Policing

Big Data Policing Dilemma

This is the era of big data policing, to a large extent, aimed at determining where to allocate police presence. Predictive policing technology uses algorithms to pore over massive amounts of data to predict when and where future crimes will occur. These algorithms can guesstimate the times and locations of crimes, the potential perpetrators, and even their upcoming victims based on a variety of risk factors. 

For example, if the system recognizes a pattern of physical altercations outside a bar every Saturday at 2am, it could suggest increasing police presence there at that time to prevent the fights from occurring. 

Used by police across the United States for almost a decade, predictive policing relies on algorithms to interpret police records, analyzing arrest or parole data to send officers to target chronic offenders, or identifying places where crime may occur.

California’s Santa Cruz has become the first U.S. city to ban predictive policing. Critics says it reinforces racist patterns of policing – low-income, ethnic minority neighbourhoods have historically been overpoliced so the data shows them as crime hotspots, leading to the deployment of more police to those areas, according to international news agencies.

Predictive policing is nothing new, but currently, law enforcement agencies and the private companies who develop predictive algorithms utilize cutting-edge, computer driven models that can tap into massive stores of data and information. 

Predictive policing can be based on patterns regarding places, persons, or groups.