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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.

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 includes data collected with the purpose of determining the geospatial capabilities of the nation's law enforcement agencies (LEAs) with regards to the tools, techniques, and practices used by these agencies.

The collection includes two Excel files. The file "Geospatial Capabilities Survey Data To NACJD V2.xlsx" provides the actual data obtained from the completed surveys (n=311; 314 variables). The other file "Coding Scheme.xlsx" provides a coding scheme to be used with the data.

This dataset contains measures of the count and density of arts, entertainment, and leisure establishments per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2022. Business establishment data were drawn from the National Establishment Time Series (NETS) database and geocoded to 2010 and 2020 Census tract and ZCTA boundaries. The dataset includes four files — Census Tract 2010, Census Tract 2020, ZCTA 2010, and ZCTA 2020 — each containing one observation per geographic unit per year across ten establishment categories including museums, theaters, amusement parks, movie theaters, zoos and gardens, gambling facilities, bowling alleys, hotels, casino hotels, and an aggregate arts and entertainment total.

This dataset contains measures of the number and density of select types of civic, social, and religious organizations per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2022.

This dataset contains measures of the number and density of dollar stores per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2022. The dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau).

This dataset provides annual measures of the number and density of eating and drinking places — including bars and night clubs, retail bakeries, coffee shops, fast food restaurants, delis, pizza restaurants, and sit-down restaurants — per census tract and ZIP Code Tabulation Area (ZCTA) across the United States from 1990 through 2022. Data are derived from the National Establishment Time Series (NETS) database and are available for four geographies: Census Tract 2010, Census Tract 2020, ZCTA 2010, and ZCTA 2020.

This dataset provides annual measures of the number and density of grocery and food stores — including grocery stores, supermarkets, meat and fish markets, fruit and vegetable markets, warehouse clubs selling food, and total food stores — per census tract and ZIP Code Tabulation Area (ZCTA) across the United States from 1990 through 2022. Data are derived from the National Establishment Time Series (NETS) database and are available for four geographies: Census Tract 2010, Census Tract 2020, ZCTA 2010, and ZCTA 2020.

This dataset contains measures of the number and density of health care services per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2022. The dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau).

This dataset measures the number and density of law enforcement organizations—including police departments, fire departments, courts, correctional facilities, and legal counsel and prosecution offices—across United States census tracts and ZIP Code Tabulation Areas (ZCTAs) from 1990 through 2022. Data are derived from the National Establishment Time Series (NETS) database and geocoded to 2010 and 2020 TIGER/Line shapefiles from the US Census Bureau.

This dataset provides annual measures of the number and density of liquor, tobacco, cannabis, vape, and convenience stores per census tract and ZIP Code Tabulation Area (ZCTA) across the United States from 1990 through 2022. Data are derived from the National Establishment Time Series (NETS) database and are available for four geographies: Census Tract 2010, Census Tract 2020, ZCTA 2010, and ZCTA 2020.

This dataset measures the number and area of parks in each U.S. census tract and ZIP Code Tabulation Area (ZCTA), as well as the spatial proximity of parks to two types of EPA-designated polluting sites: Toxics Release Inventory (TRI) facilities and Superfund sites. Park measures are derived from the 2024 ParkServe database (Trust for Public Land); polluting site measures use 2023 TRI data and 2024 Superfund Site data, with proximity calculated within park boundaries and at 0.5-, 1-, and 2-mile buffers. Geographic boundaries are drawn from the U.S. Census Bureau's 2020 TIGER/Line shapefiles.

This dataset provides annual measures of the number and density of personal care services and laundry establishments — including barber shops, beauty shops, coin-operated laundromats, and laundry and dry cleaning services — per census tract and ZIP Code Tabulation Area (ZCTA) across the United States from 1990 through 2022. Data are derived from the National Establishment Time Series (NETS) database and are available for four geographies: Census Tract 2010, Census Tract 2020, ZCTA 2010, and ZCTA 2020.

This dataset provides annual measures of the number and density of recreational services — including fitness centers, golf courses, skating rinks and pools, membership sports clubs, and specialized recreational establishments — per census tract and ZIP Code Tabulation Area (ZCTA) across the United States from 1990 through 2022. Data are derived from the National Establishment Time Series (NETS) database and are available for four geographies: Census Tract 2010, Census Tract 2020, ZCTA 2010, and ZCTA 2020.

This dataset contains measures of the number and density of retail establishments per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2022. Retail establishments are classified into eight categories based on Standard Industrial Classification (SIC) codes: clothing and shoe stores, furniture and appliance stores, music stores, hardware and garden stores, department/variety/general merchandise stores, used merchandise stores, pet stores and pet supplies, and shoe repair shops. The dataset is derived from the National Establishment Time Series (NETS) database and is available in four geographic versions: Census Tract 2010, Census Tract 2020, ZCTA 2010, and ZCTA 2020.

ZIP Codes are administrative codes generated by the United States Postal Service (USPS) that refer to the geographic area covered by a specific set of mail delivery routes. The U.S. Census Bureau calculates and distributes aggregated social, economic, and demographic information for the population associated with "ZIP Code Tabulation Areas" (ZCTAs), which are roughly analogous to ZIP Codes and serve as identifiers for specific neighborhoods and communities. These aggregated census data, however, are unable to account for changes in ZIP Code boundaries that occur between decennial censuses, leading to measurement error and missing data problems for scholars who attempt to use the aggregated ZCTA data. The purpose of this crosswalk file is to allow researchers to overcome this limitation, enabling them to appropriately link spatial reference information (ZIP Codes) with characteristics of the populations to which they refer.

Most ZIP Codes do not change boundaries in a decade, but a large enough percentage do as to create a problem with missing or mis-specified data. Boundary changes typically involve one or more of the following three processes, although a small number of cases do not conform to these typologies: (1) two or more existing ZIP Codes are combined to create a single surviving ZIP Code, (2) an existing ZIP Code is divided into multiple resulting ZIP Codes, and (3) boundaries between two or more existing ZIP Codes are altered.

Each of these types of changes alters the geographic area that a ZIP Code refers to, and as such, the spatial unit identified by the ZIP Code includes a different population, with a different array of characteristics. By linking the spatial units associated with ZIP Codes as these boundary changes are enacted, the research team can both prevent the loss of observations due to missing data, and more accurately measure social, demographic, and economic characteristics associated with each ZIP Code.

This data set identifies changes in ZIP Code boundaries between 1990 and 2020, and provides numeric codes that cluster the ZIP Codes into the smallest geographic unit, or group of ZIP Codes, that are consistent across a decade: 1990 - 2000, 2000 - 2010, and 2010 - 2020. This "crosswalk" covers the contiguous United States, Alaska, Hawaii, and the District of Columbia. Since much administrative data is available with ZIP Code as the smallest identifiable geography, ZIP Codes are often used to embed observations from administrative data (patients, businesses, survey respondents, etc.) within their social, demographic, and economic contexts. However, ZIP Code boundaries change over time, resulting in measurement error (matching observations to the wrong contextual unit) or missing data (due to an observation reporting a ZIP Code that did not exist at the beginning of the observational period). These data were collected, and the crosswalk created, in an attempt to resolve these data quality issues.

School violence, including violence en route to and from school, can make students afraid to go to school and frequently results in serious injury. These assaults occur in a context where the landscape that students navigate each day often includes bullying, substance use, and weapon carrying. Understanding the locations and times when students are vulnerable to assault as they proceed through their school-day routine could identify opportunities for more targeted, evidence-based prevention strategies.

The research team employed a mixed-methods, case-time-control design with GIS-assisted activity path mapping to understand risk factors and protective factors for school assault in the United States. Children aged 12-18 years requiring treatment at the emergency department of The Children's Hospital of Philadelphia (Philadelphia, Pennsylvania) for an assault-related injury, or who attended Philadelphia schools serving as recruitment sites during the study period, were recruited for the study (n=63). Participants were interviewed using a survey questionnaire and GIS technology to recreate details of the path of their activities, indoors during school and outdoors before and after, from the time they awoke in the morning up until the time they were assaulted. In addition, participants were asked to describe their activities sequentially during that period, including companions and weapon carrying, and site-line features of each location (prospect, refuge, and escape). To include individual- and environmental-level context, participants' paths were appended with data characterizing streets, buildings, neighborhood populations, and the weather that day.

This collection contains data from the quantitative survey measures (DS1) and qualitative interview transcripts (DS2) from the path mapping section of the interview. While GIS data were collected, they were not deposited to ICPSR. Qualitative data will be released at a future date.