skip navigation


Abstract Database

Register for Latest Research

Stay Informed
Register with NCJRS to receive NCJRS's biweekly e-newsletter JUSTINFO and additional periodic emails from NCJRS and the NCJRS federal sponsors that highlight the latest research published or sponsored by the Office of Justice Programs.

NCJRS Abstract

To download this abstract, check the box next to the NCJ number then click the "Back To Search Results" link. Then, click the "Download" button on the Search Results page. Also see the Obtain Documents page for direction on how to access resources online, via mail, through interlibrary loans, or in a local library.


NCJ Number: 250010 Find in a Library
Title: Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling
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
Page Count: 15
Sponsoring Agency: National Institute of Justice (NIJ)
Washington, DC 20531
Document: PDF
Type: Program/Project Description; Report (Grant Sponsored); Report (Study/Research); Research (Applied/Empirical)
Format: Article; Document (Online)
Language: English
Country: United States of America
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)
Main Term(s): Criminology
Index Term(s): Burglary; Crime analysis; Crime prediction; Geographic distribution of crime; Mathematical modeling; NIJ grant-related documents; NIJ Resources; Statistical analysis
To cite this abstract, use the following link:

*A link to the full-text document is provided whenever possible. For documents not available online, a link to the publisher's website is provided. Tell us how you use the NCJRS Library and Abstracts Database - send us your feedback.