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Advancing RTM Methods and
Theories of Risky Places
Risk Terrain Modeling, or
RTM, is an approach to spatial risk analysis that utilizes a geographic information system to attribute qualities of the real world to places on a digitized map. It operationalizes the spatial influence of risk factors to common geographic units, then combines separate layers to produce “risk terrain” maps showing the presence, absence, or intensity of all risk factors at every location throughout the landscape. Theoretically- and empirically-grounded risk terrain maps show where conditions are conducive for crimes or other hazardous events to occur in the future. RTM offers a statistically valid way to articulate and communicate criminogenic and vulnerable areas at the
micro-level. RTM is used for crime
analysis and
the study of global
threats.
This ongoing, multi-part, endeavor is being done in collaboration with internationally renowned criminologists, mathematicians, computer programmers, and public safety and security practitioners. Projects seek to identify current limitations of risk terrain modeling (RTM) methods, to advance RTM and other methods of spatial risk analysis (SRA), and to make SRA methods more accessible to a variety of users with different skill sets. Specific studies include issues of vulnerability and exposure, evaluating place-based interventions, crime persistence, conditional locational interdependence, simulation modeling, and crime types and settings.
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The analysis is separated into three components, each focusing on a specific, under-evaluated aspect of video surveillance.
The first component will identify and contextualize the best places for CCTV camera placement through an analysis of the micro-level environmental features surrounding camera sites. The viewsheds of each camera, which denote the precise area of visibility, will serve as the unit of analysis. Three crime categories will be measured for one year periods following/preceding camera installation: Part I violent crime, Part I property crime, and all Part I crime. Linear regression models will be used to measure the impact of seventeen variables on the Net Effect (NE) and Weighted Displacement Quotient (WDQ) of camera viewsheds. The variables fall into four categories: system-specific (2), line of sight (4), land use (7), and enforcement activity (4). A mix of quantitative approaches (e.g. counts of crime and arrests in camera viewsheds) and qualitative methods (ground truthing to ascertain environmental characteristics) will be employed to collect the necessary data.
The second component will assess the current process by which the Newark Police Department responds to incidents detected by
CCTV. Conceptually, crime detected by CCTV should be more effectively addressed than crime reported via 9-1-1 due to the instantaneous discovery and reporting of the incident. A series of ANOVA models will test this assumption by comparing video detections and calls-for-service on the following factors: average minutes between report to dispatch, average officer response time, and proportion of cases closed by a police action. Additionally, logistic regression models will measure the impact of the following independent variables on case clearance: minutes between report and dispatch, officer response time, incident priority level, and a dummy variable identifying whether the incident was detected by CCTV or not. Findings will identify whether video detections provide police with increased opportunities for offender apprehensions compared to calls-for-service, and measure whether the current policy effectively leverages such benefits.
The final component is a randomized experiment to test the effects of a dedicated team of patrol units dispatched by CCTV operators on the overall effectiveness and efficiency of the video surveillance operation. In recognition of the department’s desire to leverage available resources against violence, the average number of weekly violent crime incidents occurring within each camera scheme will be calculated for the six-month period preceding the experiment. High-violence schemes will be grouped into pairs with one case being assigned to the treatment group while the remaining cameras comprise the control group The treatment group will receive an additional operator dedicated to monitoring the experimental cameras, as well as four police units exclusively tasked with responding to events detected by experimental cameras. The control group will be policed in the standard fashion, with camera operators watching all the systems’ cameras and creating a CAD assignment upon detecting a crime.
Measuring Success
Four success measures will capture the impact of Newark's CCTV surveillance program: detections of crime by video operators; enforcement action of the dedicated field units; incidents of part I crime and calls-for-service; and experimental-group response times and proportion of cases closed. These findings will be compared with the control group and a buffer zone to gauge the impact of patrol units teamed with video surveillance operators in high crime areas.
Implications will relate to camera placements, police deployment policy, and the optimal role of CCTV cameras in targeted law enforcement strategies.
Preliminary Work Leading to this NIJ-Funded Project
Short Research Brief | PDF
Police-monitored CCTV cameras in Newark, NJ : Placement choice and their impact on street-level crime incidents. 2009.
Full Article | Link
Caplan, J. M., Kennedy, L. W., & Petrossian, G. (2011). Police-monitored CCTV cameras in Newark, NJ: A quasi-experimental test of crime deterrence. Journal of Experimental Criminology, 7, 255-274.
Environmental Corrections
This endeavor considers the predictive value of geographical factors, alongside individual attributes, in assessing risk of failure among parolees and inmates leaving prison. In particular, it takes the novel step of examining the effects of criminogenic places, as highlighted by environmental criminology. Taking certain environmental features into account, in addition to individual traits and behaviors, can pave the way for more accurate risk assessment that is attentive to spatial dynamics and their effect on parolees and other offenders who are supervised in the community.
UN Global Pulse
RCPS teamed with Global Pulse, part of the United Nations (UN) Executive Office of the Secretary-General, to bring RTM and actionable information to the global community.
Behavioral Geography
This endeavor encompass a few different projects including the use of minimally invasive GPS devices, ambulatory stress monitoring, and mobile apps. Specific studies include issues of school safety, violent crime, criminal mobility, urban development, and community engagement.
Terrorism and Global Threats
This multi-part endeavor focuses on emerging threats in the 21st century, including global violence. Projects examine the methodology, data, and analysis challenges that researchers and practitioners face in providing predictions about patterns and trends of a variety of hazards.
Calgary Project
RCPS is assisting the University of Calgary GPS-EM Offender Management Project. The Center provides technical advice on how to proceed in the identification, classification and analysis of GIS-related data. In addition, using data provided by police agencies and municipalities, RCPS provides analyses using risk terrain modeling to create base maps against which the GPS tracking data can then be compared. RCPS provides guidance and analytical support in using these maps and integrating them into the overall project. In addition, through the Center, Dr. Kennedy serves as a member of the advisory board of the GPS-EM project and assists in setting research goals and reviewing analytical products and reports.
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