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ISPRS Int. J. Geo-Inf. 2018, 7(3), 116; doi:10.3390/ijgi7030116

Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups

1
School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
2
School of Geographical Science, Guangzhou University, Guangzhou 510275, China
3
Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Received: 7 January 2018 / Revised: 23 February 2018 / Accepted: 13 March 2018 / Published: 15 March 2018
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Abstract

Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF) method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area). View Full-Text
Keywords: progressive amalgamation; building cluster; hierarchy; map generalization progressive amalgamation; building cluster; hierarchy; map generalization
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He, X.; Zhang, X.; Yang, J. Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups. ISPRS Int. J. Geo-Inf. 2018, 7, 116.

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