Mining Preferences from Spatial-Temporal Data

The discovery of preferences in space and time is important in a variety of applications. In this paper we first establish the correspondence between a set of preferences in space and time and density estimates obtained from observations of spatial-temporal features recorded within large databases. We perform density estimation using both kernel methods and mixture models. The density estimates constitute a probabilistic representation of preferences. We then present a point process transition density model for space-time event prediction that hinges upon the density estimates from the preference discovery process. The added dimension of preference discovery through feature space analysis enables our model to outperform traditional preference modeling approaches. We demonstrate this performance improvement using a criminal incident database from Richmond, Virginia. Criminal incidents are humaninitiated events that may be governed by criminal preferences over space and time. We applied our modeling technique to breaking and entering crimes committed in both residential and commercial settings. Our approach effectively recovers the preference structure of the criminals and enables one-week ahead forecasts of threatened areas. This capability to accommodate all measurable features, identify the key features, and quantify their relationship with event occurrence over space and time makes this approach applicable to domains other than law enforcement.