Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1143-1150, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-1143-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
14 Sep 2017
STUDY ON ADAPTIVE PARAMETER DETERMINATION OF CLUSTER ANALYSIS IN URBAN MANAGEMENT CASES
J. Y. Fu1, C. F. Jing1,2, M. Y. Du1,2, Y. L. Fu1, and P. P. Dai1,3 1School of Geomatics and Urban Spatial Information of Beijing University of Civil Engineering and Architecture, 100044 Beijing
2Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, Beijing, China
3Beijing Digsur Science and Technology co.Ltd, Beijing, China
Keywords: DBSCAN algorithm, urban management cases, cluster analysis, data mining Abstract. The fine management for cities is the important way to realize the smart city. The data mining which uses spatial clustering analysis for urban management cases can be used in the evaluation of urban public facilities deployment, and support the policy decisions, and also provides technical support for the fine management of the city. Aiming at the problem that DBSCAN algorithm which is based on the density-clustering can not realize parameter adaptive determination, this paper proposed the optimizing method of parameter adaptive determination based on the spatial analysis. Firstly, making analysis of the function Ripley's K for the data set to realize adaptive determination of global parameter MinPts, which means setting the maximum aggregation scale as the range of data clustering. Calculating every point object’s highest frequency K value in the range of Eps which uses K-D tree and setting it as the value of clustering density to realize the adaptive determination of global parameter MinPts. Then, the R language was used to optimize the above process to accomplish the precise clustering of typical urban management cases. The experimental results based on the typical case of urban management in XiCheng district of Beijing shows that: The new DBSCAN clustering algorithm this paper presents takes full account of the data’s spatial and statistical characteristic which has obvious clustering feature, and has a better applicability and high quality. The results of the study are not only helpful for the formulation of urban management policies and the allocation of urban management supervisors in XiCheng District of Beijing, but also to other cities and related fields.
Conference paper (PDF, 2215 KB)


Citation: Fu, J. Y., Jing, C. F., Du, M. Y., Fu, Y. L., and Dai, P. P.: STUDY ON ADAPTIVE PARAMETER DETERMINATION OF CLUSTER ANALYSIS IN URBAN MANAGEMENT CASES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 1143-1150, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1143-2017, 2017.

BibTeX EndNote Reference Manager XML