Research on Urban Point of Interest Generalization Method Based on Mapping Presentation
Without taking account of the attribute richness of POI (point of interest) data and spatial distribution limited by roads, a POI generalization method considering both attribute information and spatial distribution has been proposed against the existing point generalization algorithm merely focusing on overall information of point groups. Hierarchical characteristic of urban POI information expression has been firstly analyzed to point out the measurement feature of the corresponding hierarchy. On this basis, an urban POI generalizing strategy has been put forward: POIs urban road network have been divided into three distribution pattern; corresponding generalization methods have been proposed according to the characteristic of POI data in different distribution patterns. Experimental results showed that the method taking into account both attribute information and spatial distribution characteristics of POI can better implement urban POI generalization in the mapping presentation.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1340200Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 846
 Ai Tinghua, Liu Yaolin, A Method of Point Cluster Simplification With Spatial Distribution Properties Preserved (J). Acta Geodaetica et Cartographica Sinica, 2002, 31(2):175-181.
 Yan Haowen, Wang Jiayao. A Generic Algorithm for Point Cluster Generalization Based on Voronoi Diagrams (J). Journal of Image and Graphics, 2005, 10(5):633-636.
 Cai Yongxiang, Guo Qingsheng. Points Group Generalization Based on Kohonen Net (J). Geomatics and Information Science of Wuhan University, 2007, 32 (7):626-629.
 Deng Hongyan, Wu Fang, Qian Haizhong.A Model of Point Cluster Selection Based on Genetic Algorithms (J). Journal of Image and Graphics, 2003, 8(8):970-976
 Li Wenjing, Lin Zhiyong, Long Yi. Application of Rough Set Idea to Points Object GIS Generalization (J). Geomatics and Information Science of Wuhan University, 2008, 33(9):896-899.
 Xu Zening, Gao Xiaolu. A Novel Method for Identifying the Boundary of Urban Built-Up Areas with POI Data (J). Acta Geographica Sinica, 2016, 71(6):928-939.
 Zhao Weifeng, Li Qingquan, Li Bijun. Extracting Hierarchical Landmarks from Urban POI Data (J). Journal of Remote Sensing, 2011, 15(5):973-988.
 Yu Wenhao, Ai Tinghua, Liu Pengcheng. Network Kernel Density Estimation for the Analysis of Facility POI Hotspots (J). Acta Geodaetica et Cartographica Sinica, 2015, 44(12):1378-1383.
 Chen Weishan, Liu Lin, Liang Yutian. Retail Center Recognition and Spatial Aggregating Feature Analysis of Retail Formats in Guangzhou Based on POI Data (J). Geographical Research, 2016, 35(4):703-716.
 Guo Lishuai. Study on Parallel Computing Methods of POI Simplification (D). Nanjing: Nanjing Normal University, 2013
 Yu Yanping. Dynamic Generalization Methods of POI for the Representation of Mobile Map (D). Nanjing: Nanjing Normal University, 2012.
 Ying Shen, Chen Guiqiu, Cao Xiaohang.POI Selection with the Reference of Linear Roads in Multi-scale Maps (J). Engineering of Surveying and Mapping, 2014, 23(7):6-11.
 Yang Min, Ai Tinghua, Lu Wei. A Real-Time Generalization and Multi-Scale Visualization Method for POI Data in Volunteered Geographic Information (J). Acta Geodaetica et Cartographica Sinica, 2015, 44(2):228-234.
 Liu Xinhong, Li Tian, Zhang Yongbo. Point Features Thinning Methods Based on ARCGIS (J). Academic Research, 2015(1):31-34.
 Li Guangqiang, Deng Min, Liu Qiliang. A Spatial Clustering Method Adaptive to Local Density Change (J). Acta Geodaetica et Cartographica Sinica, 2009, 38(3):255-263.
 Yu Wenhao, Ai Tinghua. The Visualization and Analysis of POI Features under Network Space Supported by Kernel Density Estimation (J). Acta Geodaetica et Cartographica Sinica, 2015, 44(1):82-90.
 Chen Xiang, Li Xiaoming, Zhan Ran, Xu Weimin. Studying on Extracting Hierarchical Landmarks from Urban POI data (J). Geomatics and Spatial Information Technology, 2015, 38(10):130-133.