How data and legal aid can fight property crime

HAL24K was asked to help analyze and model data for a US-based civil legal aid organization, to examine what factors impact on rates of property crime. The results our work uncovered were surprising.

Open Door Legal is an award-winning, nonprofit based in San Francisco, which provides access to civil legal aid for low-income residents. It is also committed to gathering data and HAL24K offered its data science expertise to investigate what influence the availability of civil legal aid might have on rates of property crime.

Our data-science team led by Chief Science Officer Peter den Hartog first created a model to predict property crime (such as larceny, burglary, motor vehicle theft) using 26 parameters, including poverty rate, education level, household income and the unemployment rate.

It also featured three tools commonly used to prevent property crime: the availability of civil legal aid, attorneys and the police.

With the 26 parameters ranked in order of importance, the model revealed civil legal aid ranked 4th, while general attorneys ranked 10th and the police force ranked only 17th. Therefore, of the three ‘preventative tools’ which can be used to fight property crime civil legal aid is most likely to be the most effective.

While HAL24K’s model does not answer how legal aid affects the fight against property crime, the underlying implication is that civil legal aid has a significant role to play beyond providing access to justice. It can contribute to reducing overall property crime and supports Open Door Legal’s claim that universal access to representation merits greater investment.

Data science can uncover the unexpected and create new ways to look and solve some of the oldest and toughest challenges we face.