OakCrime

Code supporting citizen analysis of crime in Oakland, CA

Primary bits involve:

• importing data from Oakland Police Department (OPD), an early data set from Urban Strategies Council, and Alameda County

• crimeCat: software defining and building an ontology of crime types useful with OPD data and perhaps beyond!

• showCrime, a django site for visualization of historical crime data

• stopData, visualization of discretionary stop data

Prequisites

We assume you already have these installed:

• Docker Compose v1.x
• Docker v18.x
• Make
• Postgres v10.x
• Python 3.6+

MacOS users: you'll find most of these tools in Homebrew.

Setup

The main project is the django application. Please follow the instructions in showCrime/README.

Continuous integration

We're using CircleCI for continuous integration (CI). Continuous integration automatically tests that any new changes work correctly before they are fully integrated. This provides a faster feedback loop and helps prevent bugs or mistakes from getting caught late in development.

Continuous delivery

We use continuous delivery (CD) to automatically deploy our application. This reduces the risk of human error and makes sure the latest version of the application is deployed correctly.

Any commits to the master branch are deployed automatically to AWS Elastic Beanstalk courtesy of Open Oakland.

Contributing

Thank you for considering a contribution to our project! Please see CONTRIBUTING.md on how the OakCrime team works and how you can contribute.