NDLI: Discovering persistent change windows in spatiotemporal datasets: a summary of results

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

Content Provider	ACM Digital Library
Author	Zhou, Xun Shekhar, Shashi Oliver, Dev
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 $$ 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.
Starting Page	37
Ending Page	46
Page Count	10
File Format	PDF
ISBN	9781450325349
DOI	10.1145/2534921.2534928
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-04
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Spatiotemporal data mining Enumeration and pruning Pattern discovery
Content Type	Text
Resource Type	Article

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