Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling

• NCJ Number: 250010
• Journal: IEEE Transactions on Knowledge and Data Engineering Volume: 28 Issue: 4 Dated: April 2016 Pages: 979-993
• Author(s): C. H. Yu; W. Ding; M. Morabito; P. Chen
• Date Published: April 2016
• Length: 15 pages
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description

Annotation

The authors propose a new approach, CCRBoost, to identify the hierarchical structure of spatio-temporal patterns at different resolution levels, and they subsequently constructed a predictive model based on the identified structure.

Abstract

This was done by first obtaining indicators within different spatio-temporal spaces from the raw data. A distributed spatio-temporal pattern (DSTP) was extracted from a distribution, which consists of the locations with similar indicators from the same time period, generated by multi-clustering. Next, a greedy searching and pruning algorithm was used to combine the DSTPs in order to form an ensemble spatio-temporal pattern (ESTP). An ESTP can represent the spatio-temporal pattern of various regularities or a non-stationary pattern. To consider all the possible scenarios of a real-world ST pattern, a model was built with layers of weighted ESTPs. By evaluating all the indicators of one location, this model can predict whether a target event will occur at this location. In the case study of predicting crime events, results indicate that the predictive model can achieve 80 percent accuracy in predicting residential burglary, which is better than other methods. (Publisher abstract modified)