Sync PCOR_public staging branch

chapters/01-01-getting-started.Rmd

@@ -102,12 +102,12 @@ library(utils)
 
 ## Resources {#chapter-getting-started-spatial-resources}
 
-This section highlights resources for getting started with `R`, geospatial data analysis, and/or climate change and human health related research methods.
+This section highlights resources for getting started with `R`, geospatial data analysis, and/or environmental health related research methods.
 
-- The BUSPH-HSPH Climate Change and Health Research Coordinating Center ([CAFÉ](https://climatehealthcafe.org)) provides training and education materials for climate change and human health research in different formats for various types of users. The [Climate CAFÉ Tutorials and Code Walkthroughs](https://climate-cafe.github.io/cafe_github_org.html) demonstrate geospatial data management and analysis in climate change and human health research using `R`. CAFÉ also provides a series of [video tutorials](https://climatehealthcafe.org/training) demonstrating the use of geographic information systems (GIS) in environmental health and a list of [educational materials on climate and health](https://climatehealthcafe.org/training).
+- The BUSPH-HSPH Research Coordinating Center ([CAFÉ](https://climatehealthcafe.org)) provides training and education materials for environmental health research in different formats for various types of users. The [CAFÉ Tutorials and Code Walkthroughs](https://climate-cafe.github.io/cafe_github_org.html) demonstrate geospatial data management and analysis using `R`. CAFÉ also provides a series of [video tutorials](https://climatehealthcafe.org/training) demonstrating the use of geographic information systems (GIS) in environmental health and a list of [educational materials](https://climatehealthcafe.org/training).
 
 - The [inTelligence And Machine lEarning (TAME) Toolkit](https://uncsrp.github.io/Data-Analysis-Training-Modules/) provides tutorials for data generation, management, and analysis in environmental health research using `R`. The TAME Toolkit [Chapter 1](https://uncsrp.github.io/Data-Analysis-Training-Modules/introduction-to-coding-in-r.html#introduction-to-coding-in-r) includes a guide for installing and getting started with `R` and an introduction to data science methods in `R`. The TAME Toolkit also includes tutorials with `R` code demonstrating geospatial data analysis methods in environmental health (e.g., [Chapter 3.3](https://uncsrp.github.io/Data-Analysis-Training-Modules/database-integration-air-quality-mortality-and-environmental-justice-data.html#database-integration-air-quality-mortality-and-environmental-justice-data)).
 
-- The [IPUMS DHS Climate Change and Health Research Hub](https://tech.popdata.org/dhs-research-hub/) provides tutorials with code in `R` demonstrating use of various climate and health datasets and analysis methods. IPUMS also provides a guide to [installing and setting up `R`](https://tech.popdata.org/dhs-research-hub/posts/2024-02-01-getting-started-with-r/) for use in climate change and health research.
+- The [IPUMS Research Hub](https://tech.popdata.org/dhs-research-hub/) provides tutorials with code in `R` demonstrating use of various environment and health datasets and analysis methods. IPUMS also provides a guide to [installing and setting up `R`](https://tech.popdata.org/dhs-research-hub/posts/2024-02-01-getting-started-with-r/) for use in environmental health research.
 
 - The book [*Geocomputation with R*](https://r.geocompx.org/) provides resources for geospatial data analysis, visualization, and modeling with `R`. This book provides tutorials and examples from various disciplines that use geospatial data (e.g., transportation, ecology). This book covers introductory through advanced topics.

chapters/02-00-wildfire-data.Rmd

@@ -4,6 +4,6 @@
 
 ---
 
-This unit provides guidance and code for working with different types of wildfire-related data in climate change and health research. 
+This unit provides guidance for working with different types of wildfire-related data in environmental health and disaster research. 
 
 _Please note that the CHORDS Toolkit is a work in progress. This unit is currently in development._

chapters/04-00-health-data-integration.Rmd

@@ -4,4 +4,4 @@
 
 ---
 
-This unit provides guidance and code for integrating environmental and health data in climate change and health research, both at the individual level and at the population level.
+This unit provides guidance and code for integrating environmental and health data, both at the individual level and at the population level.

chapters/04-01-link-to-census.Rmd

@@ -27,16 +27,15 @@ This tutorial provides example code in `R`:
 
 Linking geocoded addresses to US Census geographic units is a common step in environmental health data integration workflows. First, using geographic information systems (GIS) software, the geocoded addresses are mapped to the specific Census geographic units (e.g., Census tracts) in which they are located. Second, the Census geographic unit identifying code (or, [geoID](https://www.census.gov/programs-surveys/geography/guidance/geo-identifiers.html)) is matched to each geocoded address. The result is a table of geocoded addresses and their corresponding Census geoIDs.
 
-The Census geoIDs can then serve as the basis for linking additional data to each geocoded address. Many types of data with importance for environmental health applications are available by Census geoID. Specifically, the Census collects and provides data by Census geoID for various social determinants of health (SDOH). Such Census SDOH data describe poverty, race/ethnicity, language, housing, and other socioeconomic characteristics. Increasingly, other data providers (e.g., other government agencies, research institutions, community science groups) are making their data available by Census geoID to help facilitate linkages with SDOH data. Such data cover various environment, climate, health, and built environment characteristics.
+The Census geoIDs can then serve as the basis for linking additional data to each geocoded address. Many types of data with importance for environmental health applications are available by Census geoID. Specifically, the Census collects and provides data by Census geoID for various social determinants of health (SDOH). Such Census SDOH data describe poverty, race/ethnicity, language, housing, and other socioeconomic characteristics. Increasingly, other data providers (e.g., other government agencies, research institutions, community science groups) are making their data available by Census geoID to help facilitate linkages with SDOH data. Such data cover various environment, disaster-related, health, and built environment characteristics.
 
 The following table lists example environmental health data sources readily available by Census geoID from US federal agencies.
 
 <figcaption>Example Environmental Health Data Sources</figcaption>
 
 | Data source                                                                                           | Geographic units        | Example topics                                                                                                   |
 |-----------------------|-------------------------|------------------------|
-| [AHRQ Social Detrminants of Health Database](https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html) | County, ZIP code, tract | Demographic characteristics, housing and transportation characteristics, food access, healthcare characteristics |
-| [EPA Environmental Justice Screening and Mapping Tool](https://www.epa.gov/ejscreen)                  | Block group             | Air pollution, hazardous waste, flood risk, wildfire risk, environmental justice indices                         |
+| [AHRQ Social Determinants of Health Database](https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html) | County, ZIP code, tract | Demographic characteristics, housing and transportation characteristics, food access, healthcare characteristics |                      |
 | [CDC National Environmental Public Health Tracking Network](https://ephtracking.cdc.gov)              | State, county           | Heat, sunlight and ultraviolet exposure, built environment characteristics, asthma, heat-related illnesses       |
 
 ### Background {#intro-census-geoids}
@@ -46,7 +45,7 @@ The Census defines [geographic boundaries](https://www.census.gov/newsroom/blogs
 The Census defines the boundaries of tracts and block groups to scale with local population: that is, in areas with higher population density (e.g., urban cores), these geographic units have finer spatial scale (i.e., smaller land area per unit) and in areas with lower population density (e.g., rural areas), the geographic units have coarser spatial scale (i.e., larger land area per unit). As a result, the spatial scale of geographic units varies substantially across the US. The shape of geographic units also varies substantially: the Census defines these boundaries to follow political boundaries (e.g., state boundaries) as well as physical features (e.g., roads, rivers), which often have irregular shapes.
 
 ::: {.note}
-The variable shape and spatial scale of Census geographic units is in contrast with spatial grids-- in which each geographic unit (i.e., grid cell) has the same shape and spatial resolution. Such spatial grids are common for environment and climate data.
+The variable shape and spatial scale of Census geographic units is in contrast with spatial grids-- in which each geographic unit (i.e., grid cell) has the same shape and spatial resolution. Such spatial grids are common for environmental data.
 :::
 
 [ZIP Code Tabulation Areas (ZCTAs)](https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html), which represent the geographic areas used by the US Postal Service for ZIP codes, are another common boundary used in environmental health workflows. ZCTAs have no spatial relationship with block groups, tracts, counties, or states: that is, ZCTAs can cross or overlap those other geographic boundaries. Like other Census geographic boundaries, ZCTAs vary in spatial resolution and shape across the US. The spatial scale of ZCTAs is, on average, finer than counties but coarser than tracts.
@@ -305,7 +304,7 @@ sdoh_tracts_2010_xlsx <-
   "/ YOUR FILE PATH /sdoh_2010_tract_1_0.xlsx"
 
 # read the "Data" sheet of the Excel file into a table R
-# note that you may need to provide a complete filepath for the Excel file
+# note that you may need to provide a complete file path for the Excel file
 sdoh_tracts_2010_tbl <- readxl::read_xlsx(sdoh_tracts_2010_xlsx,
                                           sheet = "Data")
 ```
@@ -322,7 +321,7 @@ sdoh_tracts_2010_xlsx <-
   "dataset/sdoh_2010_tract_1_0.xlsx"
 
 # read the "Data" sheet of the Excel file into a table R
-# note that you may need to provide a complete filepath for the Excel file
+# note that you may need to provide a complete file path for the Excel file
 sdoh_tracts_2010_tbl <- readxl::read_xlsx(sdoh_tracts_2010_xlsx,
                                           sheet = "Data")
 ```

chapters/AA-02-data-science-dictionary.Rmd

@@ -1,6 +1,6 @@
 # **B** Glossary {#chords-glossary .unnumbered}
 
-Key terms used in the CHORDS Toolkit are defined below and in the [NIEHS Climate Change and Human Health Glossary](https://tools.niehs.nih.gov/cchhglossary/).
+Key terms used in the CHORDS Toolkit are defined below and in the [NIEHS Glossary](https://tools.niehs.nih.gov/cchhglossary/).
 
 ## Index {.unnumbered .unlisted}
 

index.Rmd

@@ -7,7 +7,7 @@ bibliography: ["bibliography/book.bib", "bibliography/packages.bib"]
 csl: "bibliography/chicago-numeric-with-note.csl"
 link-citations: true
 link-bibliography: true
-description: The CHORDS Training and Use Cases Toolkit provides guides, tutorials, and example code to support climate change and human health research.
+description: The CHORDS Training and Use Cases Toolkit provides guides, tutorials, and example code to support environmental health and disaster research.
 github-repo: NIEHS/PCOR_bookdown_tools
 output: bookdown::html_document2
 ---
@@ -16,11 +16,11 @@ output: bookdown::html_document2
 
 ---
 
-Researchers interested in studying the health impacts of climate change and climate related disasters need access to relevant, timely, and harmonized data on environmental exposures, social determinants of health variables, and health outcomes. However, such data sets are often developed for different purposes, reside in multiple locations, and require linkage. The Climate and Health Outcomes Research Data Systems (CHORDS) Project seeks to connect researchers to environmental and health data sets with a toolkit that provides guides, tutorials, and example code to improve integration of geospatial data-based exposures and health data and records into their research. 
+Researchers interested in studying the health impacts of extreme weather events and related disasters need access to relevant, timely, and harmonized data on environmental exposures, social determinants of health variables, and health outcomes. However, such data sets are often developed for different purposes, reside in multiple locations, and require linkage. The Connecting Health Outcomes and Research Data Systems (CHORDS) Project seeks to connect researchers to environmental and health data sets with a toolkit that provides guides, tutorials, and example code to improve integration of geospatial data-based exposures and health data and records into their research. 
 
 ## About CHORDS {-}
 
-The [Climate and Health Outcomes Research Data Systems (CHORDS)](https://www.niehs.nih.gov/research/programs/chords) program provides resources aimed at making it easier for researchers to study the effects of place-based environmental exposures on health outcomes. The CHORDS resources include a web-based data catalog, standardized data sets, and this toolkit.
+The [Connecting Health Outcomes Research and Data Systems (CHORDS)](https://www.niehs.nih.gov/research/programs/chords) program provides resources aimed at making it easier for researchers to study the effects of place-based environmental exposures on health outcomes. The CHORDS resources include a web-based data catalog, standardized data sets, and this toolkit.
 
 ::: {.figure}
   <img src="images/chords-art-logo.png" style="width:100%">
@@ -38,11 +38,11 @@ The toolkit consists of a series of chapters organized into the following units:
 
 - **Geospatial Data Foundations**: This unit provides background, guidance and example code for working with different types of geospatial data common in environmental health research. This unit is intended as a starting point for users with less familiarity with geospatial data and geospatial analysis methods in environmental health using `R`.
 
-- **Wildfire Data**: This unit provides guidance for working with different types of wildfire-related data in climate change and health research.
+- **Wildfire Data**: This unit provides guidance for working with different types of wildfire-related data in environmental health and disaster research.
 
 - **Other Environmental Data**: This unit provides guidance and code for working with specific sources of environmental data common in environmental health research for characterizing environmental exposures as well as social determinants of health.
 
-- **Health Data Integration**: This unit provides guidance and code for integrating environmental and health data in climate change and health research, both at the individual level and at the population level.
+- **Health Data Integration**: This unit provides guidance and code for integrating environmental and health data, both at the individual level and at the population level.
 
 - **Use Cases**: This unit provides example use cases that analyze integrated wildfire-related data and other environmental exposures data with health outcomes data.