README.md

Divvy Data Challenge 2013 Toolkit

Updates

• 21 Feb, 2014: Updated everything for revised Divvy Data.
• 16 Feb, 2014: Initial Upload.

Background

Developed by Christopher Baker in collaboration with the openLab at the School of the Art Institute of Chicago.

This toolkit consists of a collection of examples, preprocessors and tools to support work on the http://divvybikes.com/datachallenge data set.

The goal of this toolkit is to provide a basic foundation for building data visualizations using the 2013 Divvy Bike data.

For questions, leave an issue here or join the conversation @ https://talk.olab.io.

Toolkit Components

Raw Data

The raw data is included in this repository in the data folder. Please see the raw data README.md for a discussion of some current issues with the data.

Data Preprocessor

The DataPreprocessor is a Processing sketch that makes it easy to pre-process the raw Divvy data. See the README.md for more information.

DIY API (Do-It-Yourself-Application-Programming-Interface)

The DIY API is a way to let your visualization project search the Divvy bike data using simple search parameters. Here are a few examples:

Select the first page of 25 results for for trips between 2013-06-01 and 2013-07-01 for males over the age of 50:

• http://data.olab.io/divvy/api.php?start_min=2013-06-01&start_max=2013-07-01&gender=male&age_min=50&page=0&rpp=25

To get results 26 - 50 from the same query:

• http://data.olab.io/divvy/api.php?start_min=2013-06-01&start_max=2013-07-01&gender=male&age_min=50&page=1&rpp=25

To get all trips taken by 33 year old females:

• http://data.olab.io/divvy/api.php?gender=female&age=33

See the README.md for DIY API installation instructions and query parameter documentation.

Examples

Descriptions coming soon. See code comments for the moment.

Related

• Divvy Data Document
  • Explains the fields, etc.
• Divvy Station Distance Tables
  • Supplementary distance data.
• http://chicagocrashes.org/
  • A map that examines pedestrian / cyclist vs. auto crashes in chicago.
• https://bikesharingdata.hackpad.com/
  • A lot of bike-data resources collected by @stevevance.