Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Discovering interesting sub-paths in spatiotemporal datasets: a summary of results
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Change detection in time series data using wavelet footprints
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Fast adaptive algorithms for abrupt change detection
Machine Learning
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Given a region S comprised of locations that each have a time series of length |T|, the Persistent Change Windows (PCW) discovery problem aims to find all spatial window and temporal interval pairs Si, Ti that exhibit persistent change of attribute values over time. PCW discovery is important for critical societal applications such as detecting desertification, deforestation, and monitoring urban sprawl. The PCW discovery problem is challenging due to the large number of candidate patterns, the lack of monotonicity where sub-regions of a PCW may not show persistent change, the lack of predefined window sizes for the ST windows, and large datasets of detailed resolution and high volume, i.e., spatial big data. Previous approaches in ST change footprint discovery have focused on local spatial footprints for persistent change discovery and may not guarantee completeness. In contrast, we propose a space-time window enumeration and pruning (SWEP) approach that considers zonal spatial footprints when finding persistent change patterns. We provide theoretical analysis of SWEP's correctness, completeness, and space-time complexity. We also present a case study on vegetation data that demonstrates the usefulness of the proposed approach. Experimental evaluation on synthetic data show that the SWEP approach is orders of magnitude faster than the naive approach.