EFFECTIVENESS OF SUPPORT VECTOR MACHINE FOR CRIME HOT-SPOTS PREDICTION
Applied Artificial Intelligence
Anomaly detection via feature-aided tracking and hidden Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
A new point process transition density model for space-time event prediction
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Planning high responsive police patrol routes with frequency constraints
Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication
Modeling and estimation of travel behaviors using bayesian network
Intelligent Decision Technologies - Special issue on design of intelligent environment
Simulating spatial-temporal pulse events in criminal site selection problems
Proceedings of the 2012 Symposium on Agent Directed Simulation
Geographic profiling of criminal groups for military cordon and search
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Police patrol routing on network voronoi diagram
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
| Hi-index | 0.00 |
Criminal gangs, insurgent groups, and terror networks demonstrate observable preferences in selecting the sites where they commit their crimes. Accordingly, police departments, military organizations, and intelligence agencies seek to learn these preferences and identify locations with a high probability of experiencing the particular event of interest in the near future. Often, such agencies are keen not just to predict the spatial pattern of future events but even more importantly to conduct threat assessments of particular criminal gangs or insurgent groups. These threat assessments include identifying where each of the various groups presents the greatest threat to the community, what the most likely targets are for each criminal group, what makes one location more likely to experience an attack than another, and how to most efficiently allocate resources to address the specific threats to the community. Previous research has demonstrated that applying multivariate prediction models to relate features in an area to the occurrence of crimes offers an improvement in predictive performance over traditional methods of hot-spot analysis. This paper introduces the application of multilevel modeling to these multivariate spatial choice models, demonstrating that it is possible to significantly improve the predictive performance of the spatial choice models for individual groups and leverage that information to provide improved threat assessments of the criminal elements in a given geographic area.