A top-down hierarchical spatio-temporal process description method and its data organization

Jiong Xie; Cunjin Xue

doi:10.1117/12.838353

15 October 2009 A top-down hierarchical spatio-temporal process description method and its data organization

Jiong Xie, Cunjin Xue

Author Affiliations +

Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 74922P (2009) https://doi.org/10.1117/12.838353
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China

Abstract

Modeling and representing spatio-temporal process is the key foundation for analyzing geographic phenomenon and acquiring spatio-temporal high-level knowledge. Spatio-temporal representation methods with bottom-up approach based on object modeling view lack of explicit definition of geographic phenomenon and finer-grained representation of spatio-temporal causal relationships. Based on significant advances in data modeling of spatio-temporal object and event, aimed to represent discrete regional dynamic phenomenon composed with group of spatio-temporal objects, a regional spatio-temporal process description method using Top-Down Hierarchical approach (STP-TDH) is proposed and a data organization structure based on relational database is designed and implemented which builds up the data structure foundation for carrying out advanced data utilization and decision-making. The land use application case indicated that process modeling with top-down approach was proved to be good with the spatio-temporal cognition characteristic of our human, and its hierarchical representation framework can depict dynamic evolution characteristic of regional phenomenon with finer-grained level and can reduce complexity of process description.

Citation Download Citation

Jiong Xie and Cunjin Xue "A top-down hierarchical spatio-temporal process description method and its data organization", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74922P (15 October 2009); https://doi.org/10.1117/12.838353

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