Taking 311 Open data and vizualizing it in a leaderboard.
http://rawgit.com/MrNickBreen/311Vis/master/scrap/index.html
HTML Leaderboard
- cd into scraps folder
- python -m SimpleHTTPServer
Python script to make CSV training data set for BigML
- cd into analysis folder
- pip install python-dateutil
- pip install pytz
- python main.py
- (notice that train_sample311.csv is created)
- see all entries, but keep filter of min size:
drawChart(filteredList, filteredList.length) - see all entries:
drawChart(comparisonList, comparisonList.length) - adjust timespan to 16 days:
start(16);
https://data.edmonton.ca/Indicators/311-Explorer/ukww-xkmj edmenont data: https://data.edmonton.ca/resource/ukww-xkmj.json
- SF Data: https://data.sfgov.org/City-Infrastructure/Case-Data-from-San-Francisco-311-SF311-/vw6y-z8j6
- Sample API call: https://data.sfgov.org/resource/vw6y-z8j6.json?$limit=2000
- Data Sample in Sheet: https://docs.google.com/spreadsheets/d/1yGd2B7F8mlDg64L-uL3Byuxfxn1irl4ZLd0vETaq2GU/edit#gid=1562030977
- Socrata API docs: https://dev.socrata.com/consumers/getting-started.html
- leadboard for the week of closed tickets by department (by percentage or sorted by change from prior week)
- After looking at the data, suggested we filter to agencies with >40 tickets this week.
- leaderboard of Neighbourhood with most open tickets (by %)
- 'Achievments / badges' for fastest responding department, best close rate, most engaged neighbourhood that reports the most, etc...
- Use BigML to predict chance of a ticket being closed, and the biggest factors.
- colour code neighbourhoods with open ticks (coloropleth)
- open tickets preportional to income
- colour code census tracts (same number of people per area)
- Places for 311: https://opendata.socrata.com/browse?q=311&sortBy=relevance&utf8=%E2%9C%93
- SFHip-map repo
- Neighbourhood geojson
- cenus tract geojson
- r script to agregate csv point data