The Center's long term 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 more than by data analysis. Few arenas make this clearer than that of crime. 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.
For the future, large-scale data analysis has the potential to reveal patterns that will help to navigate pre-sentencing decisions. Using literally millions of criminal records from multiple states, our subgroup on Criminal Policy Informatics mines patterns of crime and recidivism to help navigate a more effective criminal justice policy.
By analyzing these large datasets, the Center explores several questions: 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?
Public crime is public record. But it took a team of us over a year to acquire the data, convert it to useful formats, identify typos, clerical errors, and duplicates, classify the offenses, and calculate the summary information. We are now crowdsourcing this data. After obtaining, collating, and categorizing the data, we are opening this up for everyone to help in detecting and understanding the patterns.
The records we have available online at the moment include: