentage of officer residency is associated with fewer fatal shootings, the presence of an armed victim is linked to a significant increase in fatal shootings.

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Our hypothesis tests provide insights into the statistical significance of the observed relationships; a difference in mean number of shootings per agency in cities where a majority or minority of officers reside in the city and the nominal relationship (correlation) between an increasing proportion of in-city officer residency and number of fatal police shooting death. The first test suggests that there is insufficient evidence to reject the null hypothesis regarding the difference in mean total fatal shootings between agencies with a majority and minority of officers residing in the city. The second test indicates a significant relationship between the percentage of officer residency and the number of fatal shootings.

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Limitations and Considerations

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It’s essential to acknowledge the limitations of this study. The analysis relies on available data, and causation cannot be inferred from correlation. Factors beyond the scope of this study may influence fatal police shootings, emphasizing the need for a holistic approach to police reform.

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Policy Recommendations

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The findings of this case study carry substantial implications for policy-making, police training, and community relations. The evidence suggests that fostering a higher percentage of officer residency in the communities they police may contribute to a reduction in fatal police shootings. However, it’s crucial to consider other influential factors, such as victim armament, when formulating comprehensive policies.

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Implement measures to enhance transparency and accountability within law enforcement agencies, including the regular monitoring of fatal shooting incidents and the factors influencing them. Encourage community policing strategies that promote stronger bonds between officers and the communities they serve. Initiatives to increase the percentage of officers residing in their assigned cities may contribute to building trust. Prioritize training programs that focus on de-escalation techniques and handling situations involving armed individuals. This can mitigate the escalation of incidents and reduce the likelihood of fatal outcomes. Policy makers and researchers should also promote ongoing research and data collection to monitor trends and assess the effectiveness of implemented strategies. Continued analysis will help refine policies and ensure adaptability to evolving community needs.

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Improvements for Future Study

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In conclusion, this case study contributes to the ongoing discourse on police reform by providing evidence-based insights into the relationship between police residence and fatal police shootings. By addressing these critical issues, we aspire to foster safer, more resilient communities and facilitate positive transformations in law enforcement practices.

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- - - - \ No newline at end of file diff --git a/_site/index_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png b/_site/index_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png deleted file mode 100644 index fee8a7c..0000000 Binary files a/_site/index_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png and /dev/null differ diff --git a/_site/index_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png b/_site/index_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png deleted file mode 100644 index 3f9f491..0000000 Binary files a/_site/index_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png and /dev/null differ diff --git a/_site/index_files/figure-html/unnamed-chunk-1-1.png b/_site/index_files/figure-html/unnamed-chunk-1-1.png deleted file mode 100644 index 9a2522a..0000000 Binary files a/_site/index_files/figure-html/unnamed-chunk-1-1.png and /dev/null differ diff --git a/_site/index_files/figure-html/unnamed-chunk-2-1.png b/_site/index_files/figure-html/unnamed-chunk-2-1.png deleted file mode 100644 index a1e7dba..0000000 Binary files a/_site/index_files/figure-html/unnamed-chunk-2-1.png and /dev/null differ diff --git a/_site/index_files/figure-html/unnamed-chunk-7-1.png b/_site/index_files/figure-html/unnamed-chunk-7-1.png deleted file mode 100644 index 9ec93a7..0000000 Binary files a/_site/index_files/figure-html/unnamed-chunk-7-1.png and /dev/null differ diff --git a/_site/index_files/figure-html/unnamed-chunk-9-1.png b/_site/index_files/figure-html/unnamed-chunk-9-1.png deleted file mode 100644 index 21d5146..0000000 Binary files a/_site/index_files/figure-html/unnamed-chunk-9-1.png and /dev/null differ diff --git a/_site/search.json b/_site/search.json deleted file mode 100644 index df3c628..0000000 --- a/_site/search.json +++ /dev/null @@ -1,100 +0,0 @@ -[ - { - "objectID": "background.html", - "href": "background.html", - "title": "Background", - "section": "", - "text": "On average, police in the United States shoot and kill more than 1,000 people every year, according to an ongoing analysis by The Washington Post.\n\n\nProposal\nWe propose a case study to explore the relationship between police residence and fatal police shootings, employing advanced data science methodologies. Focusing on officers residing in the cities they serve, our project aims to uncover insights and patterns that contribute to a nuanced understanding of this complex issue.\n\nObjectives:\n\nInvestigate the correlation between police residence and fatal police shootings.\nUtilize a comprehensive dataset spanning 2015 to 2023, focusing on police agencies involved in at least one fatal shooting.\nApply advanced statistical methods and machine learning techniques to identify patterns and potential biases.\nExamine disparities in incident rates based on officers’ residency status, considering demographic, socioeconomic, and policing variables.\n\nMethodology:\n\nData Collection:\n\nCompile a dataset comprising information on police agencies involved in fatal police shootings.\nCompile a data set of census variables such as officer residency, race, community demographics, and departmental policies.\n\nAnalysis:\n\nEmploy advanced statistical methods and machine learning techniques to discern patterns and correlations.\nConduct a comprehensive exploration of variables influencing fatal police shootings.\n\n\nHypothesis and Expected Outcomes:\nWe will conduct two hypothesis tests to analyze both;\n\nthe nominal relationship between an increasing proportion of in-city officer residency and number of fatal police shooting deaths and\nthe categorical difference in fatal police shooting deaths between cities where a majority or or minority of police officers live in the city.\n\n\nInference for a Difference in Proportions\n\n\\(H_0\\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.\n\\(H_A\\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.\n\n\\(H_0 : p\\_{maj} − p\\_{min} = 0\\), or equivalently \\(H_0 : p\\_{maj} = p\\_{min}\\)\n\\(H_A : p\\_{maj} − p\\_{min} < 0\\), or equivalently \\(H_A : p\\_{maj} < p\\_{min}\\)\n\n\nInference for a Correlation\n\n\\(H_O\\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\\(H_A\\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\n\\(H_0 : \\rho = 0\\)\n\\(H_0 : \\rho \\neq 0\\)\n\n\n\n\n\n\nThe Washington Post Fatal Force Database\nIn 2015, The Washington Post began tracking details about each police-involved killing in the United States — the race of the deceased, the circumstances of the shooting, whether the person was armed and whether the person was experiencing a mental-health crisis — by manually culling local news reports, collecting information from law enforcement websites and social media, and monitoring independent databases such as Fatal Encounters and the now-defunct Killed by Police project. In many cases, The Post conducts additional reporting.\nIn 2022, The Post updated its database to standardize and publish the names of the police agencies involved in each shooting to better measure accountability at the department level.\nThe 2014 killing of Michael Brown in Ferguson, Mo. began a protest movement culminating in the Black Lives Matter movement and an increased focus on police accountability nationwide. In this data set, The Post tracks only shootings with circumstances closely paralleling those like the killing of Brown — incidents in which a police officer, in the line of duty, shoots and kills a civilian. The Post is not tracking deaths of people in police custody, fatal shootings by off-duty officers or non-shooting deaths in this data set.\nThe FBI and the Centers for Disease Control and Prevention log fatal shootings by police, but officials acknowledge that their data is incomplete. Since 2015, The Post has documented more than twice as many fatal shootings by police as recorded by federal officials on average annually. That gap has widened in recent years, as the FBI in 2021 tracked only a third of departments’ fatal shootings.\n\n\nMost Police Don’t Live In The Cities They Serve\nIn Ferguson, Missouri, where protests lamented for months following the shooting of a teenager by a police officer this month, more than two-thirds of the civilian population is black. Only 11 percent of the police force is. The racial disparity is troubling enough on its own, but it’s also suggestive of another type of misrepresentation. Given Ferguson’s racial gap, it’s likely that many of its police officers live outside city limits.\nIf so, Ferguson would have something in common with most major American cities. In about two-thirds of the U.S. cities with the largest police forces, the majority of police officers commute to work from another town.\nOn average among the 75 American cities with the largest police forces, 49 percent of black police officers and 47 percent of Hispanic officers live within the city limits. But just 35 percent of white police officers do. The disparity is starkest in cities with largely black populations. In Detroit, for example, 57 percent of black police officers live in the city but just 8 percent of white ones do. Memphis, Tennessee; Baltimore; Birmingham, Alabama; and Jackson, Mississippi — also majority black — likewise have large racial gaps in where their police officers live." - }, - { - "objectID": "codebook.html", - "href": "codebook.html", - "title": "Codebook", - "section": "", - "text": "Name\nDescription\n\n\n\n\ncity\nU.S. city\n\n\npolice_force_size\nNumber of police officers serving that city\n\n\nall\nPercentage of the total police force that lives in the city\n\n\nwhite\nPercentage of white (non-Hispanic) police officers who live in the city\n\n\nnon-white\nPercentage of non-white police officers who live in the city\n\n\nblack\nPercentage of black police officers who live in the city\n\n\nhispanic\nPercentage of Hispanic police officers who live in the city\n\n\nasian\nPercentage of Asian police officers who live in the city\n\n\n\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nid\nA unique identifier for each fatal police shooting incident.\n\n\ndate\nThe date of the fatal shooting.\n\n\nbody_camera\nWhether news reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.\n\n\ncity\nThe municipality where the fatal shooting took place\n\n\ncounty\nCounty where the fatal shooting took place.\n\n\nstate\nThe two-letter postal code abbreviation for the state in which the fatal shooting took place.\n\n\nlatitude\nThe latitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses. Please note that the precision and accuracy of incident coordinates varies depending on the precision of the input address which is often only available at the block level.\n\n\nlongitude\nThe longitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses.\n\n\n\n\n\n\n\n\n\n\nDescription\n\n\n\n\nid\nDepartment Database Id\n\n\nname\nDepartment Name\n\n\nstate\nState in which the agency is located." - }, - { - "objectID": "codebook.html#explanatory-variables", - "href": "codebook.html#explanatory-variables", - "title": "Codebook", - "section": "", - "text": "Name\nDescription\n\n\n\n\ncity\nU.S. city\n\n\npolice_force_size\nNumber of police officers serving that city\n\n\nall\nPercentage of the total police force that lives in the city\n\n\nwhite\nPercentage of white (non-Hispanic) police officers who live in the city\n\n\nnon-white\nPercentage of non-white police officers who live in the city\n\n\nblack\nPercentage of black police officers who live in the city\n\n\nhispanic\nPercentage of Hispanic police officers who live in the city\n\n\nasian\nPercentage of Asian police officers who live in the city\n\n\n\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nid\nA unique identifier for each fatal police shooting incident.\n\n\ndate\nThe date of the fatal shooting.\n\n\nbody_camera\nWhether news reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.\n\n\ncity\nThe municipality where the fatal shooting took place\n\n\ncounty\nCounty where the fatal shooting took place.\n\n\nstate\nThe two-letter postal code abbreviation for the state in which the fatal shooting took place.\n\n\nlatitude\nThe latitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses. Please note that the precision and accuracy of incident coordinates varies depending on the precision of the input address which is often only available at the block level.\n\n\nlongitude\nThe longitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses.\n\n\n\n\n\n\n\n\n\n\nDescription\n\n\n\n\nid\nDepartment Database Id\n\n\nname\nDepartment Name\n\n\nstate\nState in which the agency is located." - }, - { - "objectID": "codebook.html#project-thoughts", - "href": "codebook.html#project-thoughts", - "title": "Codebook", - "section": "Project thoughts", - "text": "Project thoughts\nI am interested in exploring data related to…\n\nPolitical Extremism\nBlack American Opinion" - }, - { - "objectID": "index.html#sec-abstract", - "href": "index.html#sec-abstract", - "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", - "section": "Abstract", - "text": "Abstract\nThis case study investigates the intricate relationship between police residence and fatal police shootings, employing a data science approach to uncover insights and patterns within the context of law enforcement agencies. Focused on police officers residing in the cities they serve, the study examines whether this residency factor correlates with the incidence of fatal police shootings. The data set, spanning the years 2015 to 2023, is composed of information on police agencies involved in at least one fatal shooting, and is subjected to rigorous analysis using advanced statistical methods and machine learning techniques.\nThis study aims to discern patterns, trends, and potential biases associated with the geographical proximity of police officers to the communities they police. A comprehensive exploration of demographic, socioeconomic, and policing variables contributes to a nuanced understanding of the factors influencing fatal police shootings. Furthermore, the study seeks to identify any disparities in incident rates based on officers’ residency status, considering variables such as race, community demographics, and departmental policies.\nThe insights derived from this case study bear substantial implications for informing public policy, refining police training protocols, and strengthening community relations. By unraveling the nuanced dynamics surrounding police residence and fatal police shootings, this case study aims to provide evidence-based recommendations to enhance transparency, accountability, and trust between law enforcement agencies and the communities they serve. In doing so, it contributes to the broader discourse on police reform, fostering a data-driven approach to address critical issues and promote safer, more resilient communities." - }, - { - "objectID": "index.html#hypotheses", - "href": "index.html#hypotheses", - "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", - "section": "Hypotheses", - "text": "Hypotheses\nWe will conduct two hypothesis tests to analyze both;\n\nThe nominal relationship between an increasing proportion of in-city officer residency and number of fatal police shooting deaths\n\n\\(H_0\\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.\n\\(H_A\\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.\n\n\\(H_0 : p\\_{maj} − p\\_{min} = 0\\), or equivalently \\(H_0 : p\\_{maj} = p\\_{min}\\)\n\\(H_A : p\\_{maj} − p\\_{min} < 0\\), or equivalently \\(H_A : p\\_{maj} < p\\_{min}\\)\n\n\nThe categorical difference in fatal police shooting deaths between cities where a majority or or minority of police officers live in the city.\n\n\\(H_0\\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\\(H_A\\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\n\\(H_0 : \\rho = 0\\)\n\\(H_0 : \\rho \\neq 0\\)" - }, - { - "objectID": "index.html#methods", - "href": "index.html#methods", - "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", - "section": "Methods", - "text": "Methods\n\nTidying Data\n\n\nShow the code\n##Tidying Data\n\n#creating dfs from .csv files\npolice_locals <- read_csv(\"data/police-locals.csv\")\nagencies <- read_csv(\"data/fatal-police-shootings-agencies.csv\")\nshootings <- read_csv(\"data/fatal-police-shootings-data.csv\")\n\n#removing old `city` tag from data set that we created when decatenated the city names\npolice_locals <- police_locals |>\n select(-city_old)\n\n# creating `agencies` df with just police departments\nagencies <- agencies |>\n filter(grepl(\"department\", tolower(name))) |>\n filter(!grepl(\"county\", tolower(name)))\n\n#creating binned categorical account of if shooting victim was `armed`\nshootings <- shootings |>\n mutate(armed = case_when(is.na(armed_with) ~ \"NO\",\n armed_with == \"unarmed\" ~ \"NO\",\n armed_with == \"unknown\" ~ \"NO\",\n armed_with == \"undetermined\" ~ \"NO\",\n armed_with == \"gun\" ~ \"YES\",\n armed_with == \"knife\" ~ \"YES\",\n armed_with == \"blunt_object\" ~ \"YES\",\n armed_with == \"other\" ~ \"YES\",\n armed_with == \"replica\" ~ \"YES\",\n armed_with == \"vehicle\" ~ \"YES\"))\n\n#creating df with only agency `names`, `id`, and `state`\nagencies_ids <- agencies |>\n select(name, id, state)\n\n#creating df with `city`, `agency`, and `state` info for each shooting\nshooting_agencies <- shootings |>\n select(city, agency_ids, state)\n\n#changing `shooting` var in `shooting_agencies` df to numeric\nshooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)\n\n#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`\nagencies_w_cities <- agencies_ids |>\n left_join(shooting_agencies, by = c(\"id\" = \"agency_ids\", \"state\" = \"state\")) |>\n drop_na(city) |>\n distinct(id, .keep_all = TRUE)\n\n#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`\nagencies_census <- agencies_w_cities |>\n full_join(police_locals, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n drop_na(police_force_size) |>\n distinct(id, .keep_all = TRUE) |>\n mutate(majority = if_else(all >= 0.5, \"TRUE\", \"FALSE\"))\n\n#creating df of only shootings involving agencies within `agencies` df\nshootings_case <- shootings |>\n right_join(agencies_census, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n select(-agency_ids) |>\n rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>\n select(-location_precision, -race_source)\n\n\n\n\nCounting Shootings\n\n\nShow the code\n#count shootings by agency\nshootings_by_agency <- shootings_case |>\n count(agency)\n\n#find top 25 agencies with the most shootings\ntop_25_agencies <- shootings_by_agency |>\n slice_max(n, n = 25)\n\n\n\n\nMapping Locations of Police-Involved Shootings between 2015 and 2023\n\n\nShow the code\nshot_map\n\n\n\n\n\n\n\nShow the code\n#creating df with total shootings per agency and census data\nagencies_census <- agencies_census |>\n left_join(shootings_by_agency, by = c(\"name\" = \"agency\"))\n\n#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside\np0 <- shootings_case |>\n ggplot(aes(x = majority, fill = armed)) +\n geom_bar() + \n labs(title = \"Shootings in Cities where a Majority of Officers Reside\",\n caption = \"This is only includes shootings where we have agency census data.\",\n x = \"Does a majority a of the total police force live in the city?\",\n y = \"Number of fatal shootings\",\n fill = \"Victim Armed?\")\n\n\n\n\nShow the code\np0\n\n\n\n\n\n\n\nShow the code\n#calculate mean number of shootings per agency in cities where a majority of officers reside in the city\nmajority_mean <- shootings_case |>\n filter(majority == TRUE) |>\n count(agency) |>\n summarize(maj_mean = mean(n))\n\n#calculate mean number of shootings per agency in cities where a minority of officers reside in the city\nminority_mean <- shootings_case |>\n filter(majority == FALSE) |>\n count(agency) |>\n summarize(min_mean = mean(n))\n\n#calculate a difference in means between the `majority` and `minority`\ndiff_in_means <- majority_mean - minority_mean\n\n\n\n\nShow the code\n#tidy table\nknitr::kable(head(diff_in_means))\n\n\n\n\n\nmaj_mean\n\n\n\n\n-2.575\n\n\n\n\n\n\n\nShow the code\n#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency\nfit <- lm(n ~ all, data = agencies_census)\n\n#add `armed` and `majority` to `shootings_by_agency` df\nshootings_by_agency_census <- shootings_case |>\n group_by(agency) |>\n count(armed) |>\n drop_na(n, armed) |>\n right_join(agencies_census, by = c(\"agency\" = \"name\")) |>\n distinct(armed, .keep_all = TRUE)\n\nshootings_by_agency_census <- shootings_by_agency_census |>\n select(n.x, armed, all) \n\n#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency\nfit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)" - }, - { - "objectID": "index.html#results", - "href": "index.html#results", - "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", - "section": "Results", - "text": "Results\n\nMultiple Linear Regression of relationship between percentage of officer residency and number of fatal shootings per agency fit\nThe model equation for fit is:\n\\[\n\\text{Number of Fatal Shootings (n)} = 35.7782 - 0.5874 \\times \\text{Percentage of Officer Residency (all)}\n\\]\n\n\nShow the code\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n\n\n\n\nMultiple Linear Regression of relationship between percentage of officer residency/victim armament and number of fatal shootings per agency fit_multi\nThe model equation for fit_multi considering victim armament (armed) is:\n\\[\n\\text{Number of Fatal Shootings (n.x)} = 4.117 + 1.211 \\times \\text{Percentage of Officer Residency (all)} + 24.921 \\times \\text{Armed (YES)}\n\\]\n\n\nShow the code\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armed:YES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed:YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\n\n\n\nShow the code\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n\n\nShow the code\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.272\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \\(\\mu_{maj} − \\mu_{min}\\)) is \\(-4.92\\) is around \\(25\\%\\) (\\(0.248\\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.\n\n\nShow the code\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\n\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n\n\nShow the code\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is sufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that there is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (correlation coefficient; \\(\\rho = 0\\)) is \\(-0.0470\\) is around \\(0\\%\\) (\\(0\\)). Meaning our observed correlation coefficient likely would not happen if there was no relationship between percentage of officer residency and number of fatal shootings for a given agency." - }, - { - "objectID": "index.html#conclusion", - "href": "index.html#conclusion", - "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", - "section": "Conclusion", - "text": "Conclusion\n\nGeneral Conclusions\nThis comprehensive case study delves into the complex interplay between police force in-city residence and fatal police shootings, utilizing a data-driven approach to unearth patterns and insights within the realm of law enforcement agencies. Focused on the residency status of police officers in the cities they serve, the study scrutinizes whether this factor is correlated with the occurrence of fatal police shootings. Covering the years 2015 to 2023 and incorporating information on police agencies involved in at least one fatal shooting, the research employs advanced statistical methods and machine learning techniques to provide a nuanced understanding of the phenomenon.\n\n\nKey Findings\nThe analysis reveals a negative association between the percentage of officer residency in a city and the number of fatal shootings, suggesting that, on average, a higher proportion of in-city officer residency is associated with fewer fatal police shootings. However, correlation is not equal to causation so we conducted a multiple linear regression. When controlling for victim armament, the study identifies a complex relationship. While an increase in the percentage of officer residency is associated with fewer fatal shootings, the presence of an armed victim is linked to a significant increase in fatal shootings.\nOur hypothesis tests provide insights into the statistical significance of the observed relationships; a difference in mean number of shootings per agency in cities where a majority or minority of officers reside in the city and the nominal relationship (correlation) between an increasing proportion of in-city officer residency and number of fatal police shooting death. The first test suggests that there is insufficient evidence to reject the null hypothesis regarding the difference in mean total fatal shootings between agencies with a majority and minority of officers residing in the city. The second test indicates a significant relationship between the percentage of officer residency and the number of fatal shootings.\n\n\nLimitations and Considerations\nIt’s essential to acknowledge the limitations of this study. The analysis relies on available data, and causation cannot be inferred from correlation. Factors beyond the scope of this study may influence fatal police shootings, emphasizing the need for a holistic approach to police reform.\n\n\nPolicy Recommendations\nThe findings of this case study carry substantial implications for policy-making, police training, and community relations. The evidence suggests that fostering a higher percentage of officer residency in the communities they police may contribute to a reduction in fatal police shootings. However, it’s crucial to consider other influential factors, such as victim armament, when formulating comprehensive policies.\nImplement measures to enhance transparency and accountability within law enforcement agencies, including the regular monitoring of fatal shooting incidents and the factors influencing them. Encourage community policing strategies that promote stronger bonds between officers and the communities they serve. Initiatives to increase the percentage of officers residing in their assigned cities may contribute to building trust. Prioritize training programs that focus on de-escalation techniques and handling situations involving armed individuals. This can mitigate the escalation of incidents and reduce the likelihood of fatal outcomes. Policy makers and researchers should also promote ongoing research and data collection to monitor trends and assess the effectiveness of implemented strategies. Continued analysis will help refine policies and ensure adaptability to evolving community needs.\n\n\nImprovements for Future Study\nIn conclusion, this case study contributes to the ongoing discourse on police reform by providing evidence-based insights into the relationship between police residence and fatal police shootings. By addressing these critical issues, we aspire to foster safer, more resilient communities and facilitate positive transformations in law enforcement practices." - }, - { - "objectID": "index.html#citations", - "href": "index.html#citations", - "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", - "section": "Citations", - "text": "Citations" - }, - { - "objectID": "data/README-fatal-police-shoot.html", - "href": "data/README-fatal-police-shoot.html", - "title": "Fatal Force Database (v2)", - "section": "", - "text": "Each day researchers at The Post identify and manually record data for fatal police shootings in the United States. Each record requires at least two sources and must be approved by our editors before being publicly released.\nData about each fatal shooting is provided via two comma-separated value (CSV) files:\nDeath Record data for each victim and incident is included in /v2/fatal-police-shootings-data.csv.\nNew in v2 is a second Agencies csv, /v2/fatal-police-shootings-agencies.csv, which contains data for police agencies involved in at least one fatal police shooting since 2015. The agencies csv has a field called agency_ids, which can be used to associate each death record with the agencies involved.\nTo enable joining this dataset with other federal law enforcement datasets, in 2022 The Post undertook an effort to increase the coverage of federal Originating Agency Identifier (ORI) codes recorded for agencies in the database. Using agency data from the FBI and Department of Justice, reporters did a combination of automated and manual name and ORI code matching to fill in missing ORI code data. They also standardized department names. Agency naming, organization, hierarchy and classification varies from state to state. For this reason, sub-agencies and local troops, regions and posts have been aggregated into their parent agencies, meaning that in some cases an individual agency will have multiple associated ORI codes. For instance, a state police department may have the ORI codes of multiple barracks associated with it.\n\n\nThe Post created version 2 of this dataset in 2022 to refine and better define data categories, as well as improve documentation about how the data is obtained and classified. In addition to the altering of fields and formatting, a separate agencies dataset has been added which includes information on the law enforcement agencies which have been involved in at least one fatal shooting since 2015.\nHigh-level summary of major changes to the data schema introduced in version 2:\n\n\n\n\n\n\n\n\nV1 field\nV2 Field\nContext\n\n\n\n\nmanner_of_death\nfield removed\nThis database tracks fatal police shootings. Other weapons (e.g. a Taser) may be involved in the encounter, but the manner of death for victims in these records is always a police shooting. Thus, the field was removed.\n\n\nrace (single)\nrace (multiple)\nField converted from a single option to a list of options. Previously multi-racial or multi-ethnic victims were tracked using the Other option. This update allows us to more accurately categorize shootings of individuals with multiple ethnic and racial backgrounds.\n\n\n–\nrace_source\nThis field was added to identify the source from which race was determined, which can range from court documents to a researchers’ assessment based on a victim’s photograph.\n\n\nthreat_level\nthreat_type\nRenamed field to more accurately portray data we have collected, while removing implication of hierarchy.\n\n\nsigns_of_mental_illness\nwas_mental_illness_related\nRenamed field to more accurately portray data we have collected when news accounts or police reports reflect a victim may have had a history of mental health issues or was experiencing mental distress at the time of the shooting.\n\n\nflee\nflee_status\nRenamed field to more accurately portray data we collected\n\n\narmed\narmed_with\nThis field originally included the specific weapon if the victim had one and otherwise indicated unarmed, undetermined, or unknown. It has been updated to use federal classifications based on NIBRS, the national incident-level crime reporting system. toy category has been renamed to replica.\n\n\nis_geocoding_exact\nfield removed\nLocation information for an incident is often only available at the block level (eg. 100 Block of Main St). Precision and accuracy of geocoded location information was not sufficiently represented by this field.\n\n\n–\nlocation_precision\nNew field to indicate the precision level of the location data which was geocoded to generate the record’s coordinate data.\n\n\n–\nagency_ids\nList of agency ids to enable joining with the agencies csv.\n\n\n\n\n\n\nFile: /v2/fatal-police-shootings-data.csv\nSummary: Death record data about each incident and victim. The file can be downloaded at this URL. Victim information is obtained from any available recordings of the incident, news accounts, court records and/or official statements.\nFields:\n\n\n\n\n\n\n\n\n\n\nDescription\nType\nExample value\n\n\n\n\nIncident information\n\n\n\n\n\nid\nA unique identifier for each fatal police shooting incident.\nnumber\n2\n\n\ndate\nThe date of the fatal shooting.\nstring YYYY-MM-DD\n2020-06-25\n\n\nbody_camera\nWhether news reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.\nboolean True/ False\nTrue\n\n\ncity\nThe municipality where the fatal shooting took place\nstring\nNew Orleans\n\n\ncounty\nCounty where the fatal shooting took plce.\nstring\nBrown\n\n\nstate\nThe two-letter postal code abbreviation for the state in which the fatal shooting took place.\nstring\nLA\n\n\nlatitude\nThe latitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses. Please note that the precision and accuracy of incident coordinates varies depending on the precision of the input address which is often only available at the block level.\ndecimal number\n47.246826\n\n\nlongitude\nThe longitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses.\ndecimal number\n-123.121592\n\n\nlocation_precision\nIndicates the precision level of the input which was geocoded to generate the coordinate data.\nstring options:-address: Specific address, eg: 1 Main St.-poi_small: A small point of interest, eg: Jim's Pizza.-intersection: Intersection of two roads, eg: 6th Avenue and Izabel Street -poi_large: A large point of interest, eg: Central Mall. -block: Block level address, eg: 100 Block of Main St.-road: Road name, eg: Glenwood Drive -highway: Highway name, eg: Georgia Highway 11-not_available: Location was entered before current methodology was established.\nblock\n\n\nVictim information\n\n\n\n\n\nname\nThe name of the victim.\nstring\nJohn Doe\n\n\nage\nThe age of the victim at the time of the incident.\nnumber\n23\n\n\ngender\nThe gender of the victim. The Post identifies victims by the gender they identified with if reports indicate that it differs from their biological sex.\nstring options:- male- female- non-binary- --: Unknown\nmale\n\n\nrace\nThe race and ethnicity (where known) of the victim. May contain multiple values to accommodate for multi-racial or several racial and ethnic identifications. Race has been included where news accounts, police reports or other official documents specifically mention a victim’s race or where researchers were able to make a visual determination on racial identification through photos. With the introduction of v2 of the database, the Post has began tracking multiple race and ethnicity designations; prior to 2021, only one race or ethnicity was assigned to most victims.\nstring options:- W: White- B: Black- A: Asian heritage- N: Native American- H: Hispanic- O: Other- --: Unknown\n\"B;H\"\n\n\nrace_source\nSourcing methodology for victim race data.\nstring options: - public_record- clip- photo- other- not_available: Indicates older records which have race populated but The Post does not have details on the methodology because it was collected before the current structure was introduced. - undetermined: When The Post’s race research avenues have been exhausted and race is still unknown, this indicates that race is still unknown, but research is complete.- null\npublic_record\n\n\nwas_mental_illness_related\nWhether news reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.\nboolean True/False\nTrue\n\n\nthreat_type*\nActions the victim took leading up to the fatal shooting.\nstring options: - shoot: The victim fired a weapon.- point :The victim pointed a weapon at another individual.- attack: The victim attacked with other weapons or physical force.- threat: The victim had some kind of weapon visible to the officers on the scene.- move: The victim was moving in a threatening way.- flee: The victim was fleeing (see flee_status)- accident: :question: - undetermined: The threat type could not be determined from available evidence\npoint\n\n\narmed_with\nWhat, if anything, was the victim armed with per federal classifications based on NIBRS, the national incident-level crime reporting system. These categories are roughly reflected in local police data and forms. A NIBRS manual is can be found here (see values for data element Type Weapon/Force involved, on PDF page 104/report page 94).\nstring options: - gun: A firearm, handgun, shotgun, or other firearm- knife : A knife or other cutting instrument (razors, hatches, axes, cleavers, scissors, broken bottles, ice picks, etc.)- blunt_object: A blunt object (baseball bats, the butt of a handgun, clubs, bricks, tire irons, bottles, etc.)- other: Any other weapon (BB guns, pellet guns, Tasers, pepper spray, stun guns, etc.)- replica: A toy weapon, replica, or other non-functional firearm.- undetermined: Whether the victim had a weapon could not be determined from available evidence.- unknown: There was a weapon involved, but we do not know what kind.- unarmed: The victim had no weapon according to available evidence.- vehicle: A motor vehicle or vessel.\n\"gun;knife\"\n\n\nflee_status*\nHow, if at all, was the victim moving relative to officers leading up to the shooting.\nstring options: - foot : On foot- car : Via car - other: Via another vehicle- not: Not fleeing\nfoot\n\n\nagency_ids\nList of agency ids associated with the death record.\nstring\n\"1;2\"\n\n\n\nNote: threat_type and flee_status are not necessarily related. For example, there may be an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer.\n\n\n\nFile: /v2/fatal-police-shootings-agencies.csv\nSummary: Data for departments and agencies involved in a fatal police shooting in the database. This separate CSV can be joined with the death record CSV via the agency_ids value.\nFields:\n\n\n\n\n\n\n\n\n\n\nDescription\nType\nExample value\n\n\n\n\nid\nDepartment database id\ninteger\n1\n\n\nname\nDepartment name\nstring\nDavis County Sheriff's Office\n\n\ntype\nDepartment type\nstring options: -local_police: County or other local police -local_other: Other local department-sheriff: Sheriffoffice-other: Other non-local department-state_police: State police-state_other: Other statewide law enforcement-federal: Federal law enforcement\nstate_other\n\n\nstate\nDepartment state\nState in which the agency is located.\nUT\n\n\noricodes\nDepartment ORI codes (federal identifiers). Note that there can be multiple ORI codes for a single department due to aggregated sub-departments and/or differing values provided by the FBI vs DOJ.\nstring\n\"ABC123;XYZ987\"\n\n\ntotal_shootings\nTotal death records the agency has been involved with.\ninteger\n2" - }, - { - "objectID": "data/README-fatal-police-shoot.html#about-the-data", - "href": "data/README-fatal-police-shoot.html#about-the-data", - "title": "Fatal Force Database (v2)", - "section": "", - "text": "Each day researchers at The Post identify and manually record data for fatal police shootings in the United States. Each record requires at least two sources and must be approved by our editors before being publicly released.\nData about each fatal shooting is provided via two comma-separated value (CSV) files:\nDeath Record data for each victim and incident is included in /v2/fatal-police-shootings-data.csv.\nNew in v2 is a second Agencies csv, /v2/fatal-police-shootings-agencies.csv, which contains data for police agencies involved in at least one fatal police shooting since 2015. The agencies csv has a field called agency_ids, which can be used to associate each death record with the agencies involved.\nTo enable joining this dataset with other federal law enforcement datasets, in 2022 The Post undertook an effort to increase the coverage of federal Originating Agency Identifier (ORI) codes recorded for agencies in the database. Using agency data from the FBI and Department of Justice, reporters did a combination of automated and manual name and ORI code matching to fill in missing ORI code data. They also standardized department names. Agency naming, organization, hierarchy and classification varies from state to state. For this reason, sub-agencies and local troops, regions and posts have been aggregated into their parent agencies, meaning that in some cases an individual agency will have multiple associated ORI codes. For instance, a state police department may have the ORI codes of multiple barracks associated with it.\n\n\nThe Post created version 2 of this dataset in 2022 to refine and better define data categories, as well as improve documentation about how the data is obtained and classified. In addition to the altering of fields and formatting, a separate agencies dataset has been added which includes information on the law enforcement agencies which have been involved in at least one fatal shooting since 2015.\nHigh-level summary of major changes to the data schema introduced in version 2:\n\n\n\n\n\n\n\n\nV1 field\nV2 Field\nContext\n\n\n\n\nmanner_of_death\nfield removed\nThis database tracks fatal police shootings. Other weapons (e.g. a Taser) may be involved in the encounter, but the manner of death for victims in these records is always a police shooting. Thus, the field was removed.\n\n\nrace (single)\nrace (multiple)\nField converted from a single option to a list of options. Previously multi-racial or multi-ethnic victims were tracked using the Other option. This update allows us to more accurately categorize shootings of individuals with multiple ethnic and racial backgrounds.\n\n\n–\nrace_source\nThis field was added to identify the source from which race was determined, which can range from court documents to a researchers’ assessment based on a victim’s photograph.\n\n\nthreat_level\nthreat_type\nRenamed field to more accurately portray data we have collected, while removing implication of hierarchy.\n\n\nsigns_of_mental_illness\nwas_mental_illness_related\nRenamed field to more accurately portray data we have collected when news accounts or police reports reflect a victim may have had a history of mental health issues or was experiencing mental distress at the time of the shooting.\n\n\nflee\nflee_status\nRenamed field to more accurately portray data we collected\n\n\narmed\narmed_with\nThis field originally included the specific weapon if the victim had one and otherwise indicated unarmed, undetermined, or unknown. It has been updated to use federal classifications based on NIBRS, the national incident-level crime reporting system. toy category has been renamed to replica.\n\n\nis_geocoding_exact\nfield removed\nLocation information for an incident is often only available at the block level (eg. 100 Block of Main St). Precision and accuracy of geocoded location information was not sufficiently represented by this field.\n\n\n–\nlocation_precision\nNew field to indicate the precision level of the location data which was geocoded to generate the record’s coordinate data.\n\n\n–\nagency_ids\nList of agency ids to enable joining with the agencies csv.\n\n\n\n\n\n\nFile: /v2/fatal-police-shootings-data.csv\nSummary: Death record data about each incident and victim. The file can be downloaded at this URL. Victim information is obtained from any available recordings of the incident, news accounts, court records and/or official statements.\nFields:\n\n\n\n\n\n\n\n\n\n\nDescription\nType\nExample value\n\n\n\n\nIncident information\n\n\n\n\n\nid\nA unique identifier for each fatal police shooting incident.\nnumber\n2\n\n\ndate\nThe date of the fatal shooting.\nstring YYYY-MM-DD\n2020-06-25\n\n\nbody_camera\nWhether news reports have indicated an officer was wearing a body camera and it may have recorded some portion of the incident.\nboolean True/ False\nTrue\n\n\ncity\nThe municipality where the fatal shooting took place\nstring\nNew Orleans\n\n\ncounty\nCounty where the fatal shooting took plce.\nstring\nBrown\n\n\nstate\nThe two-letter postal code abbreviation for the state in which the fatal shooting took place.\nstring\nLA\n\n\nlatitude\nThe latitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses. Please note that the precision and accuracy of incident coordinates varies depending on the precision of the input address which is often only available at the block level.\ndecimal number\n47.246826\n\n\nlongitude\nThe longitude location of the shooting expressed as WGS84 coordinates, geocoded from addresses.\ndecimal number\n-123.121592\n\n\nlocation_precision\nIndicates the precision level of the input which was geocoded to generate the coordinate data.\nstring options:-address: Specific address, eg: 1 Main St.-poi_small: A small point of interest, eg: Jim's Pizza.-intersection: Intersection of two roads, eg: 6th Avenue and Izabel Street -poi_large: A large point of interest, eg: Central Mall. -block: Block level address, eg: 100 Block of Main St.-road: Road name, eg: Glenwood Drive -highway: Highway name, eg: Georgia Highway 11-not_available: Location was entered before current methodology was established.\nblock\n\n\nVictim information\n\n\n\n\n\nname\nThe name of the victim.\nstring\nJohn Doe\n\n\nage\nThe age of the victim at the time of the incident.\nnumber\n23\n\n\ngender\nThe gender of the victim. The Post identifies victims by the gender they identified with if reports indicate that it differs from their biological sex.\nstring options:- male- female- non-binary- --: Unknown\nmale\n\n\nrace\nThe race and ethnicity (where known) of the victim. May contain multiple values to accommodate for multi-racial or several racial and ethnic identifications. Race has been included where news accounts, police reports or other official documents specifically mention a victim’s race or where researchers were able to make a visual determination on racial identification through photos. With the introduction of v2 of the database, the Post has began tracking multiple race and ethnicity designations; prior to 2021, only one race or ethnicity was assigned to most victims.\nstring options:- W: White- B: Black- A: Asian heritage- N: Native American- H: Hispanic- O: Other- --: Unknown\n\"B;H\"\n\n\nrace_source\nSourcing methodology for victim race data.\nstring options: - public_record- clip- photo- other- not_available: Indicates older records which have race populated but The Post does not have details on the methodology because it was collected before the current structure was introduced. - undetermined: When The Post’s race research avenues have been exhausted and race is still unknown, this indicates that race is still unknown, but research is complete.- null\npublic_record\n\n\nwas_mental_illness_related\nWhether news reports have indicated the victim had a history of mental health issues, expressed suicidal intentions or was experiencing mental distress at the time of the shooting.\nboolean True/False\nTrue\n\n\nthreat_type*\nActions the victim took leading up to the fatal shooting.\nstring options: - shoot: The victim fired a weapon.- point :The victim pointed a weapon at another individual.- attack: The victim attacked with other weapons or physical force.- threat: The victim had some kind of weapon visible to the officers on the scene.- move: The victim was moving in a threatening way.- flee: The victim was fleeing (see flee_status)- accident: :question: - undetermined: The threat type could not be determined from available evidence\npoint\n\n\narmed_with\nWhat, if anything, was the victim armed with per federal classifications based on NIBRS, the national incident-level crime reporting system. These categories are roughly reflected in local police data and forms. A NIBRS manual is can be found here (see values for data element Type Weapon/Force involved, on PDF page 104/report page 94).\nstring options: - gun: A firearm, handgun, shotgun, or other firearm- knife : A knife or other cutting instrument (razors, hatches, axes, cleavers, scissors, broken bottles, ice picks, etc.)- blunt_object: A blunt object (baseball bats, the butt of a handgun, clubs, bricks, tire irons, bottles, etc.)- other: Any other weapon (BB guns, pellet guns, Tasers, pepper spray, stun guns, etc.)- replica: A toy weapon, replica, or other non-functional firearm.- undetermined: Whether the victim had a weapon could not be determined from available evidence.- unknown: There was a weapon involved, but we do not know what kind.- unarmed: The victim had no weapon according to available evidence.- vehicle: A motor vehicle or vessel.\n\"gun;knife\"\n\n\nflee_status*\nHow, if at all, was the victim moving relative to officers leading up to the shooting.\nstring options: - foot : On foot- car : Via car - other: Via another vehicle- not: Not fleeing\nfoot\n\n\nagency_ids\nList of agency ids associated with the death record.\nstring\n\"1;2\"\n\n\n\nNote: threat_type and flee_status are not necessarily related. For example, there may be an incident in which the suspect is fleeing and at the same time turns to fire at gun at the officer.\n\n\n\nFile: /v2/fatal-police-shootings-agencies.csv\nSummary: Data for departments and agencies involved in a fatal police shooting in the database. This separate CSV can be joined with the death record CSV via the agency_ids value.\nFields:\n\n\n\n\n\n\n\n\n\n\nDescription\nType\nExample value\n\n\n\n\nid\nDepartment database id\ninteger\n1\n\n\nname\nDepartment name\nstring\nDavis County Sheriff's Office\n\n\ntype\nDepartment type\nstring options: -local_police: County or other local police -local_other: Other local department-sheriff: Sheriffoffice-other: Other non-local department-state_police: State police-state_other: Other statewide law enforcement-federal: Federal law enforcement\nstate_other\n\n\nstate\nDepartment state\nState in which the agency is located.\nUT\n\n\noricodes\nDepartment ORI codes (federal identifiers). Note that there can be multiple ORI codes for a single department due to aggregated sub-departments and/or differing values provided by the FBI vs DOJ.\nstring\n\"ABC123;XYZ987\"\n\n\ntotal_shootings\nTotal death records the agency has been involved with.\ninteger\n2" - }, - { - "objectID": "data/README-police-locals.html", - "href": "data/README-police-locals.html", - "title": "Police Residence", - "section": "", - "text": "Police Residence\nThis folder contains data behind the story Most Police Don’t Live In The Cities They Serve.\nIncludes the cities with the 75 largest police forces, with the exception of Honolulu for which data is not available. All calculations are based on data from the U.S. Census.\nThe Census Bureau numbers are potentially going to differ from other counts for three reasons:\n\nThe census category for police officers also includes sheriffs, transit police and others who might not be under the same jurisdiction as a city’s police department proper. The census category won’t include private security officers.\nThe census data is estimated from 2006 to 2010; police forces may have changed in size since then.\nThere is always a margin of error in census numbers; they are estimates, not complete counts.\n\nHow to read police-locals.csv\n\n\n\n\n\n\n\nHeader\nDefinition\n\n\n\n\ncity\nU.S. city\n\n\npolice_force_size\nNumber of police officers serving that city\n\n\nall\nPercentage of the total police force that lives in the city\n\n\nwhite\nPercentage of white (non-Hispanic) police officers who live in the city\n\n\nnon-white\nPercentage of non-white police officers who live in the city\n\n\nblack\nPercentage of black police officers who live in the city\n\n\nhispanic\nPercentage of Hispanic police officers who live in the city\n\n\nasian\nPercentage of Asian police officers who live in the city\n\n\n\nNote: When a cell contains ** it means that there are fewer than 100 police officers of that race serving that city." - }, - { - "objectID": "data.html", - "href": "data.html", - "title": "Data", - "section": "", - "text": "library(tidyverse)\nlibrary(usmap)\nlibrary(sf)\nlibrary(infer)\nlibrary(moderndive)\n\n\n##Tidying Data\n\n#creating dfs from .csv files\npolice_locals <- read_csv(\"data/police-locals.csv\")\nagencies <- read_csv(\"data/fatal-police-shootings-agencies.csv\")\nshootings <- read_csv(\"data/fatal-police-shootings-data.csv\")\n\n#removing old `city` tag from data set that we created when decatenated the city names\npolice_locals <- police_locals |>\n select(-city_old)\n\n# creating `agencies` df with just police departments\nagencies <- agencies |>\n filter(grepl(\"department\", tolower(name))) |>\n filter(!grepl(\"county\", tolower(name)))\n\n#creating binned categorical account of if shooting victim was `armed`\nshootings <- shootings |>\n mutate(armed = case_when(is.na(armed_with) ~ \"NO\",\n armed_with == \"unarmed\" ~ \"NO\",\n armed_with == \"unknown\" ~ \"NO\",\n armed_with == \"undetermined\" ~ \"NO\",\n armed_with == \"gun\" ~ \"YES\",\n armed_with == \"knife\" ~ \"YES\",\n armed_with == \"blunt_object\" ~ \"YES\",\n armed_with == \"other\" ~ \"YES\",\n armed_with == \"replica\" ~ \"YES\",\n armed_with == \"vehicle\" ~ \"YES\"))\n\n#creating df with only agency `names`, `id`, and `state`\nagencies_ids <- agencies |>\n select(name, id, state)\n\n#creating df with `city`, `agency`, and `state` info for each shooting\nshooting_agencies <- shootings |>\n select(city, agency_ids, state)\n\n#changing `shooting` var in `shooting_agencies` df to numeric\nshooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)\n\n#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`\nagencies_w_cities <- agencies_ids |>\n left_join(shooting_agencies, by = c(\"id\" = \"agency_ids\", \"state\" = \"state\")) |>\n drop_na(city) |>\n distinct(id, .keep_all = TRUE)\n\n#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`\nagencies_census <- agencies_w_cities |>\n full_join(police_locals, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n drop_na(police_force_size) |>\n distinct(id, .keep_all = TRUE) |>\n mutate(majority = if_else(all >= 0.5, \"TRUE\", \"FALSE\"))\n\n#creating df of only shootings involving agencies within `agencies` df\nshootings_case <- shootings |>\n right_join(agencies_census, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n select(-agency_ids) |>\n rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>\n select(-location_precision, -race_source)\n\n\n#count shootings by agency\nshootings_by_agency <- shootings_case |>\n count(agency)\n\n#find top 25 agencies with the most shootings\ntop_25_agencies <- shootings_by_agency |>\n slice_max(n, n = 25)\n\n# visulize top 25 agencies with the most shootings\nggplot(data = top_25_agencies,\n mapping = aes(x = agency, y = n)) +\n geom_col() +\n theme(axis.text.x = element_text(angle = 75,\n vjust = 1,\n hjust = 1,\n margin = margin(t = 5, b = 5)))\n\n\n\n\n\n#mapping Locations of Police-Involved Shootings between 2015 and 2023\n\n#load geo-viz libraries\nlibrary(ggmap)\nlibrary(maps)\nlibrary(mapdata)\n\n#create blank map\nusa <- map_data(\"usa\")\nstates <- map_data(\"state\")\n\n#add locations of shootings to maps\nshot_map <- ggplot(data = states) + \n geom_polygon(aes(x = long, y = lat, fill = group, group = group), color = \"white\") + \n coord_fixed(1.3) +\n guides(fill=FALSE) + # do this to leave off the color legend\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"black\", size = .2) +\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"red\", size = .1) +\n labs(title = \"Locations of Police-Involved Shootings between 2015 and 2023\",\n captions = \"This is only includes cities where we have agency census data.\",\n x = \"Longitude\",\n y = \"Latitude\")\n\n\n#creating df with total shootings per agency and census data\nagencies_census <- agencies_census |>\n left_join(shootings_by_agency, by = c(\"name\" = \"agency\"))\n\n#prelim visualization of relationship between percentage of officer residency and number of fatal shootings per agency\nagencies_census |>\n ggplot(mapping = aes(x = all, y = n)) +\n geom_point() +\n geom_smooth(method = \"lm\", se = TRUE)\n\n\n\n#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside\np0 <- shootings_case |>\n ggplot(aes(x = majority, fill = armed)) +\n geom_bar() + \n labs(title = \"Shootings in Cities where a Majority of Officers Reside\",\n caption = \"This is only includes shootings where we have agency census data.\",\n x = \"Does a majority a of the total police force live in the city?\",\n y = \"Number of fatal shootings\",\n fill = \"Victim Armed?\")\np0\n\n\n\n#calculate mean number of shootings per agency in cities where a majority of officers reside in the city\nmajority_mean <- shootings_case |>\n filter(majority == TRUE) |>\n count(agency) |>\n summarize(maj_mean = mean(n))\n\n#calculate mean number of shootings per agency in cities where a minority of officers reside in the city\nminority_mean <- shootings_case |>\n filter(majority == FALSE) |>\n count(agency) |>\n summarize(min_mean = mean(n))\n\n#calculate a difference in means between the `majority` and `minority`\ndiff_in_means <- majority_mean - minority_mean\ndiff_in_means\n\n maj_mean\n1 -2.575\n\n#tidy table\nknitr::kable(head(diff_in_means))\n\n\n\n\nmaj_mean\n\n\n\n\n-2.575\n\n\n\n\n\n\n#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency\nfit <- lm(n ~ all, data = agencies_census)\nfit\n\n\nCall:\nlm(formula = n ~ all, data = agencies_census)\n\nCoefficients:\n(Intercept) all \n 35.7782 -0.5874 \n\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n#add `armed` and `majority` to `shootings_by_agency` df\nshootings_by_agency_census <- shootings_case |>\n group_by(agency) |>\n count(armed) |>\n drop_na(n, armed) |>\n right_join(agencies_census, by = c(\"agency\" = \"name\")) |>\n distinct(armed, .keep_all = TRUE)\n\nshootings_by_agency_census <- shootings_by_agency_census |>\n select(n.x, armed, all) \n\n#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency\nfit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)\nfit_multi\n\n\nCall:\nlm(formula = n.x ~ all + armed, data = shootings_by_agency_census)\n\nCoefficients:\n(Intercept) all armedYES \n 4.117 1.211 24.921 \n\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\nThe model equation for fit is:\n[ = 35.7782 - 0.5874 ]\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\nThe model equation for fit_multi considering victim armament (armed) is:\n[ = 4.117 + 1.211 + 24.921 ]\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.268\n\n\nInference for a Difference in Means\n\n\\(H_0\\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.\n\\(H_A\\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.\n\n– \\(H_0 : \\mu_{maj} − \\mu_{min} = 0\\), or equivalently \\(H_0 : \\mu_{maj} = \\mu_{min}\\) – \\(H_A : \\mu_{maj} − \\mu_{min} < 0\\), or equivalently \\(H_A : \\mu_{maj} < \\mu_{min}\\)\n\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\ntest_stat_cor\n\nResponse: n (numeric)\nExplanatory: white (numeric)\n# A tibble: 1 × 1\n stat\n <dbl>\n1 -0.0470\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nInference for a Correlation\n\n\\(H_O\\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\\(H_A\\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\n\\(H_0 : \\rho = 0\\)\n\\(H_0 : \\rho \\neq 0\\)" - } -] \ No newline at end of file diff --git a/_site/site_libs/bootstrap/bootstrap-icons.css b/_site/site_libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index 94f1940..0000000 --- a/_site/site_libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,2018 +0,0 @@ -@font-face { - font-display: block; - font-family: "bootstrap-icons"; - src: -url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); -} - -.bi::before, -[class^="bi-"]::before, -[class*=" bi-"]::before { - display: inline-block; - 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document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._popper&&this._popper.destroy(),this._menu.classList.remove(Je),this._element.classList.remove(Je),this._element.setAttribute("aria-expanded","false"),U.removeDataAttribute(this._menu,"popper"),j.trigger(this._element,"hidden.bs.dropdown",t))}_getConfig(t){if(t={...this.constructor.Default,...U.getDataAttributes(this._element),...t},a(Ue,t,this.constructor.DefaultType),"object"==typeof t.reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${Ue.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(t){if(void 0===Fe)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let e=this._element;"parent"===this._config.reference?e=t:o(this._config.reference)?e=r(this._config.reference):"object"==typeof this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" 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this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const 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a/_site/site_libs/quarto-html/quarto-syntax-highlighting.css +++ /dev/null @@ -1,203 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-ot-color: #003B4F; - --quarto-hl-at-color: #657422; - --quarto-hl-ss-color: #20794D; - --quarto-hl-an-color: #5E5E5E; - --quarto-hl-fu-color: #4758AB; - --quarto-hl-st-color: #20794D; - --quarto-hl-cf-color: #003B4F; - --quarto-hl-op-color: #5E5E5E; - --quarto-hl-er-color: #AD0000; - --quarto-hl-bn-color: #AD0000; - --quarto-hl-al-color: #AD0000; - --quarto-hl-va-color: #111111; - --quarto-hl-bu-color: inherit; - --quarto-hl-ex-color: inherit; - --quarto-hl-pp-color: #AD0000; - --quarto-hl-in-color: #5E5E5E; - --quarto-hl-vs-color: #20794D; - --quarto-hl-wa-color: #5E5E5E; - --quarto-hl-do-color: #5E5E5E; - --quarto-hl-im-color: #00769E; - --quarto-hl-ch-color: #20794D; - --quarto-hl-dt-color: #AD0000; - --quarto-hl-fl-color: #AD0000; - --quarto-hl-co-color: #5E5E5E; - --quarto-hl-cv-color: #5E5E5E; - --quarto-hl-cn-color: #8f5902; - 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- } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - const currentPagePath = offsetAbsoluteUrl(window.location.href); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - if ( - item === currentPagePath || - item === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; - - const toggleIcon = window.document.createElement("i"); - toggleIcon.classList.add("quarto-sidebar-toggle-icon"); - toggleIcon.classList.add("bi"); - toggleIcon.classList.add("bi-caret-down-fill"); - - const toggleTitle = window.document.createElement("div"); - const titleEl = window.document.body.querySelector( - placeholderDescriptor.titleSelector - ); - if (titleEl) { - toggleTitle.append( - titleEl.textContent || titleEl.innerText, - toggleIcon - ); - } - toggleTitle.classList.add("zindex-over-content"); - toggleTitle.classList.add("quarto-sidebar-toggle-title"); - toggleContainer.append(toggleTitle); - - const toggleContents = window.document.createElement("div"); - toggleContents.classList = el.classList; - toggleContents.classList.add("zindex-over-content"); - toggleContents.classList.add("quarto-sidebar-toggle-contents"); - for (const child of el.children) { - if (child.id === "toc-title") { - continue; - } - - const clone = child.cloneNode(true); - clone.style.opacity = 1; - clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } - - layoutMarginEls(); - }); - } - } - - // Manage the visibility of the toc and the sidebar - const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { - id: "quarto-toc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { - id: "quarto-sidebarnav-toggle", - titleSelector: ".title", - dismissOnClick: false, - }); - let tocLeftScrollVisibility; - if (leftTocEl) { - tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { - id: "quarto-lefttoc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - } - - // Find the first element that uses formatting in special columns - const conflictingEls = window.document.body.querySelectorAll( - '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' - ); - - // Filter all the possibly conflicting elements into ones - // the do conflict on the left or ride side - const arrConflictingEls = Array.from(conflictingEls); - const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // Process the collapse state if this is an UL - if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - - if (tocEl) { - walk(tocEl, 0); - updateActiveLink(); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -// grouped tabsets -window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - localStorage.setItem("quarto-persistent-tabsets-data", "{}"); - return {}; - } - if (data) { - return JSON.parse(data); - } - } - - function setTabSettings(data) { - localStorage.setItem( - "quarto-persistent-tabsets-data", - JSON.stringify(data) - ); - } - - function setTabState(groupName, groupValue) { - const data = getTabSettings(); - data[groupName] = groupValue; - setTabSettings(data); - } - - function toggleTab(tab, active) { - const tabPanelId = tab.getAttribute("aria-controls"); - const tabPanel = document.getElementById(tabPanelId); - if (active) { - tab.classList.add("active"); - tabPanel.classList.add("active"); - } else { - tab.classList.remove("active"); - tabPanel.classList.remove("active"); - } - } - - function toggleAll(selectedGroup, selectorsToSync) { - for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { - const active = selectedGroup === thisGroup; - for (const tab of tabs) { - toggleTab(tab, active); - } - } - } - - function findSelectorsToSyncByLanguage() { - const result = {}; - const tabs = Array.from( - document.querySelectorAll(`div[data-group] a[id^='tabset-']`) - ); - for (const item of tabs) { - const div = item.parentElement.parentElement.parentElement; - const group = div.getAttribute("data-group"); - if (!result[group]) { - result[group] = {}; - } - const selectorsToSync = result[group]; - const value = item.innerHTML; - if (!selectorsToSync[value]) { - selectorsToSync[value] = []; - } - selectorsToSync[value].push(item); - } - return result; - } - - function setupSelectorSync() { - const selectorsToSync = findSelectorsToSyncByLanguage(); - Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { - Object.entries(tabSetsByValue).forEach(([value, items]) => { - items.forEach((item) => { - item.addEventListener("click", (_event) => { - setTabState(group, value); - toggleAll(value, selectorsToSync[group]); - }); - }); - }); - }); - return selectorsToSync; - } - - const selectorsToSync = setupSelectorSync(); - for (const [group, selectedName] of 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a/_site/site_libs/quarto-search/quarto-search.js b/_site/site_libs/quarto-search/quarto-search.js deleted file mode 100644 index f5d852d..0000000 --- a/_site/site_libs/quarto-search/quarto-search.js +++ /dev/null @@ -1,1140 +0,0 @@ -const kQueryArg = "q"; -const kResultsArg = "show-results"; - -// If items don't provide a URL, then both the navigator and the onSelect -// function aren't called (and therefore, the default implementation is used) -// -// We're using this sentinel URL to signal to those handlers that this -// item is a more item (along with the type) and can be handled appropriately -const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Ensure that search is available on this page. If it isn't, - // should return early and not do anything - var searchEl = window.document.getElementById("quarto-search"); - if (!searchEl) return; - - const { autocomplete } = window["@algolia/autocomplete-js"]; - - let quartoSearchOptions = {}; - let language = {}; - const searchOptionEl = window.document.getElementById( - "quarto-search-options" - ); - if (searchOptionEl) { - const jsonStr = searchOptionEl.textContent; - quartoSearchOptions = JSON.parse(jsonStr); - language = quartoSearchOptions.language; - } - - // note the search mode - if (quartoSearchOptions.type === "overlay") { - searchEl.classList.add("type-overlay"); - } else { - searchEl.classList.add("type-textbox"); - } - - // Used to determine highlighting behavior for this page - // A `q` query param is expected when the user follows a search - // to this page - const currentUrl = new URL(window.location); - const query = currentUrl.searchParams.get(kQueryArg); - const showSearchResults = currentUrl.searchParams.get(kResultsArg); - const mainEl = window.document.querySelector("main"); - - // highlight matches on the page - if (query !== null && mainEl) { - // perform any highlighting - highlight(escapeRegExp(query), mainEl); - - // fix up the URL to remove the q query param - const replacementUrl = new URL(window.location); - replacementUrl.searchParams.delete(kQueryArg); - window.history.replaceState({}, "", replacementUrl); - } - - // function to clear highlighting on the page when the search query changes - // (e.g. if the user edits the query or clears it) - let highlighting = true; - const resetHighlighting = (searchTerm) => { - if (mainEl && highlighting && query !== null && searchTerm !== query) { - clearHighlight(query, mainEl); - highlighting = false; - } - }; - - // Clear search highlighting when the user scrolls sufficiently - const resetFn = () => { - resetHighlighting(""); - window.removeEventListener("quarto-hrChanged", resetFn); - window.removeEventListener("quarto-sectionChanged", resetFn); - }; - - // Register this event after the initial scrolling and settling of events - // on the page - window.addEventListener("quarto-hrChanged", resetFn); - window.addEventListener("quarto-sectionChanged", resetFn); - - // Responsively switch to overlay mode if the search is present on the navbar - // Note that switching the sidebar to overlay mode requires more coordinate (not just - // the media query since we generate different HTML for sidebar overlays than we do - // for sidebar input UI) - const detachedMediaQuery = - quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; - - // If configured, include the analytics client to send insights - const plugins = configurePlugins(quartoSearchOptions); - - let lastState = null; - const { setIsOpen, setQuery, setCollections } = autocomplete({ - container: searchEl, - detachedMediaQuery: detachedMediaQuery, - defaultActiveItemId: 0, - panelContainer: "#quarto-search-results", - panelPlacement: quartoSearchOptions["panel-placement"], - debug: false, - openOnFocus: true, - plugins, - classNames: { - form: "d-flex", - }, - translations: { - clearButtonTitle: language["search-clear-button-title"], - detachedCancelButtonText: language["search-detached-cancel-button-title"], - submitButtonTitle: language["search-submit-button-title"], - }, - initialState: { - query, - }, - getItemUrl({ item }) { - return item.href; - }, - onStateChange({ state }) { - // Perhaps reset highlighting - resetHighlighting(state.query); - - // If the panel just opened, ensure the panel is positioned properly - if (state.isOpen) { - if (lastState && !lastState.isOpen) { - setTimeout(() => { - positionPanel(quartoSearchOptions["panel-placement"]); - }, 150); - } - } - - // Perhaps show the copy link - showCopyLink(state.query, quartoSearchOptions); - - lastState = state; - }, - reshape({ sources, state }) { - return sources.map((source) => { - try { - const items = source.getItems(); - - // Validate the items - validateItems(items); - - // group the items by document - const groupedItems = new Map(); - items.forEach((item) => { - const hrefParts = item.href.split("#"); - const baseHref = hrefParts[0]; - const isDocumentItem = hrefParts.length === 1; - - const items = groupedItems.get(baseHref); - if (!items) { - groupedItems.set(baseHref, [item]); - } else { - // If the href for this item matches the document - // exactly, place this item first as it is the item that represents - // the document itself - if (isDocumentItem) { - items.unshift(item); - } else { - items.push(item); - } - groupedItems.set(baseHref, items); - } - }); - - const reshapedItems = []; - let count = 1; - for (const [_key, value] of groupedItems) { - const firstItem = value[0]; - reshapedItems.push({ - ...firstItem, - type: kItemTypeDoc, - }); - - const collapseMatches = quartoSearchOptions["collapse-after"]; - const collapseCount = - typeof collapseMatches === "number" ? collapseMatches : 1; - - if (value.length > 1) { - const target = `search-more-${count}`; - const isExpanded = - state.context.expanded && - state.context.expanded.includes(target); - - const remainingCount = value.length - collapseCount; - - for (let i = 1; i < value.length; i++) { - if (collapseMatches && i === collapseCount) { - reshapedItems.push({ - target, - title: isExpanded - ? language["search-hide-matches-text"] - : remainingCount === 1 - ? `${remainingCount} ${language["search-more-match-text"]}` - : `${remainingCount} ${language["search-more-matches-text"]}`, - type: kItemTypeMore, - href: kItemTypeMoreHref, - }); - } - - if (isExpanded || !collapseMatches || i < collapseCount) { - reshapedItems.push({ - ...value[i], - type: kItemTypeItem, - target, - }); - } - } - } - count += 1; - } - - return { - ...source, - getItems() { - return reshapedItems; - }, - }; - } catch (error) { - // Some form of error occurred - return { - ...source, - getItems() { - return [ - { - title: error.name || "An Error Occurred While Searching", - text: - error.message || - "An unknown error occurred while attempting to perform the requested search.", - type: kItemTypeError, - }, - ]; - }, - }; - } - }); - }, - navigator: { - navigate({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.location.assign(itemUrl); - } - }, - navigateNewTab({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - const windowReference = window.open(itemUrl, "_blank", "noopener"); - if (windowReference) { - windowReference.focus(); - } - } - }, - navigateNewWindow({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.open(itemUrl, "_blank", "noopener"); - } - }, - }, - getSources({ state, setContext, setActiveItemId, refresh }) { - return [ - { - sourceId: "documents", - getItemUrl({ item }) { - if (item.href) { - return offsetURL(item.href); - } else { - return undefined; - } - }, - onSelect({ - item, - state, - setContext, - setIsOpen, - setActiveItemId, - refresh, - }) { - if (item.type === kItemTypeMore) { - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - - // Toggle more - setIsOpen(true); - } - }, - getItems({ query }) { - if (query === null || query === "") { - return []; - } - - const limit = quartoSearchOptions.limit; - if (quartoSearchOptions.algolia) { - return algoliaSearch(query, limit, quartoSearchOptions.algolia); - } else { - // Fuse search options - const fuseSearchOptions = { - isCaseSensitive: false, - shouldSort: true, - minMatchCharLength: 2, - limit: limit, - }; - - return readSearchData().then(function (fuse) { - return fuseSearch(query, fuse, fuseSearchOptions); - }); - } - }, - templates: { - noResults({ createElement }) { - const hasQuery = lastState.query; - - return createElement( - "div", - { - class: `quarto-search-no-results${ - hasQuery ? "" : " no-query" - }`, - }, - language["search-no-results-text"] - ); - }, - header({ items, createElement }) { - // count the documents - const count = items.filter((item) => { - return item.type === kItemTypeDoc; - }).length; - - if (count > 0) { - return createElement( - "div", - { class: "search-result-header" }, - `${count} ${language["search-matching-documents-text"]}` - ); - } else { - return createElement( - "div", - { class: "search-result-header-no-results" }, - `` - ); - } - }, - footer({ _items, createElement }) { - if ( - quartoSearchOptions.algolia && - quartoSearchOptions.algolia["show-logo"] - ) { - const libDir = quartoSearchOptions.algolia["libDir"]; - const logo = createElement("img", { - src: offsetURL( - `${libDir}/quarto-search/search-by-algolia.svg` - ), - class: "algolia-search-logo", - }); - return createElement( - "a", - { href: "http://www.algolia.com/" }, - logo - ); - } - }, - - item({ item, createElement }) { - return renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh - ); - }, - }, - }, - ]; - }, - }); - - window.quartoOpenSearch = () => { - setIsOpen(false); - setIsOpen(true); - focusSearchInput(); - }; - - // Remove the labeleledby attribute since it is pointing - // to a non-existent label - if (quartoSearchOptions.type === "overlay") { - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - if (inputEl) { - inputEl.removeAttribute("aria-labelledby"); - } - } - - // If the main document scrolls dismiss the search results - // (otherwise, since they're floating in the document they can scroll with the document) - window.document.body.onscroll = () => { - setIsOpen(false); - }; - - if (showSearchResults) { - setIsOpen(true); - focusSearchInput(); - } -}); - -function configurePlugins(quartoSearchOptions) { - const autocompletePlugins = []; - const algoliaOptions = quartoSearchOptions.algolia; - if ( - algoliaOptions && - algoliaOptions["analytics-events"] && - algoliaOptions["search-only-api-key"] && - algoliaOptions["application-id"] - ) { - const apiKey = algoliaOptions["search-only-api-key"]; - const appId = algoliaOptions["application-id"]; - - // Aloglia insights may not be loaded because they require cookie consent - // Use deferred loading so events will start being recorded when/if consent - // is granted. - const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { - if ( - window.aa && - window["@algolia/autocomplete-plugin-algolia-insights"] - ) { - window.aa("init", { - appId, - apiKey, - useCookie: true, - }); - - const { createAlgoliaInsightsPlugin } = - window["@algolia/autocomplete-plugin-algolia-insights"]; - // Register the insights client - const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ - insightsClient: window.aa, - onItemsChange({ insights, insightsEvents }) { - const events = insightsEvents.map((event) => { - const maxEvents = event.objectIDs.slice(0, 20); - return { - ...event, - objectIDs: maxEvents, - }; - }); - - insights.viewedObjectIDs(...events); - }, - }); - return algoliaInsightsPlugin; - } - }); - - // Add the plugin - autocompletePlugins.push(algoliaInsightsDeferredPlugin); - return autocompletePlugins; - } -} - -// For plugins that may not load immediately, create a wrapper -// plugin and forward events and plugin data once the plugin -// is initialized. This is useful for cases like cookie consent -// which may prevent the analytics insights event plugin from initializing -// immediately. -function deferredLoadPlugin(createPlugin) { - let plugin = undefined; - let subscribeObj = undefined; - const wrappedPlugin = () => { - if (!plugin && subscribeObj) { - plugin = createPlugin(); - if (plugin && plugin.subscribe) { - plugin.subscribe(subscribeObj); - } - } - return plugin; - }; - - return { - subscribe: (obj) => { - subscribeObj = obj; - }, - onStateChange: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onStateChange) { - plugin.onStateChange(obj); - } - }, - onSubmit: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onSubmit) { - plugin.onSubmit(obj); - } - }, - onReset: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onReset) { - plugin.onReset(obj); - } - }, - getSources: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.getSources) { - return plugin.getSources(obj); - } else { - return Promise.resolve([]); - } - }, - data: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.data) { - plugin.data(obj); - } - }, - }; -} - -function validateItems(items) { - // Validate the first item - if (items.length > 0) { - const item = items[0]; - const missingFields = []; - if (item.href == undefined) { - missingFields.push("href"); - } - if (!item.title == undefined) { - missingFields.push("title"); - } - if (!item.text == undefined) { - missingFields.push("text"); - } - - if (missingFields.length === 1) { - throw { - name: `Error: Search index is missing the ${missingFields[0]} field.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } else if (missingFields.length > 1) { - const missingFieldList = missingFields - .map((field) => { - return `${field}`; - }) - .join(", "); - - throw { - name: `Error: Search index is missing the following fields: ${missingFieldList}.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } - } -} - -let lastQuery = null; -function showCopyLink(query, options) { - const language = options.language; - lastQuery = query; - // Insert share icon - const inputSuffixEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix" - ); - - if (inputSuffixEl) { - let copyButtonEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" - ); - - if (copyButtonEl === null) { - copyButtonEl = window.document.createElement("button"); - copyButtonEl.setAttribute("class", "aa-CopyButton"); - copyButtonEl.setAttribute("type", "button"); - copyButtonEl.setAttribute("title", language["search-copy-link-title"]); - copyButtonEl.onmousedown = (e) => { - e.preventDefault(); - e.stopPropagation(); - }; - - const linkIcon = "bi-clipboard"; - const checkIcon = "bi-check2"; - - const shareIconEl = window.document.createElement("i"); - shareIconEl.setAttribute("class", `bi ${linkIcon}`); - copyButtonEl.appendChild(shareIconEl); - inputSuffixEl.prepend(copyButtonEl); - - const clipboard = new window.ClipboardJS(".aa-CopyButton", { - text: function (_trigger) { - const copyUrl = new URL(window.location); - copyUrl.searchParams.set(kQueryArg, lastQuery); - copyUrl.searchParams.set(kResultsArg, "1"); - return copyUrl.toString(); - }, - }); - clipboard.on("success", function (e) { - // Focus the input - - // button target - const button = e.trigger; - const icon = button.querySelector("i.bi"); - - // flash "checked" - icon.classList.add(checkIcon); - icon.classList.remove(linkIcon); - setTimeout(function () { - icon.classList.remove(checkIcon); - icon.classList.add(linkIcon); - }, 1000); - }); - } - - // If there is a query, show the link icon - if (copyButtonEl) { - if (lastQuery && options["copy-button"]) { - copyButtonEl.style.display = "flex"; - } else { - copyButtonEl.style.display = "none"; - } - } - } -} - -/* Search Index Handling */ -// create the index -var fuseIndex = undefined; -async function readSearchData() { - // Initialize the search index on demand - if (fuseIndex === undefined) { - // create fuse index - const options = { - keys: [ - { name: "title", weight: 20 }, - { name: "section", weight: 20 }, - { name: "text", weight: 10 }, - ], - ignoreLocation: true, - threshold: 0.1, - }; - const fuse = new window.Fuse([], options); - - // fetch the main search.json - const response = await fetch(offsetURL("search.json")); - if (response.status == 200) { - return response.json().then(function (searchDocs) { - searchDocs.forEach(function (searchDoc) { - fuse.add(searchDoc); - }); - fuseIndex = fuse; - return fuseIndex; - }); - } else { - return Promise.reject( - new Error( - "Unexpected status from search index request: " + response.status - ) - ); - } - } - return fuseIndex; -} - -function inputElement() { - return window.document.body.querySelector(".aa-Form .aa-Input"); -} - -function focusSearchInput() { - setTimeout(() => { - const inputEl = inputElement(); - if (inputEl) { - inputEl.focus(); - } - }, 50); -} - -/* Panels */ -const kItemTypeDoc = "document"; -const kItemTypeMore = "document-more"; -const kItemTypeItem = "document-item"; -const kItemTypeError = "error"; - -function renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh -) { - switch (item.type) { - case kItemTypeDoc: - return createDocumentCard( - createElement, - "file-richtext", - item.title, - item.section, - item.text, - item.href - ); - case kItemTypeMore: - return createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh - ); - case kItemTypeItem: - return createSectionCard( - createElement, - item.section, - item.text, - item.href - ); - case kItemTypeError: - return createErrorCard(createElement, item.title, item.text); - default: - return undefined; - } -} - -function createDocumentCard(createElement, icon, title, section, text, href) { - const iconEl = createElement("i", { - class: `bi bi-${icon} search-result-icon`, - }); - const titleEl = createElement("p", { class: "search-result-title" }, title); - const titleContainerEl = createElement( - "div", - { class: "search-result-title-container" }, - [iconEl, titleEl] - ); - - const textEls = []; - if (section) { - const sectionEl = createElement( - "p", - { class: "search-result-section" }, - section - ); - textEls.push(sectionEl); - } - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - textEls.push(descEl); - - const textContainerEl = createElement( - "div", - { class: "search-result-text-container" }, - textEls - ); - - const containerEl = createElement( - "div", - { - class: "search-result-container", - }, - [titleContainerEl, textContainerEl] - ); - - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - containerEl - ); - - const classes = ["search-result-doc", "search-item"]; - if (!section) { - classes.push("document-selectable"); - } - - return createElement( - "div", - { - class: classes.join(" "), - }, - linkEl - ); -} - -function createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh -) { - const moreCardEl = createElement( - "div", - { - class: "search-result-more search-item", - onClick: (e) => { - // Handle expanding the sections by adding the expanded - // section to the list of expanded sections - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - e.stopPropagation(); - }, - }, - item.title - ); - - return moreCardEl; -} - -function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { - const expanded = state.context.expanded || []; - if (expanded.includes(item.target)) { - setContext({ - expanded: expanded.filter((target) => target !== item.target), - }); - } else { - setContext({ expanded: [...expanded, item.target] }); - } - - refresh(); - setActiveItemId(item.__autocomplete_id); -} - -function createSectionCard(createElement, section, text, href) { - const sectionEl = createSection(createElement, section, text, href); - return createElement( - "div", - { - class: "search-result-doc-section search-item", - }, - sectionEl - ); -} - -function createSection(createElement, title, text, href) { - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { class: "search-result-section" }, title); - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - [titleEl, descEl] - ); - return linkEl; -} - -function createErrorCard(createElement, title, text) { - const descEl = createElement("p", { - class: "search-error-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { - class: "search-error-title", - dangerouslySetInnerHTML: { - __html: ` ${title}`, - }, - }); - const errorEl = createElement("div", { class: "search-error" }, [ - titleEl, - descEl, - ]); - return errorEl; -} - -function positionPanel(pos) { - const panelEl = window.document.querySelector( - "#quarto-search-results .aa-Panel" - ); - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - - if (panelEl && inputEl) { - panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; - if (pos === "start") { - panelEl.style.left = `${Math.round(inputEl.left)}px`; - } else { - panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; - } - } -} - -/* Highlighting */ -// highlighting functions -function highlightMatch(query, text) { - if (text) { - const start = text.toLowerCase().indexOf(query.toLowerCase()); - if (start !== -1) { - const startMark = ""; - const endMark = ""; - - const end = start + query.length; - text = - text.slice(0, start) + - startMark + - text.slice(start, end) + - endMark + - text.slice(end); - const startInfo = clipStart(text, start); - const endInfo = clipEnd( - text, - startInfo.position + startMark.length + endMark.length - ); - text = - startInfo.prefix + - text.slice(startInfo.position, endInfo.position) + - endInfo.suffix; - - return text; - } else { - return text; - } - } else { - return text; - } -} - -function clipStart(text, pos) { - const clipStart = pos - 50; - if (clipStart < 0) { - // This will just return the start of the string - return { - position: 0, - prefix: "", - }; - } else { - // We're clipping before the start of the string, walk backwards to the first space. - const spacePos = findSpace(text, pos, -1); - return { - position: spacePos.position, - prefix: "", - }; - } -} - -function clipEnd(text, pos) { - const clipEnd = pos + 200; - if (clipEnd > text.length) { - return { - position: text.length, - suffix: "", - }; - } else { - const spacePos = findSpace(text, clipEnd, 1); - return { - position: spacePos.position, - suffix: spacePos.clipped ? "…" : "", - }; - } -} - -function findSpace(text, start, step) { - let stepPos = start; - while (stepPos > -1 && stepPos < text.length) { - const char = text[stepPos]; - if (char === " " || char === "," || char === ":") { - return { - position: step === 1 ? stepPos : stepPos - step, - clipped: stepPos > 1 && stepPos < text.length, - }; - } - stepPos = stepPos + step; - } - - return { - position: stepPos - step, - clipped: false, - }; -} - -// removes highlighting as implemented by the mark tag -function clearHighlight(searchterm, el) { - const childNodes = el.childNodes; - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - if (node.nodeType === Node.ELEMENT_NODE) { - if ( - node.tagName === "MARK" && - node.innerText.toLowerCase() === searchterm.toLowerCase() - ) { - el.replaceChild(document.createTextNode(node.innerText), node); - } else { - clearHighlight(searchterm, node); - } - } - } -} - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -} - -// highlight matches -function highlight(term, el) { - const termRegex = new RegExp(term, "ig"); - const childNodes = el.childNodes; - - // walk back to front avoid mutating elements in front of us - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - - if (node.nodeType === Node.TEXT_NODE) { - // Search text nodes for text to highlight - const text = node.nodeValue; - - let startIndex = 0; - let matchIndex = text.search(termRegex); - if (matchIndex > -1) { - const markFragment = document.createDocumentFragment(); - while (matchIndex > -1) { - const prefix = text.slice(startIndex, matchIndex); - markFragment.appendChild(document.createTextNode(prefix)); - - const mark = document.createElement("mark"); - mark.appendChild( - document.createTextNode( - text.slice(matchIndex, matchIndex + term.length) - ) - ); - markFragment.appendChild(mark); - - startIndex = matchIndex + term.length; - matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); - if (matchIndex > -1) { - matchIndex = startIndex + matchIndex; - } - } - if (startIndex < text.length) { - markFragment.appendChild( - document.createTextNode(text.slice(startIndex, text.length)) - ); - } - - el.replaceChild(markFragment, node); - } - } else if (node.nodeType === Node.ELEMENT_NODE) { - // recurse through elements - highlight(term, node); - } - } -} - -/* Link Handling */ -// get the offset from this page for a given site root relative url -function offsetURL(url) { - var offset = getMeta("quarto:offset"); - return offset ? offset + url : url; -} - -// read a meta tag value -function getMeta(metaName) { - var metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; -} - -function algoliaSearch(query, limit, algoliaOptions) { - const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; - - const applicationId = algoliaOptions["application-id"]; - const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; - const indexName = algoliaOptions["index-name"]; - const indexFields = algoliaOptions["index-fields"]; - const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); - const searchParams = algoliaOptions["params"]; - const searchAnalytics = !!algoliaOptions["analytics-events"]; - - return getAlgoliaResults({ - searchClient, - queries: [ - { - indexName: indexName, - query, - params: { - hitsPerPage: limit, - clickAnalytics: searchAnalytics, - ...searchParams, - }, - }, - ], - transformResponse: (response) => { - if (!indexFields) { - return response.hits.map((hit) => { - return hit.map((item) => { - return { - ...item, - text: highlightMatch(query, item.text), - }; - }); - }); - } else { - const remappedHits = response.hits.map((hit) => { - return hit.map((item) => { - const newItem = { ...item }; - ["href", "section", "title", "text"].forEach((keyName) => { - const mappedName = indexFields[keyName]; - if ( - mappedName && - item[mappedName] !== undefined && - mappedName !== keyName - ) { - newItem[keyName] = item[mappedName]; - delete newItem[mappedName]; - } - }); - newItem.text = highlightMatch(query, newItem.text); - return newItem; - }); - }); - return remappedHits; - } - }, - }); -} - -function fuseSearch(query, fuse, fuseOptions) { - return fuse.search(query, fuseOptions).map((result) => { - const addParam = (url, name, value) => { - const anchorParts = url.split("#"); - const baseUrl = anchorParts[0]; - const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; - anchorParts[0] = baseUrl + sep + name + "=" + value; - return anchorParts.join("#"); - }; - - return { - title: result.item.title, - section: result.item.section, - href: addParam(result.item.href, kQueryArg, query), - text: highlightMatch(query, result.item.text), - }; - }); -} diff --git a/_site/styles.css b/_site/styles.css deleted file mode 100644 index 2ddf50c..0000000 --- a/_site/styles.css +++ /dev/null @@ -1 +0,0 @@ -/* css styles */ diff --git a/docs/data.html b/docs/data.html index cda1739..04b438d 100644 --- a/docs/data.html +++ b/docs/data.html @@ -543,7 +543,7 @@

Data

# A tibble: 1 × 1
   p_value
     <dbl>
-1   0.265
+1 0.268

Inference for a Difference in Means

diff --git a/docs/data_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png b/docs/data_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png index 0b74ad1..8e2a782 100644 Binary files a/docs/data_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png and b/docs/data_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png differ diff --git a/docs/data_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png b/docs/data_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png index c1cc2cb..b475abc 100644 Binary files a/docs/data_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png and b/docs/data_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png differ diff --git a/docs/data_files/figure-html/unnamed-chunk-2-1.png b/docs/data_files/figure-html/unnamed-chunk-2-1.png index f83b604..5bbdcdd 100644 Binary files a/docs/data_files/figure-html/unnamed-chunk-2-1.png and b/docs/data_files/figure-html/unnamed-chunk-2-1.png differ diff --git a/docs/data_files/figure-html/unnamed-chunk-2-2.png b/docs/data_files/figure-html/unnamed-chunk-2-2.png index 9ee6d63..5196263 100644 Binary files a/docs/data_files/figure-html/unnamed-chunk-2-2.png and b/docs/data_files/figure-html/unnamed-chunk-2-2.png differ diff --git a/docs/index.html b/docs/index.html index 263e637..851ad4e 100644 --- a/docs/index.html +++ b/docs/index.html @@ -160,10 +160,11 @@

On this page

  • Conclusion
  • -
  • Citations
  • @@ -495,7 +496,7 @@

    Multiple Linear Regression of relationship between percentage of officer residency/victim armament and number of fatal shootings per agency fit_multi

    The model equation for fit_multi considering victim armament (armed) is:

    \[ -\text{\ of Fatal Shootings (n.x)} = 4.117 + 1.211 \times \text{Percentage of Officer Residency (all)} + 24.921 \times \text{Armed (YES)} +\text{Number of Fatal Shootings (n.x)} = 4.117 + 1.211 \times \text{Percentage of Officer Residency (all)} + 24.921 \times \text{Armed (YES)} \]

    @@ -559,8 +560,8 @@

    \(4.117\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \(0\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \(1.211\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.

    -
  • The coefficient for ‘armedYES’, \(24.921\), indicates that the victim is armed (armed is YES), we expect an increase of \(24.921\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.

  • +
  • The intercept, \(4.117\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \(0\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \(1.211\) fatal shootings, assuming the victim’s armament status (armed:YES) remains constant.

  • +
  • The coefficient for ‘armedYES’, \(24.921\), indicates that the victim is armed (armed:YES), we expect an increase of \(24.921\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.

  • In summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.

    @@ -612,7 +613,7 @@

    \(\alpha = 0.05\), the p-value of \(0.248\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \(\mu_{maj} − \mu_{min}\)) is \(-4.92\) is around \(25\%\) (\(0.248\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.

    @@ -660,18 +661,28 @@

    Conclusion

    General Conclusions

    +

    This comprehensive case study delves into the complex interplay between police force in-city residence and fatal police shootings, utilizing a data-driven approach to unearth patterns and insights within the realm of law enforcement agencies. Focused on the residency status of police officers in the cities they serve, the study scrutinizes whether this factor is correlated with the occurrence of fatal police shootings. Covering the years 2015 to 2023 and incorporating information on police agencies involved in at least one fatal shooting, the research employs advanced statistical methods and machine learning techniques to provide a nuanced understanding of the phenomenon.

    -
    -

    Study Limitations

    +
    +

    Key Findings

    +

    The analysis reveals a negative association between the percentage of officer residency in a city and the number of fatal shootings, suggesting that, on average, a higher proportion of in-city officer residency is associated with fewer fatal police shootings. However, correlation is not equal to causation so we conducted a multiple linear regression. When controlling for victim armament, the study identifies a complex relationship. While an increase in the percentage of officer residency is associated with fewer fatal shootings, the presence of an armed victim is linked to a significant increase in fatal shootings.

    +

    Our hypothesis tests provide insights into the statistical significance of the observed relationships; a difference in mean number of shootings per agency in cities where a majority or minority of officers reside in the city and the nominal relationship (correlation) between an increasing proportion of in-city officer residency and number of fatal police shooting death. The first test suggests that there is insufficient evidence to reject the null hypothesis regarding the difference in mean total fatal shootings between agencies with a majority and minority of officers residing in the city. The second test indicates a significant relationship between the percentage of officer residency and the number of fatal shootings.

    -
    -

    Improvements for Future Study

    +
    +

    Limitations and Considerations

    +

    It’s essential to acknowledge the limitations of this study. The analysis relies on available data, and causation cannot be inferred from correlation. Factors beyond the scope of this study may influence fatal police shootings, emphasizing the need for a holistic approach to police reform.

    +
    +

    Policy Recommendations

    +

    The findings of this case study carry substantial implications for policy-making, police training, and community relations. The evidence suggests that fostering a higher percentage of officer residency in the communities they police may contribute to a reduction in fatal police shootings. However, it’s crucial to consider other influential factors, such as victim armament, when formulating comprehensive policies.

    +

    Implement measures to enhance transparency and accountability within law enforcement agencies, including the regular monitoring of fatal shooting incidents and the factors influencing them. Encourage community policing strategies that promote stronger bonds between officers and the communities they serve. Initiatives to increase the percentage of officers residing in their assigned cities may contribute to building trust. Prioritize training programs that focus on de-escalation techniques and handling situations involving armed individuals. This can mitigate the escalation of incidents and reduce the likelihood of fatal outcomes. Policy makers and researchers should also promote ongoing research and data collection to monitor trends and assess the effectiveness of implemented strategies. Continued analysis will help refine policies and ensure adaptability to evolving community needs.

    -
    -

    Citations

    +
    +

    Improvements for Future Study

    +

    In conclusion, this case study contributes to the ongoing discourse on police reform by providing evidence-based insights into the relationship between police residence and fatal police shootings. By addressing these critical issues, we aspire to foster safer, more resilient communities and facilitate positive transformations in law enforcement practices.

    +
    diff --git a/docs/index_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png b/docs/index_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png index 4d6ab71..fee8a7c 100644 Binary files a/docs/index_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png and b/docs/index_files/figure-html/Hypothesis Testing for Correlation between Total Fatal Shootings and Residency Prop-1.png differ diff --git a/docs/index_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png b/docs/index_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png index dc519c9..3f9f491 100644 Binary files a/docs/index_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png and b/docs/index_files/figure-html/Hypothesis Testing for Diff in Mean Total Fatal Shootings between Residency Prop-1.png differ diff --git a/docs/index_files/figure-html/unnamed-chunk-7-1.png b/docs/index_files/figure-html/unnamed-chunk-7-1.png index d1ca079..9ec93a7 100644 Binary files a/docs/index_files/figure-html/unnamed-chunk-7-1.png and b/docs/index_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/docs/index_files/figure-html/unnamed-chunk-9-1.png b/docs/index_files/figure-html/unnamed-chunk-9-1.png index f9bd315..21d5146 100644 Binary files a/docs/index_files/figure-html/unnamed-chunk-9-1.png and b/docs/index_files/figure-html/unnamed-chunk-9-1.png differ diff --git a/docs/search.json b/docs/search.json index 7eeba43..df3c628 100644 --- a/docs/search.json +++ b/docs/search.json @@ -53,14 +53,14 @@ "href": "index.html#results", "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", "section": "Results", - "text": "Results\n\nMultiple Linear Regression of relationship between percentage of officer residency and number of fatal shootings per agency fit\nThe model equation for fit is:\n\\[\n\\text{Number of Fatal Shootings (n)} = 35.7782 - 0.5874 \\times \\text{Percentage of Officer Residency (all)}\n\\]\n\n\nShow the code\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n\n\n\n\nMultiple Linear Regression of relationship between percentage of officer residency/victim armament and number of fatal shootings per agency fit_multi\nThe model equation for fit_multi considering victim armament (armed) is:\n\\[\n\\text{\\ of Fatal Shootings (n.x)} = 4.117 + 1.211 \\times \\text{Percentage of Officer Residency (all)} + 24.921 \\times \\text{Armed (YES)}\n\\]\n\n\nShow the code\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\n\n\n\nShow the code\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n\n\nShow the code\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.278\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \\(\\mu_{maj} − \\mu_{min}\\)) is \\(-4.92\\) is around \\(25\\%\\) (\\(0.248\\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.\n\n\nShow the code\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\n\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n\n\nShow the code\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is sufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that there is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (correlation coefficient; \\(\\rho = 0\\)) is \\(-0.0470\\) is around \\(0\\%\\) (\\(0\\)). Meaning our observed correlation coefficient likely would not happen if there was no relationship between percentage of officer residency and number of fatal shootings for a given agency." + "text": "Results\n\nMultiple Linear Regression of relationship between percentage of officer residency and number of fatal shootings per agency fit\nThe model equation for fit is:\n\\[\n\\text{Number of Fatal Shootings (n)} = 35.7782 - 0.5874 \\times \\text{Percentage of Officer Residency (all)}\n\\]\n\n\nShow the code\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n\n\n\n\nMultiple Linear Regression of relationship between percentage of officer residency/victim armament and number of fatal shootings per agency fit_multi\nThe model equation for fit_multi considering victim armament (armed) is:\n\\[\n\\text{Number of Fatal Shootings (n.x)} = 4.117 + 1.211 \\times \\text{Percentage of Officer Residency (all)} + 24.921 \\times \\text{Armed (YES)}\n\\]\n\n\nShow the code\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armed:YES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed:YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\n\n\n\nShow the code\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n\n\nShow the code\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.272\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \\(\\mu_{maj} − \\mu_{min}\\)) is \\(-4.92\\) is around \\(25\\%\\) (\\(0.248\\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.\n\n\nShow the code\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\n\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n\n\nShow the code\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is sufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that there is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (correlation coefficient; \\(\\rho = 0\\)) is \\(-0.0470\\) is around \\(0\\%\\) (\\(0\\)). Meaning our observed correlation coefficient likely would not happen if there was no relationship between percentage of officer residency and number of fatal shootings for a given agency." }, { "objectID": "index.html#conclusion", "href": "index.html#conclusion", "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", "section": "Conclusion", - "text": "Conclusion\n\nGeneral Conclusions\n\n\nStudy Limitations\n\n\nImprovements for Future Study" + "text": "Conclusion\n\nGeneral Conclusions\nThis comprehensive case study delves into the complex interplay between police force in-city residence and fatal police shootings, utilizing a data-driven approach to unearth patterns and insights within the realm of law enforcement agencies. Focused on the residency status of police officers in the cities they serve, the study scrutinizes whether this factor is correlated with the occurrence of fatal police shootings. Covering the years 2015 to 2023 and incorporating information on police agencies involved in at least one fatal shooting, the research employs advanced statistical methods and machine learning techniques to provide a nuanced understanding of the phenomenon.\n\n\nKey Findings\nThe analysis reveals a negative association between the percentage of officer residency in a city and the number of fatal shootings, suggesting that, on average, a higher proportion of in-city officer residency is associated with fewer fatal police shootings. However, correlation is not equal to causation so we conducted a multiple linear regression. When controlling for victim armament, the study identifies a complex relationship. While an increase in the percentage of officer residency is associated with fewer fatal shootings, the presence of an armed victim is linked to a significant increase in fatal shootings.\nOur hypothesis tests provide insights into the statistical significance of the observed relationships; a difference in mean number of shootings per agency in cities where a majority or minority of officers reside in the city and the nominal relationship (correlation) between an increasing proportion of in-city officer residency and number of fatal police shooting death. The first test suggests that there is insufficient evidence to reject the null hypothesis regarding the difference in mean total fatal shootings between agencies with a majority and minority of officers residing in the city. The second test indicates a significant relationship between the percentage of officer residency and the number of fatal shootings.\n\n\nLimitations and Considerations\nIt’s essential to acknowledge the limitations of this study. The analysis relies on available data, and causation cannot be inferred from correlation. Factors beyond the scope of this study may influence fatal police shootings, emphasizing the need for a holistic approach to police reform.\n\n\nPolicy Recommendations\nThe findings of this case study carry substantial implications for policy-making, police training, and community relations. The evidence suggests that fostering a higher percentage of officer residency in the communities they police may contribute to a reduction in fatal police shootings. However, it’s crucial to consider other influential factors, such as victim armament, when formulating comprehensive policies.\nImplement measures to enhance transparency and accountability within law enforcement agencies, including the regular monitoring of fatal shooting incidents and the factors influencing them. Encourage community policing strategies that promote stronger bonds between officers and the communities they serve. Initiatives to increase the percentage of officers residing in their assigned cities may contribute to building trust. Prioritize training programs that focus on de-escalation techniques and handling situations involving armed individuals. This can mitigate the escalation of incidents and reduce the likelihood of fatal outcomes. Policy makers and researchers should also promote ongoing research and data collection to monitor trends and assess the effectiveness of implemented strategies. Continued analysis will help refine policies and ensure adaptability to evolving community needs.\n\n\nImprovements for Future Study\nIn conclusion, this case study contributes to the ongoing discourse on police reform by providing evidence-based insights into the relationship between police residence and fatal police shootings. By addressing these critical issues, we aspire to foster safer, more resilient communities and facilitate positive transformations in law enforcement practices." }, { "objectID": "index.html#citations", @@ -95,6 +95,6 @@ "href": "data.html", "title": "Data", "section": "", - "text": "library(tidyverse)\nlibrary(usmap)\nlibrary(sf)\nlibrary(infer)\nlibrary(moderndive)\n\n\n##Tidying Data\n\n#creating dfs from .csv files\npolice_locals <- read_csv(\"data/police-locals.csv\")\nagencies <- read_csv(\"data/fatal-police-shootings-agencies.csv\")\nshootings <- read_csv(\"data/fatal-police-shootings-data.csv\")\n\n#removing old `city` tag from data set that we created when decatenated the city names\npolice_locals <- police_locals |>\n select(-city_old)\n\n# creating `agencies` df with just police departments\nagencies <- agencies |>\n filter(grepl(\"department\", tolower(name))) |>\n filter(!grepl(\"county\", tolower(name)))\n\n#creating binned categorical account of if shooting victim was `armed`\nshootings <- shootings |>\n mutate(armed = case_when(is.na(armed_with) ~ \"NO\",\n armed_with == \"unarmed\" ~ \"NO\",\n armed_with == \"unknown\" ~ \"NO\",\n armed_with == \"undetermined\" ~ \"NO\",\n armed_with == \"gun\" ~ \"YES\",\n armed_with == \"knife\" ~ \"YES\",\n armed_with == \"blunt_object\" ~ \"YES\",\n armed_with == \"other\" ~ \"YES\",\n armed_with == \"replica\" ~ \"YES\",\n armed_with == \"vehicle\" ~ \"YES\"))\n\n#creating df with only agency `names`, `id`, and `state`\nagencies_ids <- agencies |>\n select(name, id, state)\n\n#creating df with `city`, `agency`, and `state` info for each shooting\nshooting_agencies <- shootings |>\n select(city, agency_ids, state)\n\n#changing `shooting` var in `shooting_agencies` df to numeric\nshooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)\n\n#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`\nagencies_w_cities <- agencies_ids |>\n left_join(shooting_agencies, by = c(\"id\" = \"agency_ids\", \"state\" = \"state\")) |>\n drop_na(city) |>\n distinct(id, .keep_all = TRUE)\n\n#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`\nagencies_census <- agencies_w_cities |>\n full_join(police_locals, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n drop_na(police_force_size) |>\n distinct(id, .keep_all = TRUE) |>\n mutate(majority = if_else(all >= 0.5, \"TRUE\", \"FALSE\"))\n\n#creating df of only shootings involving agencies within `agencies` df\nshootings_case <- shootings |>\n right_join(agencies_census, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n select(-agency_ids) |>\n rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>\n select(-location_precision, -race_source)\n\n\n#count shootings by agency\nshootings_by_agency <- shootings_case |>\n count(agency)\n\n#find top 25 agencies with the most shootings\ntop_25_agencies <- shootings_by_agency |>\n slice_max(n, n = 25)\n\n# visulize top 25 agencies with the most shootings\nggplot(data = top_25_agencies,\n mapping = aes(x = agency, y = n)) +\n geom_col() +\n theme(axis.text.x = element_text(angle = 75,\n vjust = 1,\n hjust = 1,\n margin = margin(t = 5, b = 5)))\n\n\n\n\n\n#mapping Locations of Police-Involved Shootings between 2015 and 2023\n\n#load geo-viz libraries\nlibrary(ggmap)\nlibrary(maps)\nlibrary(mapdata)\n\n#create blank map\nusa <- map_data(\"usa\")\nstates <- map_data(\"state\")\n\n#add locations of shootings to maps\nshot_map <- ggplot(data = states) + \n geom_polygon(aes(x = long, y = lat, fill = group, group = group), color = \"white\") + \n coord_fixed(1.3) +\n guides(fill=FALSE) + # do this to leave off the color legend\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"black\", size = .2) +\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"red\", size = .1) +\n labs(title = \"Locations of Police-Involved Shootings between 2015 and 2023\",\n captions = \"This is only includes cities where we have agency census data.\",\n x = \"Longitude\",\n y = \"Latitude\")\n\n\n#creating df with total shootings per agency and census data\nagencies_census <- agencies_census |>\n left_join(shootings_by_agency, by = c(\"name\" = \"agency\"))\n\n#prelim visualization of relationship between percentage of officer residency and number of fatal shootings per agency\nagencies_census |>\n ggplot(mapping = aes(x = all, y = n)) +\n geom_point() +\n geom_smooth(method = \"lm\", se = TRUE)\n\n\n\n#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside\np0 <- shootings_case |>\n ggplot(aes(x = majority, fill = armed)) +\n geom_bar() + \n labs(title = \"Shootings in Cities where a Majority of Officers Reside\",\n caption = \"This is only includes shootings where we have agency census data.\",\n x = \"Does a majority a of the total police force live in the city?\",\n y = \"Number of fatal shootings\",\n fill = \"Victim Armed?\")\np0\n\n\n\n#calculate mean number of shootings per agency in cities where a majority of officers reside in the city\nmajority_mean <- shootings_case |>\n filter(majority == TRUE) |>\n count(agency) |>\n summarize(maj_mean = mean(n))\n\n#calculate mean number of shootings per agency in cities where a minority of officers reside in the city\nminority_mean <- shootings_case |>\n filter(majority == FALSE) |>\n count(agency) |>\n summarize(min_mean = mean(n))\n\n#calculate a difference in means between the `majority` and `minority`\ndiff_in_means <- majority_mean - minority_mean\ndiff_in_means\n\n maj_mean\n1 -2.575\n\n#tidy table\nknitr::kable(head(diff_in_means))\n\n\n\n\nmaj_mean\n\n\n\n\n-2.575\n\n\n\n\n\n\n#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency\nfit <- lm(n ~ all, data = agencies_census)\nfit\n\n\nCall:\nlm(formula = n ~ all, data = agencies_census)\n\nCoefficients:\n(Intercept) all \n 35.7782 -0.5874 \n\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n#add `armed` and `majority` to `shootings_by_agency` df\nshootings_by_agency_census <- shootings_case |>\n group_by(agency) |>\n count(armed) |>\n drop_na(n, armed) |>\n right_join(agencies_census, by = c(\"agency\" = \"name\")) |>\n distinct(armed, .keep_all = TRUE)\n\nshootings_by_agency_census <- shootings_by_agency_census |>\n select(n.x, armed, all) \n\n#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency\nfit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)\nfit_multi\n\n\nCall:\nlm(formula = n.x ~ all + armed, data = shootings_by_agency_census)\n\nCoefficients:\n(Intercept) all armedYES \n 4.117 1.211 24.921 \n\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\nThe model equation for fit is:\n[ = 35.7782 - 0.5874 ]\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\nThe model equation for fit_multi considering victim armament (armed) is:\n[ = 4.117 + 1.211 + 24.921 ]\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.265\n\n\nInference for a Difference in Means\n\n\\(H_0\\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.\n\\(H_A\\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.\n\n– \\(H_0 : \\mu_{maj} − \\mu_{min} = 0\\), or equivalently \\(H_0 : \\mu_{maj} = \\mu_{min}\\) – \\(H_A : \\mu_{maj} − \\mu_{min} < 0\\), or equivalently \\(H_A : \\mu_{maj} < \\mu_{min}\\)\n\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\ntest_stat_cor\n\nResponse: n (numeric)\nExplanatory: white (numeric)\n# A tibble: 1 × 1\n stat\n <dbl>\n1 -0.0470\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nInference for a Correlation\n\n\\(H_O\\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\\(H_A\\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\n\\(H_0 : \\rho = 0\\)\n\\(H_0 : \\rho \\neq 0\\)" + "text": "library(tidyverse)\nlibrary(usmap)\nlibrary(sf)\nlibrary(infer)\nlibrary(moderndive)\n\n\n##Tidying Data\n\n#creating dfs from .csv files\npolice_locals <- read_csv(\"data/police-locals.csv\")\nagencies <- read_csv(\"data/fatal-police-shootings-agencies.csv\")\nshootings <- read_csv(\"data/fatal-police-shootings-data.csv\")\n\n#removing old `city` tag from data set that we created when decatenated the city names\npolice_locals <- police_locals |>\n select(-city_old)\n\n# creating `agencies` df with just police departments\nagencies <- agencies |>\n filter(grepl(\"department\", tolower(name))) |>\n filter(!grepl(\"county\", tolower(name)))\n\n#creating binned categorical account of if shooting victim was `armed`\nshootings <- shootings |>\n mutate(armed = case_when(is.na(armed_with) ~ \"NO\",\n armed_with == \"unarmed\" ~ \"NO\",\n armed_with == \"unknown\" ~ \"NO\",\n armed_with == \"undetermined\" ~ \"NO\",\n armed_with == \"gun\" ~ \"YES\",\n armed_with == \"knife\" ~ \"YES\",\n armed_with == \"blunt_object\" ~ \"YES\",\n armed_with == \"other\" ~ \"YES\",\n armed_with == \"replica\" ~ \"YES\",\n armed_with == \"vehicle\" ~ \"YES\"))\n\n#creating df with only agency `names`, `id`, and `state`\nagencies_ids <- agencies |>\n select(name, id, state)\n\n#creating df with `city`, `agency`, and `state` info for each shooting\nshooting_agencies <- shootings |>\n select(city, agency_ids, state)\n\n#changing `shooting` var in `shooting_agencies` df to numeric\nshooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)\n\n#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`\nagencies_w_cities <- agencies_ids |>\n left_join(shooting_agencies, by = c(\"id\" = \"agency_ids\", \"state\" = \"state\")) |>\n drop_na(city) |>\n distinct(id, .keep_all = TRUE)\n\n#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`\nagencies_census <- agencies_w_cities |>\n full_join(police_locals, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n drop_na(police_force_size) |>\n distinct(id, .keep_all = TRUE) |>\n mutate(majority = if_else(all >= 0.5, \"TRUE\", \"FALSE\"))\n\n#creating df of only shootings involving agencies within `agencies` df\nshootings_case <- shootings |>\n right_join(agencies_census, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n select(-agency_ids) |>\n rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>\n select(-location_precision, -race_source)\n\n\n#count shootings by agency\nshootings_by_agency <- shootings_case |>\n count(agency)\n\n#find top 25 agencies with the most shootings\ntop_25_agencies <- shootings_by_agency |>\n slice_max(n, n = 25)\n\n# visulize top 25 agencies with the most shootings\nggplot(data = top_25_agencies,\n mapping = aes(x = agency, y = n)) +\n geom_col() +\n theme(axis.text.x = element_text(angle = 75,\n vjust = 1,\n hjust = 1,\n margin = margin(t = 5, b = 5)))\n\n\n\n\n\n#mapping Locations of Police-Involved Shootings between 2015 and 2023\n\n#load geo-viz libraries\nlibrary(ggmap)\nlibrary(maps)\nlibrary(mapdata)\n\n#create blank map\nusa <- map_data(\"usa\")\nstates <- map_data(\"state\")\n\n#add locations of shootings to maps\nshot_map <- ggplot(data = states) + \n geom_polygon(aes(x = long, y = lat, fill = group, group = group), color = \"white\") + \n coord_fixed(1.3) +\n guides(fill=FALSE) + # do this to leave off the color legend\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"black\", size = .2) +\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"red\", size = .1) +\n labs(title = \"Locations of Police-Involved Shootings between 2015 and 2023\",\n captions = \"This is only includes cities where we have agency census data.\",\n x = \"Longitude\",\n y = \"Latitude\")\n\n\n#creating df with total shootings per agency and census data\nagencies_census <- agencies_census |>\n left_join(shootings_by_agency, by = c(\"name\" = \"agency\"))\n\n#prelim visualization of relationship between percentage of officer residency and number of fatal shootings per agency\nagencies_census |>\n ggplot(mapping = aes(x = all, y = n)) +\n geom_point() +\n geom_smooth(method = \"lm\", se = TRUE)\n\n\n\n#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside\np0 <- shootings_case |>\n ggplot(aes(x = majority, fill = armed)) +\n geom_bar() + \n labs(title = \"Shootings in Cities where a Majority of Officers Reside\",\n caption = \"This is only includes shootings where we have agency census data.\",\n x = \"Does a majority a of the total police force live in the city?\",\n y = \"Number of fatal shootings\",\n fill = \"Victim Armed?\")\np0\n\n\n\n#calculate mean number of shootings per agency in cities where a majority of officers reside in the city\nmajority_mean <- shootings_case |>\n filter(majority == TRUE) |>\n count(agency) |>\n summarize(maj_mean = mean(n))\n\n#calculate mean number of shootings per agency in cities where a minority of officers reside in the city\nminority_mean <- shootings_case |>\n filter(majority == FALSE) |>\n count(agency) |>\n summarize(min_mean = mean(n))\n\n#calculate a difference in means between the `majority` and `minority`\ndiff_in_means <- majority_mean - minority_mean\ndiff_in_means\n\n maj_mean\n1 -2.575\n\n#tidy table\nknitr::kable(head(diff_in_means))\n\n\n\n\nmaj_mean\n\n\n\n\n-2.575\n\n\n\n\n\n\n#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency\nfit <- lm(n ~ all, data = agencies_census)\nfit\n\n\nCall:\nlm(formula = n ~ all, data = agencies_census)\n\nCoefficients:\n(Intercept) all \n 35.7782 -0.5874 \n\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n#add `armed` and `majority` to `shootings_by_agency` df\nshootings_by_agency_census <- shootings_case |>\n group_by(agency) |>\n count(armed) |>\n drop_na(n, armed) |>\n right_join(agencies_census, by = c(\"agency\" = \"name\")) |>\n distinct(armed, .keep_all = TRUE)\n\nshootings_by_agency_census <- shootings_by_agency_census |>\n select(n.x, armed, all) \n\n#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency\nfit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)\nfit_multi\n\n\nCall:\nlm(formula = n.x ~ all + armed, data = shootings_by_agency_census)\n\nCoefficients:\n(Intercept) all armedYES \n 4.117 1.211 24.921 \n\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\nThe model equation for fit is:\n[ = 35.7782 - 0.5874 ]\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\nThe model equation for fit_multi considering victim armament (armed) is:\n[ = 4.117 + 1.211 + 24.921 ]\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.268\n\n\nInference for a Difference in Means\n\n\\(H_0\\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.\n\\(H_A\\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.\n\n– \\(H_0 : \\mu_{maj} − \\mu_{min} = 0\\), or equivalently \\(H_0 : \\mu_{maj} = \\mu_{min}\\) – \\(H_A : \\mu_{maj} − \\mu_{min} < 0\\), or equivalently \\(H_A : \\mu_{maj} < \\mu_{min}\\)\n\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\ntest_stat_cor\n\nResponse: n (numeric)\nExplanatory: white (numeric)\n# A tibble: 1 × 1\n stat\n <dbl>\n1 -0.0470\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nInference for a Correlation\n\n\\(H_O\\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\\(H_A\\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\n\\(H_0 : \\rho = 0\\)\n\\(H_0 : \\rho \\neq 0\\)" } ] \ No newline at end of file