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.

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. Researchers compiled datasets on prison admissions and releases that would be comparable across places and geocoded and mapped those data onto crime rates across those same places. The data used were panel data. The data were quarterly or annual data, depending on the location, from a mix of urban (Boston, Newark and Trenton) and rural communities in New Jersey covering various years between 2000 and 2010. The crime, release, and admission data were individual level data that were then aggregated from the individual incident level to the census tract level by quarter (in Boston and Newark) or year (in Trenton). The analyses centered on the effects of rates of prison removals and returns on rates of crime in communities (defined as census tracts) in the cities of Boston, Massachusetts, Newark, New Jersey, and Trenton, New Jersey, and across rural municipalities in New Jersey. There are 4 Stata data files. The Boston data file has 6,862 cases, and 44 variables. The Newark data file has 1,440 cases, and 45 variables. The Trenton data file has 66 cases, and 32 variables. The New Jersey Rural data file has 1,170 cases, and 32 variables.

This data collection was designed to evaluate the effects of disorderly neighborhood conditions on community decline and residents' reactions toward crime. Data from five previously collected datasets were aggregated and merged to produce this collection: (1) REACTIONS TO CRIME PROJECT, 1977 [CHICAGO, PHILADELPHIA, SAN FRANCISCO]: SURVEY ON FEAR OF CRIME AND CITIZEN BEHAVIOR (ICPSR 8162), (2) CHARACTERISTICS OF HIGH AND LOW CRIME NEIGHBORHOODS IN ATLANTA, 1980 (ICPSR 8951), (3) CRIME FACTORS AND NEIGHBORHOOD DECLINE IN CHICAGO, 1979 (ICPSR 7952), (4) REDUCING FEAR OF CRIME PROGRAM EVALUATION SURVEYS IN NEWARK AND HOUSTON, 1983-1984 (ICPSR 8496), and (5) a survey of citizen participation in crime prevention in six Chicago neighborhoods conducted by Rosenbaum, Lewis, and Grant. Neighborhood-level data cover topics such as disorder, crime, fear, residential satisfaction, and other key factors in community decline. Variables include disorder characteristics such as loitering, drugs, vandalism, noise, and gang activity, demographic characteristics such as race, age, and unemployment rate, and neighborhood crime problems such as burglary, robbery, assault, and rape. Information is also available on crime avoidance behaviors, fear of crime on an aggregated scale, neighborhood satisfaction on an aggregated scale, and cohesion and social interaction.