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The Detection of Crime, Resource Deployment, and Predictors of Success: A Multi-Level Analysis of Closed-Circuit Television (CCTV) in Newark, NJ collection represents the findings of a multi-level analysis of the Newark, New Jersey Police Department's video surveillance system. This collection contains multiple quantitative data files (Datasets 1-14) as well as spatial data files (Dataset 15 and Dataset 16). The overall project was separated into three components:

• Component 1 (Dataset 1, Individual CCTV Detections and Calls-For-Service Data and Dataset 2, Weekly CCTV Detections in Newark Data) evaluates CCTV's ability to increase the "certainty of punishment" in target areas;
• Component 2 (Dataset 3, Overall Crime Incidents Data; Dataset 4, Auto Theft Incidents Data; Dataset 5, Property Crime Incidents Data; Dataset 6, Robbery Incidents Data; Dataset 7, Theft From Auto Incidents Data; Dataset 8, Violent Crime Incidents Data; Dataset 9, Attributes of CCTV Catchment Zones Data; Dataset 10, Attributes of CCTV Camera Viewsheds Data; and Dataset 15, Impact of Micro-Level Features Spatial Data) analyzes the context under which CCTV cameras best deter crime. Micro-level factors were grouped into five categories: environmental features, line-of-sight, camera design and enforcement activity (including both crime and arrests); and
• Component 3 (Dataset 11, Calls-for-service Occurring Within CCTV Scheme Catchment Zones During the Experimental Period Data; Dataset 12, Calls-for-service Occurring Within CCTV Schemes During the Experimental Period Data; Dataset 13, Targeted Surveillances Conducted by the Experimental Operators Data; Dataset 14, Weekly Surveillance Activity Data; and Dataset 16, Randomized Controlled Trial Spatial Data) was a randomized, controlled trial measuring the effects of coupling proactive CCTV monitoring with directed patrol units.

Over 40 separate four-hour tours of duty, an additional camera operator was funded to monitor specific CCTV cameras in Newark. Two patrol units were dedicated solely to the operators and were tasked with exclusively responding to incidents of concern detected on the experimental cameras. Variables included throughout the datasets include police report and incident dates, crime type, disposition code, number of each type of incident that occurred in a viewshed precinct, number of CCTV detections that resulted in any police enforcement, and number of schools, retail stores, bars and public transit within the catchment zone.

The Enhancing Procedural Justness of Encounters Through Substantiation (EPJETS) project was a collaborative initiative between researchers from Stockton, Rutgers, and Northwestern Universities and the police departments of Atlantic City and Pleasantville, New Jersey that aimed to test whether incorporating principles of procedural justice, sharing body-worn camera (BWC) footage with drivers following traffic stops, and strategically targeting identified high-traffic crash locations for enforcement could improve public trust and perceptions of police legitimacy. Between October 2022 and June 2024, the study evaluated 1,423 traffic stops conducted for speeding violations by comparing standard enforcement protocols to a novel procedural justice-based intervention. Drivers who were speeding were surveyed immediately after the stop by researchers to determine whether the EPJETS protocol positively affected their perceptions of officer treatment and the effectiveness of BWCs.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

The study used a place-based method of evaluation and spatial units of analysis to measure the extent to which allocating police patrols to high-risk areas effected the frequency and spatial distribution of new crime events in 5 U.S. cities. High-risk areas were defined using risk terrain modeling methods. Risk terrain modeling, or RTM, is a geospatial method of operationalizing the spatial influence of risk factors to common geographic units.

The collection contains 333 shape files, 8 SPSS files, and 9 Excel files. The shape files include both city level risk factor locations and crime data from police departments. SPSS and Excel files contain output from GIS data used for analysis.