Using Big Data to understand patterns of crime

Our goal is to steer social policy in an evidence-based manner, thereby reducing rates of incarceration and providing novel options for dealing with criminal offenders.

In the past, legal policy has often been driven by intuition and politics rather than by data analysis. Because of the social cost and stigma of certain types of crimes, sentencing and policy changes are often navigated by emotional response rather than tailored to the person and the circumstances.

Using literally millions of criminal records from multiple states, our subgroup on Criminal Policy Informatics mines patterns of crime and recidivism using the SciLaw Criminal records Database to help navigate a more effective criminal justice policy. By analyzing these large datasets, the we can explores quesitons such as:

  • Which policies over the past few decades have effectively reduced crime?
  • Which types of crime respond to which types of policies?
  • Are there “gateway crimes” that lead offenders to commit other crimes in the future?
  • What patterns correlate with re-offense?
  • Which crime types cluster, and which are rarely performed by the same individual?
  • When does sentencing effectively prevent offenders from reoffending?
  • What is the link between childhood or prenatal brain development and crime?

We offer expert data analytics

For organizations seeking analytic support, our team of data scientistscan mine the answers to your questions.  We currently work with organizations of all sizes — from not-for-profits to state governments — to analyze the tens of millions of records in the Criminal Records Database.  We give detailed reports regarding the effects of legislation, policy, race, gender, sentencing, and much more.  If you are an academic, please contact us for a discounted rate.


Click on any city below to explore the data



1977-2013 (6.1 million records)

New Mexico:

1984-2018 (5.1 million records)