The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Rapid detection of significant spatial clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On detecting space-time clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Spatial scan statistics: approximations and performance study
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A LRT framework for fast spatial anomaly detection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Smarter water management: a challenge for spatio-temporal network databases
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
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Regional human behavior change refers to the scenarios that people in a certain area exhibit significant behavior deviation from their neighbors and their own past. This regional pattern usually reveals underlying changes of living environment, such as regional development, immigration, disease breakout; or uncovers demographic information from special events, for instance, start/end of school holidays, or religious holidays. Statistically significant behavior changes contain both temporal and spatial characteristics. In this paper, we propose local spatial scan statistic to identify regional behavior changes. To accelerate local search, spatial index is modified to provide data-driven clusters and scalable data access. Base on the restricted spatial index, we provide both exact and approximated approaches to compute local spatial scan. Simulation analysis and case studies on water bills of 15K households validated the efficiency and effectiveness of these approaches on identifying regional behavior changes.