Risk Terrain Modeling (RTM)
RTM is a spatial analytical method, available within a number of software programs, that uses the prior locations of a specific crime type (e.g., shootings) and the landscape features that have traditionally been correlated with the crime type in previous academic research (e.g., corner stores, fast-food restaurants, gas stations, etc.) to predict, based on the presence and/or clustering of these landscape features, which areas of a jurisdiction are at increased risk for the crime type in question.
Is the program based on research?
The academic literature on space and crime is nearly a century old. RTM – developed in the late 2000s – allows us to take theory and findings from this body of literature (i.e., which landscape features are correlated with certain types of crime) to predict areas for increased risk of a particular crime.
Has the program been independently evaluated?
Several studies have shown that not only are crime analysts more effective at predicting crime using RTM than traditional, retrospective mapping but also that RTM has a high predictive validity across crime types and jurisdictions. That is, for example, if an RTM was generated using 2017 shooting incidents, a high proportion of 2018 shooting incidents occurred in areas of an increased risk that were identified by the RTM using 2017 data.
Was the program rigorously tested?
RTM has not been rigorously tested (i.e., quasi-experiment, randomized controlled trial) largely because it is a computer program that is also highly dependent on local context and RTM-trained analysts. As such, constructing studies around RTM to be experimental or quasi-experimental is difficult.
Has the program evaluation been replicated?
Study after study, RTM outputs have been found to have extremely high predictive validity cross crime types and jurisdictions.
Was the program tested in Canada?
To-date, RTM has been used in a few Canadian jurisdictions. For example, Andresen and Hodgkinson (2018) used RTM to predict property crime in Vancouver, British Columbia; whereas Onat, Akca, and Bastug (2018) used RTM to predict illicit drug activities in Durham Region, Ontario. Both studies generated high predictive validates.
Despite not being able to be rigorously tested through a randomized controlled trial (level 5 of the Maryland Scientific Methods Scale), RTM consistently generates high prediction validity. Grounded in prior research and theory, RTM has shown to be an effective and evidence-based method of crime prediction.
Assessor: Jacek Koziarski, University of Western Ontario
Jacek Koziarski is a Ph.D. student in the Sociology department at the University of Western Ontario, and a Research Associate for Can-SEBP. Jacek has a broad interest in policing research and developing evidence-based approaches to policing, but his most recent work has specifically focused on specialized police responses to persons in crisis, missing persons, the spatial analysis of crime, and hot spots policing.
Reviewer: Dr. Martin Andresen, Simon Fraser University
Dr. Martin A. Andresen is a Professor in the School of Criminology and Director of the Institute for Canadian Urban Research Studies, Simon Fraser University. His research focuses on spatial crime analysis, crime and place, the geography of crime, environmental criminology, applied spatial statistics and geographical information analysis.