Association rule mining based on spatio-temporal processes of spatial distribution patterns

Xuewu Zhang; Fenzhen Su; Yishao Shi; Yawen He

doi:10.1117/12.815880

29 December 2008 Association rule mining based on spatio-temporal processes of spatial distribution patterns

Xuewu Zhang, Fenzhen Su, Yishao Shi, Yawen He

Proceedings Volume 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA); 72853Z (2008) https://doi.org/10.1117/12.815880
Event: International Conference on Earth Observation Data Processing and Analysis, 2008, Wuhan, China

Abstract

Spatial distribution pattern is an arrangement of two or more spatial objects according to some spatial relations, such as spatial direction, topological and distance relations. In the real world, spatial objects and spatial distribution pattern all vary continuously along the time-line. Traditional spatial and non-spatial data dissevers this continuous spatio-temporal process. Under analyzing relations among spatial object, its attributes and spatial distribution pattern, we brought metaspatio- temporal process, spatio-temporal process and spatial distribution pattern spatio-temporal process. Rainfall in Eastern China has a typical spatial distribution pattern, being composed of the northern rain area and the southern rain area. Through constructing spatio-temporal process transactions, the association rules can be extracted from spatiotemporal process data set by the Apriori algorithm. The result of the spaio-temporal process association rule mining is consistent with the analysis of the theory. Finally, it is concluded that the spatio-temporal process can describe change of a spatial object in a defined time range, and change trend of one entity can be forecasted through varying trend of others based on the valuable spatio-temporal process association rules.

Citation Download Citation

Xuewu Zhang, Fenzhen Su, Yishao Shi, and Yawen He "Association rule mining based on spatio-temporal processes of spatial distribution patterns", Proc. SPIE 7285, International Conference on Earth Observation Data Processing and Analysis (ICEODPA), 72853Z (29 December 2008); https://doi.org/10.1117/12.815880

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