The authors propose a new approach, CCRBoost, to identify the hierarchical structure of spatio-temporal patterns at different resolution levels and subsequently construct a predictive model based on the identified structure.
To accomplish this, they first obtained indicators within different spatio-temporal spaces from the raw data. A distributed spatio-temporal pattern (DSTP) is extracted from a distribution, which consists of the locations with similar indicators from the same time, generated by multi-clustering. Next, they use a greedy searching and pruning algorithm to combine the DSTPs 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, the authors then built a model 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, the results indicate that the predictive model can achieve 80 percent accuracy in predicting residential burglary, which is better than other methods. (Published abstract provided).