The Method of Extracting Spatial Distribution Characteristics of Buildings Combined with Feature Classification and Proximity Graph

doi:10.11947/j.AGCS.2017.20160374

Abstract

Abstract: Spatial distribution characteristics of building clusters should be recognized and detected in generalization of building clusters. Based on analysis of relevant research at home and abroad, four major measures(area of the convex hull, compactness, number of edges, orientation of the smallest minimum bounding rectangle) are summarized and put forward from the existing measures with the help of principal component analysis. According to these selected measures, the building classification are studied. When MST(minimum spanning tree) is used to partition the building clusters, factors such as rivers and roads are taken into consideration. Furthermore, a method detecting linear patterns in building clusters automatically is proposed by means of NNG(nearest neighborhood graph), MST, RNG(relative neighborhood graph) and GG(Gabriel graph). Then the influence factors and usability about the recognized results are analysed. Finally, a part of map from OSM (open street map) in Beijing is chosen as experimental data, classification and clustering of the buildings are realized, and the linear patterns in the sub-clusters are recognized.

Key words: map generalization, building clusters, classification, spatial clustering, spatial pattern

CLC Number:

P208

GUO Qingsheng, WEI Zhiwei, WANG Yong, WANG Lin. The Method of Extracting Spatial Distribution Characteristics of Buildings Combined with Feature Classification and Proximity Graph[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(5): 631-638.

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