Discovering persistent change windows in spatiotemporal datasets: A summary of results

Xun Zhou, Shashi Shekhar, Dev Oliver

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

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, T i〉 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013
PublisherAssociation for Computing Machinery
Pages37-46
Number of pages10
ISBN (Print)9781450325349
DOIs
StatePublished - Jan 1 2013
Event2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013 - Orlando, FL, United States
Duration: Nov 4 2013Nov 4 2013

Publication series

NameProceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013

Other

Other2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013
CountryUnited States
CityOrlando, FL
Period11/4/1311/4/13

Keywords

  • enumeration and pruning
  • pattern discovery
  • spatiotemporal data mining

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    Zhou, X., Shekhar, S., & Oliver, D. (2013). Discovering persistent change windows in spatiotemporal datasets: A summary of results. In Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013 (pp. 37-46). (Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013). Association for Computing Machinery. https://doi.org/10.1145/2534921.2534928