An algorithmic strategy for measuring police presence with GPS data.

Area of Science:

• Criminology
• Geographic Information Systems (GIS)
• Data Science

Background:

• Traditional policing strategies often lack precise measurement of police presence at microgeographic levels.
• Understanding the spatial and temporal dynamics of police deployment is crucial for effective crime prevention.
• Existing theories, like the integrated theory of hot spots patrol, require refined quantitative measures.

Purpose of the Study:

• To develop and present an algorithmic strategy for quantifying police presence using GPS data.
• To measure three key dimensions of police presence: frequency, duration, and intermittency.
• To provide a framework for analyzing the impact of police presence on crime and related outcomes.

Main Methods:

• Utilized GPS data from police patrol units.
• Developed an algorithm with pseudocodes for data pre-processing.
• Focused on calculating measures for frequency, duration, and intermittency of police presence.
• Applied the algorithm to microgeographic units.

Main Results:

• Successfully generated quantifiable measures for frequency, duration, and intermittency of police presence.
• Provided pseudocodes for an algorithm to process GPS data for these measures.
• Established a methodological framework for microgeographic analysis of police deployment.

Conclusions:

• The proposed algorithmic strategy offers a novel method for measuring police presence using GPS data.
• This framework can facilitate empirical investigations into the relationship between police presence and crime at microgeographic levels.
• The approach supports the development of evidence-based, place-based policing strategies.