Name
Abstract | ||
|---|---|---|
Building generalization is a difficult operation due to the complexity of the spatial distribution of buildings and for reasons of spatial recognition. In this study, building generalization is decomposed into two steps, i.e. building grouping and generalization execution. The neighbourhood model in urban morphology provides global constraints for guiding the global partitioning of building sets on the whole map by means of roads and rivers, by which enclaves, blocks, superblocks or neighbourhoods are formed; whereas the local constraints from Gestalt principles provide criteria for the further grouping of enclaves, blocks, superblocks and/or neighbourhoods. In the grouping process, graph theory, Delaunay triangulation and the Voronoi diagram are employed as supporting techniques. After grouping, some useful information, such as the sum of the building's area, the mean separation and the standard deviation of the separation of buildings, is attached to each group. By means of the attached information, an appropriate operation is selected to generalize the corresponding groups. Indeed, the methodology described brings together a number of well-developed theories/techniques, including graph theory, Delaunay triangulation, the Voronoi diagram, urban morphology and Gestalt theory, in such a way that multiscale products can be derived. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1080/13658810410001702021 | INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE |
Keywords | Field | DocType |
standard deviation,graph theory,voronoi diagram,group process,development theory,delaunay triangulation | Graph theory,Data mining,Computer science,Urban morphology,Gestalt psychology,Neighbourhood (mathematics),Voronoi diagram,Standard deviation,Delaunay triangulation | Journal |
Volume | Issue | ISSN |
18 | 5 | 1365-8816 |
Citations | PageRank | References |
45 | 2.39 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhilin Li | 1 | 433 | 62.27 |
| Haowen Yan | 2 | 101 | 11.43 |
| Tinghua Ai | 3 | 175 | 27.82 |
| Jun Chen | 4 | 207 | 21.33 |