Geospatial Analysis
Geography and Public Safety: A Quarterly Bulletin of Applied Geography for the Study of Crime and Public Safety, Volume 2, Issue 4
Evidence-Based Practices and Strategies: Risk Terrain Modeling
Captain Baughman of the Kansas City (MO) Police Department answers the question “What is risk terrain modeling?” and explains how it differs from crime mapping, what resources his agency deploys at high risk areas, and the results he has seen form using risk terrain models.
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Police Stops, Crime Prevention, and Community Reaction: A Randomized Field Experiment at Violent Crime Hot Spots
Domestic Violence Research 15 Years After VAWA
Since the passage of the Violence Against Women Act, a majority of the more than 250 research and evaluation studies funded by NIJ examined domestic violence issues. This research has been collected in the Compendium of Research on Violence Against Women, which includes an abstract of each grant and the results of completed studies.
Large-Scale Deep Point Process Models for Crime Forecasting
Informing The Utility of GPS Monitoring for Pretrial Probationary Supervision: An Examination of Reoffending and Collateral Consequences
Stable Isotope Analysis as a Geospatial Tool for Identification: Intra-Individual Isotopic Variability
Investigative and Prosecutorial Strategies for Mitigating Pathways to Radicalization: Creation of a Federal Terrorism Court Record Repository
INNOVATIVE METHODOLOGIES FOR ASSESSING RADICALIZATION RISK: RISK TERRAIN MODELING AND CONJUNCTIVE ANALYSIS
Infusing Community Policing Strategies into Hot Spots Policing Practices: The Impacts on Police-Community Relations in a Mid-Sized City
A Randomized Controlled Trial on Community Infused Problem-Oriented Policing in Crime Hotspots
Point process modeling with spatiotemporal covariates for predicting crime
Development of Advanced Computational and Geographic Visualization Methods for Geospatial and Temporal Strategic Risk Assessment of Crime
Real-Time Crime Forecasting Challenge
The Real-Time Crime Forecasting Challenge sought to harness the advances in data science to address the challenges of crime and justice. It encouraged data scientists across all scientific disciplines to foster innovation in forecasting methods. The goal was to develop algorithms that advance place-based crime forecasting through the use of data from one police jurisdiction.