Development of an artificial intelligence system for detection and visualization of auto theft recovery patterns
Olcay Kurşun, Kenneth Michael Reynolds, Ronald Eaglin, Bing Chen, Michael Georgiopoulos · 2005
Auto theft is the most expensive property crime that is on the rise across the nation. The prediction of auto drop-off locations can increase the probability of offender apprehension. For successful prediction, first the patterns of thefts are identified. Then, a prototype expert system successfully identified embedded drop-off location clusters that were previously unknown to investigators. The system was developed using the expert knowledge of auto theft investigators along with spatial and temporal auto theft event data. Drop-off clusters were identified and validated. A map interface allows the user to visualize the feature clusters and produce detailed reports. Such GIS applications give us the ability to attain a geographical perspective of incidents within the community, thus help law enforcement officers discover the patterns of incidents and take necessary measures to prevent them