TY - GEN
T1 - Discovering persistent change windows in spatiotemporal datasets
T2 - 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013
AU - Zhou, Xun
AU - Shekhar, Shashi
AU - Oliver, Dev
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - enumeration and pruning
KW - pattern discovery
KW - spatiotemporal data mining
UR - http://www.scopus.com/inward/record.url?scp=84896374962&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896374962&partnerID=8YFLogxK
U2 - 10.1145/2534921.2534928
DO - 10.1145/2534921.2534928
M3 - Conference contribution
AN - SCOPUS:84896374962
SN - 9781450325349
T3 - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013
SP - 37
EP - 46
BT - Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2013
PB - Association for Computing Machinery
Y2 - 4 November 2013 through 4 November 2013
ER -