Page 12 - Case study: Crime prediction for more agile policing in cities – Rio de Janeiro, Brazil

• Fairness – Ensure that algorithmic predictions do not create discriminatory or unjust impacts
when comparing across different demographics.

• Auditability – Enable interested third parties to probe, understand, and review the behavior of
the algorithm through disclosure of information that enables monitoring, checking, or criticism,
including through provision of detailed documentation, technically suitable APIs, and permissive
terms of use.

CrimeRadar was featured in the following prominent publications (see figure 3):

Figure 3: CrimeRadar featured in several prominent publications

CrimeRadar has had the following impacts:

• It has helped Rio de Janeiro residents and visitors by providing a simple visual heatmap tool
which displays various city locations and their likelihood for crime.

• It has incorporated new crime data into Rio de Janeiro’s public security system as it becomes
available.

• The city residents’ quality of life has been enhanced by better safety and security.

• Increased safety has increased economic activities in Rio de Janeiro and will likely to continue to
be the case in the long run.

• It has helped in deploying police force more effectively to avoid crime by predicting it before it
happens.

• It has created operational efficiencies for the police force by reducing and optimizing various
costs such as patrolling, focused deployment of police force, etc.

• Reduced crime rate has improved the image of Rio de Janeiro as a highly visited global city.

• Reduced patrolling by police vehicles has also reduced CO2 emissions.

6 Crime prediction for more agile policing in cities – Rio de Janeiro, Brazil