Statistical hypothesis tests and predictive modeling for the CA DOJ's OpenJustice project.
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OpenJustice Exploration and Hypothesis Testing for Prompt 2.ipynb
README.md
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README.md

CA Dept. of Justice OpenJustice Project

Modeling & Hypothesis Testing

Below, members of the Data Science Working Group have been charged with answering, via inferential statistics, some of the California Department of Justice's inquiries around criminal justice. These more pointed inquiries were inspired by the OpenJustice project's exploratory analyses at OpenJustice.org.

Responsible DSWG Members:

  • Catherine Zhang
  • Matthew Mollison, Ph.D.
  • John Huynh
  • Saniya Jesupaul
  • Brian Smith
  • Biljana Rolih, Ph.D.
  • Jude Calvillo
  • Alex Novet
  • Nitya Subramanian

Status, as of January 25, 2017:

  • We were recently informed of some very interesting data coming down the pipeline (gun sales and justice department transactions, post-arrest), and the team has already formulated some great hypotheses to test upon that data.
  • We will be having another call with OpenJustice's head of research, Eric Giannella, Ph.D., in the coming weeks, to see how realistic it would be to execute our latest batch of hypothesis tests, given state regulations/policy, as well as to get his insights into the sociological issues involved.

Past Prompts (new prompts coming)

  1. Which counties/agencies arrest African American juveniles at a statistically significantly higher rate than that of other counties/agencies?

    • Extending analysis to each ethnic group represented
    • Drilling down to felonies vs. misdemeanors
  2. For the same criminal offense, are particular ethnic juvenile groups more likely to be treated with harsher consequences by law enforcement?

  3. Statewide, what contextual and ethnic factors best predict the arrest of juveniles for felonies?

  4. Statewide, what contextual and ethnic factors best predict the arrest of juveniles for battery, specifically?

  5. For resource allocation prompt: ~. predictors to statewide crime rate (i.e. not necessarily optimization; just a first, exploratory step, probably via LM)