Predictive Hotspot Mapping for Data-driven Crime Prediction

TL;DR

A non-parametric model using a spatio-temporal kernel density formulation for the purpose of crime prediction based on historical data that is able to incorporate expert inputs coming from humans through alternate sources is created.

Abstract

Predictive hotspot mapping is an important problem in crime prediction and control. An accurate hotspot mapping helps in appropriately targeting the available resources to manage crime in cities. With an aim to make data-driven decisions and automate policing and patrolling operations, police departments across the world are moving towards predictive approaches relying on historical data. In this paper, we create a non-parametric model using a spatio-temporal kernel density formulation for the purpose of crime prediction based on historical data. The proposed approach is also able to incorporate expert inputs coming from humans through alternate sources. The approach has been extensively evaluated in a real-world setting by collaborating with the Delhi police department to make crime predictions that would help in effective assignment of patrol vehicles to control street crime. The results obtained in the paper are promising and can be easily applied in other settings. We release the algorithm and the dataset (masked) used in our study to support future research that will be useful in achieving further improvements.

Figures (15)

• Figure 1: Preliminary predictive approaches to predict street crime locations in Delhi during '8 pm to 12 am' for the week of 21 March 2021 (The grey shaded area is the approximate map of Delhi).
• Figure 2: Overall average AUC over the 25 weeks [week of 4 October 2020 up to week of March 21 2021]
• Figure 3: AUC by week [week of 4 October 2020 up to week of March 21 2021] for different time intervals in the day based on different models
• Figure 4: Predicted hotspot maps for two successive time intervals for the week of 21 March 2021, based on Model 5. Red marks the top 20% likely spots and and yellow the next 20% likely spots. Part (c) highlights differences between Parts (a) and (b): blue indicates spots that were neither red nor yellow in Part (a) but are marked as red or yellow in Part (b), green indicates spots that were red or yellow in Part (a) but are neither red nor yellow in Part (b). There are 1.4% green and 1.4% blue dots in Part (c).
• Figure 5: Predicted hotspot maps for two successive weeks, for the week of 14 March 2021 and the week of 21 March 2021, based on the proposed Model 5. Red marks the top 20% likely spots and yellow the next 20% likely spots. Part (b) highlights some spots in relation to Part (a): blue indicates spots that were neither red nor yellow in Part (a) but should be marked as red or yellow in Part (b), green indicates spots that were red or yellow in Part (a) but should neither be red nor yellow in Part (b). There are about 3.1% green and 3.1% blue dots.