Multi-scale qualitative location: A direction-based model |
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| Affiliation: | 1. School of Geographical Sciences and Urban Planning, Arizona State University, United States;2. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, United States;1. UL Transaction Security, De Heyderweg 2, Leiden, The Netherlands;2. College of Surveying and Geo-Informatics, Tongji University, Siping Road, 1239 Shanghai, China;3. University of New South Wales, Built Environment, Red Centre, Kensington Campus, 2052, NSW, Sydney, Australia;4. CGI Netherland, George Hintzenweg 89, Rotterdam, The Netherlands;5. State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, P.O. Box C310, 129 Luoyu Road, Wuhan, Hubei 430079, China;1. School of Business, Northeast Normal University, Jilin, China;2. School of Management, Jilin University, Jilin, China;3. GoPerception Laboratory, NY, USA |
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| Abstract: | Qualitative locations describe the locations of spatial objects by relating them to a reference frame with qualitative relations. Existing models concerned with regional partitions are mainly topology-based and do not consider the effects of scale changes on locations. This study develops a direction-based multi-scale qualitative location (DMQL) model to fill this gap. First, a cell partition is defined by extending the borders of the minimum bounding rectangles of the regions in a regional partition. Relating spatial objects to all regions by a set of directions is equal to representing the objects as a set of cells in a cell partition. Second, due to the multiple cell representations of spatial objects and the changes in direction relations across scales, some approaches are presented to derive the direction changes between regions in different frames, between spatial objects and regions, and between spatial objects at different scales. Third, the location and relation consistencies of qualitative locations are evaluated based on the cell representations of spatial objects at multiple scales through a case study. The results indicate that the DMQL model can locate objects more precisely than the topology-based models. |
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| Keywords: | GIS Qualitative location Qualitative spatial reasoning Cell partition Direction relations Multi-scale spatial data |
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