Predicting Dynamical Crime Distribution From Environmental and Social Influences

Abstract

Understanding how social and environmental factors contribute to the spatio-temporal distribution of criminal activities is a fundamental question in modern criminology. Thanks to the development of statistical techniques such as Risk Terrain Modeling (RTM), it is possible to evaluate precisely the criminogenic contribution of environmental features to a given location. However, the role of social information in shaping the distribution of criminal acts is largely understudied by the criminological research literature. In this paper we investigate the existence of spatio-temporal correlations between successive robbery events, after controlling for environmental influences as estimated by RTM. We begin by showing that a robbery event increases the likelihood of future robberies at and in the neighborhood of its location. This event-dependent influence decreases exponentially with time and as an inverse function of the distance to the original event.We then combine event-dependence and environmental influences in a simulation model to predict robbery patterns at the scale of a large city (Newark, NJ). We show that this model significantly improves upon the predictions of RTM alone and of a model taking into account event-dependence only when tested against real data that were not used to calibrate either model. We conclude that combining risk from exposure (past event) and vulnerability (environment), following from the Theory of Risky Places, when modeling crime distribution can improve crime suppression and prevention efforts by providing more accurate forecasting of the most likely locations of criminal events.

Mapping the Risk Terrain for Crime Using Machine Learning

“…However, near repeat factors can be incorporated into RF models by predicting crimes over shorter time periods and including factors such as the number of nearby crimes within a short time period (Mohler and Porter 2018). This opens up new research avenues using our predictive model: to investigate the extent to which certain place based characteristics interact with crime to create greater or lesser risk for near repeat crime events Garnier et al 2018;Moreto et al 2014;Piza and Carter 2018).…”

“…Nor is long term urban planning, such as forecasting the future crime impact if a city zones for additional commercial locations. Thus we assess the predictive accuracy of the technique over a long period, effectively eliminating explanations of crime due to near-repeat patterns Garnier, Caplan, and Kennedy 2018;Ratcliffe, Taylor, and Perezin 2016;Taylor, Ratcliffe, and Perezin, 2015). 5 Given that micro level crime patterns tend to exhibit a strong amount of stability over time (Curman, Andresen, and Brantingham 2014;Weisburd et al 2004;Wheeler, Worden, and McLean 2016), forecasts over a long period of time are meaningful for police departments.…”

Spatial Concentration of Opioid Overdose Deaths in Indianapolis: An Application of the Law of Crime Concentration at Place to a Public Health Epidemic

“…For example, we find that 17 percent of opioid related deaths fall in the top one percent of 500m x 500m cells. Similar analyses are used to demonstrate the concentration of crime on street segments (Weisburd, 2015;Hipp & Kim, 2017) as well as spatiotemporal patterns of crime (Garnier, Caplan & Kennedy, 2018). We present the results of this analysis in Table 1. In general, the concentration of crime increases (meaning more crime in a smaller area) as the cell size and percentage area flagged decrease (see Bernasco & Steenbeek, 2017;Hipp & Kim, 2017;Mohler et al, 2017).…”

Forecasting Crime and Understanding its Causes. Applying Risk Terrain Modeling Worldwide

“…Going forward, RTM researchers are exploring new applications of the approach, including simulation modeling (Garnier et al 2018) and implementation in risk-based policing (Kennedy et al 2018). The future holds new challenges for spatial analysis of crime and we believe that this special issue has made a significant contribution to this emerging field of inquiry.…”