The purpose of this research was to further understanding of why crime occurs where it does by exploring the spatial etiology of homicides that occurred in Washington, DC, during the 13-year period 1990-2002. The researchers accessed records from the case management system of the Metropolitan Police, District of Columbia (MPDC) Homicide Division to collect data regarding offenders and victims associated with the homicide cases. Using geographic information systems (GIS) software, the researchers geocoded the addresses of the incident location, the victim's residence, and offender's residence for each homicide case. They then calculated both Euclidean distance and shortest path distance along the streets between each address per case. Upon applying the concept of triad as developed by Block et al. (2004) in order to create a unit of analysis for studying the convergence of victims and offenders in space, the researchers categorized the triads according to the geometry of locations associated with each case. (Dots represented homicides in which the victim and offender both lived in the residence where the homicide occurred; lines represented homicides that occurred in the home of either the victim or the offender; and triangles represented three non-coincident locations: the separate residences of the victim and offender, as well as the location of the homicide incident.) The researchers then classified each triad according to two separate mobility triangle classification schemes: Traditional Mobility, based on shared or disparate social areas, and Distance Mobility, based on relative distance categories between locations. Finally, the researchers classified each triad by the neighborhood associated with the location of the homicide incident, the location of the victim's residence, and the location of the offender's residence. A total of 3 statistical datasets and 7 geographic information systems (GIS) shapefiles resulted from this study. Note: All datasets exclude open homicide cases. The statistical datasets consist of Offender Characteristics (Dataset 1) with 2,966 cases; Victim Characteristics (Dataset 2) with 2,311 cases; and Triads Data (Dataset 3) with 2,510 cases. The GIS shapefiles have been grouped into a zip file (Dataset 4). Included are point data for homicide locations, offender residences, triads, and victim residences; line data for streets in the District of Columbia, Maryland, and Virginia; and polygon data for neighborhood clusters in the District of Columbia.

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.