利用模糊密度聚类和双向缓冲区自动识别热点区
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摘要
通过数据挖掘手段获取聚集模式(即热点)等地理空间知识是地理信息智能化服务的基础和前提。点群聚集模式的提取本质上是热点及其边界(热点区)的探测。首先分析了使用空间聚类提取热点并以凸壳表达热点轮廓的不足,进而提出一种利用模糊密度聚类和双向缓冲区的热点区自动识别方法。该方法借鉴模糊集理论,通过计算对象之间的模糊隶属度改进基于密度的聚类算法,用以提取点群的聚集模式;在此基础上,将模糊隶属度作为对象间的影响程度,采用正负缓冲区建立热点边界。以郑州市城区的科研机构点为例进行实验,结果表明,提出的方法既能有效区分空间点的类型(噪声点与非噪声点),又能生成连续平滑的热点边界,总体效果优于对比方法。
Obtaining geospatial knowledge such as aggregation mode(i.e. hotspot) by data mining is the basis and premise of geographic information intelligent service. The aggregation mode extraction from point group is the detection of hotspots and their boundaries(hotspot areas) essentially. This paper firstly analyzes the shortcomings of the DBSCAN(density-based spatial clustering of applications with noise)-convex hull method for hotspot area recognition, and then proposes an automatic method of hotspot area generation using fuzzy density clustering and bidirectional buffer. There are two parts in this method: ①Based on the theory of fuzzy sets, the fuzzy membership is calculated to improve the DBSCAN; ②The boundaries of hotspots are generated using positive-negative buffer method according to the influence radius calculated by the fuzzy membership formula. The experimental results show that this method can reflect the spatial pattern of the scientific situation. Besides, noises can be distinguished from points, thus ensuring there are no noise points in the hotspot area. More-over, the hotspot boundaries are not only continuous and flat, also can reflect the actual shape and range of reasonable hotspot areas. Compared with the DBSCAN-convex hull method and the kernel density-contour method, the hotspot area recognized by the method proposed in this paper is better.
Obtaining geospatial knowledge such as aggregation mode(i.e. hotspot) by data mining is the basis and premise of geographic information intelligent service. The aggregation mode extraction from point group is the detection of hotspots and their boundaries(hotspot areas) essentially. This paper firstly analyzes the shortcomings of the DBSCAN(density-based spatial clustering of applications with noise)-convex hull method for hotspot area recognition, and then proposes an automatic method of hotspot area generation using fuzzy density clustering and bidirectional buffer. There are two parts in this method: ①Based on the theory of fuzzy sets, the fuzzy membership is calculated to improve the DBSCAN; ②The boundaries of hotspots are generated using positive-negative buffer method according to the influence radius calculated by the fuzzy membership formula. The experimental results show that this method can reflect the spatial pattern of the scientific situation. Besides, noises can be distinguished from points, thus ensuring there are no noise points in the hotspot area. More-over, the hotspot boundaries are not only continuous and flat, also can reflect the actual shape and range of reasonable hotspot areas. Compared with the DBSCAN-convex hull method and the kernel density-contour method, the hotspot area recognized by the method proposed in this paper is better.
引文
[1] Wang Jiayao. Cartography in the Age of Spatial-Temporal Big Data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1 226-1 237 (王家耀. 时空大数据时代的地图学[J]. 测绘学报, 2017, 46(10): 1 226-1 237)
[2] Wang Jiayao, Cui Xiaojie. Transformation and Development of Geoinformation Industry Driven by Innovation[M]//Blue Book of China’s Surveying, Mapping and Geoinformation: Report on Transformation and Upgrading of Surveying, Mapping and Geoinformation (2014). Beijing: Social Sciences Academic Press, 2014: 201-217(王家耀,崔晓杰.创新驱动地理信息产业转型发展[M]// 测绘地理信息蓝皮书:测绘地理信息转型升级研究报告(2014). 北京:社会科学文献出版社,2014: 201-217)
[3] Gong Jianya, Geng Jing, Wu Huayi. Geospatial Knowledge Service: A Review[J].Geomatics and Information Science of Wuhan University, 2014, 39(8):883-890 (龚健雅, 耿晶, 吴华意. 地理空间知识服务概论[J]. 武汉大学学报·信息科学版, 2014, 39(8):883-890)
[4] Wang Jiayao, Cui Xiaojie.“Internet+” Spatial and Temporal Big Data and Smart City[M]//Blue Book of China’s Surveying, Mapping and Geoinformation: Report on Surveying, Mapping and Geoinformation Under the New Normal(2015). Beijing: Social Sciences Academic Press, 2015:149-164(王家耀,崔晓杰. “互联网+”时代的地理时空大数据与智慧城市[M]// 测绘地理信息蓝皮书:新常态下的测绘地理信息研究报告(2015). 北京:社会科学文献出版社,2015: 149-164)
[5] Li Deren. From Geomatics to Geospatial Intelligent Service Science[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1 207-1 212 (李德仁. 从测绘学到地球空间信息智能服务科学[J]. 测绘学报, 2017, 46(10) :1 207-1 212)
[6] Wu Fang, Gong Xianyong, Du Jiawei. Overview of the Research Progress in Automated Map Generalization [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1 645-1 664 (武芳, 巩现勇, 杜佳威. 地图制图综合回顾与前望[J]. 测绘学报, 2017, 46(10): 1 645-1 664)
[7] Mao Zhengyuan. The Study of Extracting Structure Information of a Clustered Spatial Point Pattern[J].Acta Geodaetica et Cartographica Sinica, 2007, 36(2):181-186 (毛政元. 集聚型空间点模式结构信息提取研究[J]. 测绘学报, 2007, 36(2):181-186)
[8] Galton A, Duckham M. What is the Region Occupied by a Set of Points?[C]// International Conference on Geographic Information Science. Berlin, Heidelberg:Springer, 2006:81-98
[9] Duckham M, Kulik L, Worboys M, et al. Efficient Generation of Simple Polygons for Characterizing the Shape of a Set of Points in the Plane[J]. Pattern Recognition, 2008, 41(10):3 224-3 236
[10] Liu Y, Yuan Y, Xiao D, et al. A Point-Set-Based Approximation for Areal Objects: A Case Study of Representing Localities[J]. Computers, Environment and Urban Systems, 2010, 34(1):28-39
[11] Akdag F, Eick C F, Chen G. Creating Polygon Models for Spatial Clusters[C].International Symposium on Methodologies for Intelligent Systems,Roskilde, Denmark, 2014
[12] Hollenstein L, Purves R. Exploring Place Through User-Generated Content: Using Flickr to Describe City Cores[J]. Journal of Spatial Information Science, 2010, 1(1):21-48
[13] 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 (许泽宁, 高晓路. 基于电子地图兴趣点的城市建成区边界识别方法[J]. 地理学报, 2016, 71(6):928-939)
[14] Xue Dongqian, Huang Jing, Ma Beibei, et al. Spatial Distribution Characteristics and Hot Zone Patterns of Entertainment Industry in Xi’an[J]. Acta Geographica Sinica, 2014, 69(4):541-552 (薛东前, 黄晶, 马蓓蓓,等. 西安市文化娱乐业的空间格局及热点区模式研究[J]. 地理学报, 2014, 69(4): 541-552)
[15] Chen Peng, Li Xin, Hu Xiaofeng, et al. Clustering Pattern Analysis and Prevention Strategies to Pickpocketing Offence Along the Chang’an Street in Beijing[J]. Progress in Geography, 2015, 34(10): 1 250-1 258 (陈鹏, 李欣, 胡啸峰, 等. 北京市长安街沿线的扒窃案件高发区分析及防控对策[J]. 地理科学进展, 2015, 34(10):1 250-1 258)
[16] Shen J, Liu X, Chen M. Discovering Spatial and Temporal Patterns from Taxi-Based Floating Car Data: A Case Study from Nanjing[J]. GIScience & Remote Sensing, 2017, 54(5):617-638
[17] Tian Jing, Xiong Fuquan, Cheng Xueping, et al. Road Density Partition and Its Application in Evalua- tion of Road Selection[J].Geomatics and Information Science of Wuhan University, 2016, 41(9):1 225-1 231 (田晶, 熊富全, 程雪萍,等. 道路密度分区及其在道路选取质量评价中的应用[J]. 武汉大学学报·信息科学版, 2016, 41(9):1 225-1 231)
[18] Wu Kangmin, Zhang Hong’ou, Wang Yang, et al. Identify of the Multiple Types of Commercial Center in Guangzhou and Its Spatial Pattern[J].Progress in Geography, 2016, 35(8):963-974 (吴康敏, 张虹鸥, 王洋,等. 广州市多类型商业中心识别与空间模式[J]. 地理科学进展, 2016, 35(8): 963-974)
[19] Yu W, Ai T, Shao S. The Analysis and Delimitation of Central Business District Using Network Kernel Density Estimation[J].Journal of Transport Geography, 2015, 45(1):32-47
[20] Tang M, Zhou Y, Cui P, et al. Discovery of Migration Habitats and Routes of Wild Bird Species by Clustering and Association Analysis[C].International Conference on Advanced Data Mining and Applications,Beijing,China, 2009
[21] Hu Y, Gao S, Janowicz K, et al. Extracting and Understanding Urban Areas of Interest Using Geotagged Photos[J]. Computers, Environment and Urban Systems, 2015, 54:240-254
[22] Gao S, Janowicz K, Montello D R, et al. A Data-Synthesis-Driven Method for Detecting and Extracting Vague Cognitive Regions[J]. International Journal of Geographical Information Systems, 2017, 31(6):1 245-1 271
[23] Gao Kai, Yang Min, Zhang Yuepeng. A Method of Automatic Selection of Hash-Style Habitation with Spatial Distribution Characteristics Preserved[J].Journal of Geomatics Science and Technology, 2015, 32(6):626-630 (高凯, 杨敏, 张跃鹏. 保持空间分布特征的散列式居民地综合选取方法[J]. 测绘科学技术学报, 2015, 32(6):626-630)
[24] Miller H, Han J. Geographic Data Mining and Knowledge Discovery[M]. 2nd ed. New York:CRC, 2009
[25] Deng M, Liu Q, Cheng T, et al. An Adaptive Spatial Clustering Algorithm Based on Delaunay Triangulation[J]. Computers Environment & Urban Systems, 2011, 35(4):320-332
[26] Liu Q, Deng M, Shi Y, et al. A Density-Based Spatial Clustering Algorithm Considering Both Spatial Proximity and Attribute Similarity[J]. Compu- ters and Geosciences, 2012, 46(3):296-309
[27] Ester M, Kriegel H P, Sander J, et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996: 226-231
[28] He Dan, Chen Songcan. Robust Kernal-Based Fuzzy Clustering Using Difference of Convex Functions Programming[J].Pattern Recognition and Artificial Intelligence, 2016, 29(8):744-750 (贺丹, 陈松灿. 基于DC规划的鲁棒模糊核聚类算法[J]. 模式识别与人工智能, 2016, 29(8):744-750)
[29] Nasibov E N, Ulutagay G. Robustness of Density-Based Clustering Methods with Various Neighborhood Relations[J]. Fuzzy Sets and Systems, 2009, 160(24):3 601-3 615
[30] Guo Renzhong. Spatial Analysis[M]. 2nd ed. Beijing: Higher Education Press, 2001 (郭仁忠. 空间分析[M]. 2版.北京: 高等教育出版社, 2001)
[31] Yu Wenhao, Ai Tinghua, Yang Min, et al. Detecting “Hot Spots” of Facility POIs Based on Kernel Density Estimation and Spatial Autocorrelation Technique[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2):221-227(禹文豪, 艾廷华, 杨敏,等. 利用核密度与空间自相关进行城市设施兴趣点分布热点探测[J]. 武汉大学学报·信息科学版, 2016, 41(2):221-227)
[2] Wang Jiayao, Cui Xiaojie. Transformation and Development of Geoinformation Industry Driven by Innovation[M]//Blue Book of China’s Surveying, Mapping and Geoinformation: Report on Transformation and Upgrading of Surveying, Mapping and Geoinformation (2014). Beijing: Social Sciences Academic Press, 2014: 201-217(王家耀,崔晓杰.创新驱动地理信息产业转型发展[M]// 测绘地理信息蓝皮书:测绘地理信息转型升级研究报告(2014). 北京:社会科学文献出版社,2014: 201-217)
[3] Gong Jianya, Geng Jing, Wu Huayi. Geospatial Knowledge Service: A Review[J].Geomatics and Information Science of Wuhan University, 2014, 39(8):883-890 (龚健雅, 耿晶, 吴华意. 地理空间知识服务概论[J]. 武汉大学学报·信息科学版, 2014, 39(8):883-890)
[4] Wang Jiayao, Cui Xiaojie.“Internet+” Spatial and Temporal Big Data and Smart City[M]//Blue Book of China’s Surveying, Mapping and Geoinformation: Report on Surveying, Mapping and Geoinformation Under the New Normal(2015). Beijing: Social Sciences Academic Press, 2015:149-164(王家耀,崔晓杰. “互联网+”时代的地理时空大数据与智慧城市[M]// 测绘地理信息蓝皮书:新常态下的测绘地理信息研究报告(2015). 北京:社会科学文献出版社,2015: 149-164)
[5] Li Deren. From Geomatics to Geospatial Intelligent Service Science[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1 207-1 212 (李德仁. 从测绘学到地球空间信息智能服务科学[J]. 测绘学报, 2017, 46(10) :1 207-1 212)
[6] Wu Fang, Gong Xianyong, Du Jiawei. Overview of the Research Progress in Automated Map Generalization [J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10): 1 645-1 664 (武芳, 巩现勇, 杜佳威. 地图制图综合回顾与前望[J]. 测绘学报, 2017, 46(10): 1 645-1 664)
[7] Mao Zhengyuan. The Study of Extracting Structure Information of a Clustered Spatial Point Pattern[J].Acta Geodaetica et Cartographica Sinica, 2007, 36(2):181-186 (毛政元. 集聚型空间点模式结构信息提取研究[J]. 测绘学报, 2007, 36(2):181-186)
[8] Galton A, Duckham M. What is the Region Occupied by a Set of Points?[C]// International Conference on Geographic Information Science. Berlin, Heidelberg:Springer, 2006:81-98
[9] Duckham M, Kulik L, Worboys M, et al. Efficient Generation of Simple Polygons for Characterizing the Shape of a Set of Points in the Plane[J]. Pattern Recognition, 2008, 41(10):3 224-3 236
[10] Liu Y, Yuan Y, Xiao D, et al. A Point-Set-Based Approximation for Areal Objects: A Case Study of Representing Localities[J]. Computers, Environment and Urban Systems, 2010, 34(1):28-39
[11] Akdag F, Eick C F, Chen G. Creating Polygon Models for Spatial Clusters[C].International Symposium on Methodologies for Intelligent Systems,Roskilde, Denmark, 2014
[12] Hollenstein L, Purves R. Exploring Place Through User-Generated Content: Using Flickr to Describe City Cores[J]. Journal of Spatial Information Science, 2010, 1(1):21-48
[13] 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 (许泽宁, 高晓路. 基于电子地图兴趣点的城市建成区边界识别方法[J]. 地理学报, 2016, 71(6):928-939)
[14] Xue Dongqian, Huang Jing, Ma Beibei, et al. Spatial Distribution Characteristics and Hot Zone Patterns of Entertainment Industry in Xi’an[J]. Acta Geographica Sinica, 2014, 69(4):541-552 (薛东前, 黄晶, 马蓓蓓,等. 西安市文化娱乐业的空间格局及热点区模式研究[J]. 地理学报, 2014, 69(4): 541-552)
[15] Chen Peng, Li Xin, Hu Xiaofeng, et al. Clustering Pattern Analysis and Prevention Strategies to Pickpocketing Offence Along the Chang’an Street in Beijing[J]. Progress in Geography, 2015, 34(10): 1 250-1 258 (陈鹏, 李欣, 胡啸峰, 等. 北京市长安街沿线的扒窃案件高发区分析及防控对策[J]. 地理科学进展, 2015, 34(10):1 250-1 258)
[16] Shen J, Liu X, Chen M. Discovering Spatial and Temporal Patterns from Taxi-Based Floating Car Data: A Case Study from Nanjing[J]. GIScience & Remote Sensing, 2017, 54(5):617-638
[17] Tian Jing, Xiong Fuquan, Cheng Xueping, et al. Road Density Partition and Its Application in Evalua- tion of Road Selection[J].Geomatics and Information Science of Wuhan University, 2016, 41(9):1 225-1 231 (田晶, 熊富全, 程雪萍,等. 道路密度分区及其在道路选取质量评价中的应用[J]. 武汉大学学报·信息科学版, 2016, 41(9):1 225-1 231)
[18] Wu Kangmin, Zhang Hong’ou, Wang Yang, et al. Identify of the Multiple Types of Commercial Center in Guangzhou and Its Spatial Pattern[J].Progress in Geography, 2016, 35(8):963-974 (吴康敏, 张虹鸥, 王洋,等. 广州市多类型商业中心识别与空间模式[J]. 地理科学进展, 2016, 35(8): 963-974)
[19] Yu W, Ai T, Shao S. The Analysis and Delimitation of Central Business District Using Network Kernel Density Estimation[J].Journal of Transport Geography, 2015, 45(1):32-47
[20] Tang M, Zhou Y, Cui P, et al. Discovery of Migration Habitats and Routes of Wild Bird Species by Clustering and Association Analysis[C].International Conference on Advanced Data Mining and Applications,Beijing,China, 2009
[21] Hu Y, Gao S, Janowicz K, et al. Extracting and Understanding Urban Areas of Interest Using Geotagged Photos[J]. Computers, Environment and Urban Systems, 2015, 54:240-254
[22] Gao S, Janowicz K, Montello D R, et al. A Data-Synthesis-Driven Method for Detecting and Extracting Vague Cognitive Regions[J]. International Journal of Geographical Information Systems, 2017, 31(6):1 245-1 271
[23] Gao Kai, Yang Min, Zhang Yuepeng. A Method of Automatic Selection of Hash-Style Habitation with Spatial Distribution Characteristics Preserved[J].Journal of Geomatics Science and Technology, 2015, 32(6):626-630 (高凯, 杨敏, 张跃鹏. 保持空间分布特征的散列式居民地综合选取方法[J]. 测绘科学技术学报, 2015, 32(6):626-630)
[24] Miller H, Han J. Geographic Data Mining and Knowledge Discovery[M]. 2nd ed. New York:CRC, 2009
[25] Deng M, Liu Q, Cheng T, et al. An Adaptive Spatial Clustering Algorithm Based on Delaunay Triangulation[J]. Computers Environment & Urban Systems, 2011, 35(4):320-332
[26] Liu Q, Deng M, Shi Y, et al. A Density-Based Spatial Clustering Algorithm Considering Both Spatial Proximity and Attribute Similarity[J]. Compu- ters and Geosciences, 2012, 46(3):296-309
[27] Ester M, Kriegel H P, Sander J, et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]//Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. Portland: AAAI Press, 1996: 226-231
[28] He Dan, Chen Songcan. Robust Kernal-Based Fuzzy Clustering Using Difference of Convex Functions Programming[J].Pattern Recognition and Artificial Intelligence, 2016, 29(8):744-750 (贺丹, 陈松灿. 基于DC规划的鲁棒模糊核聚类算法[J]. 模式识别与人工智能, 2016, 29(8):744-750)
[29] Nasibov E N, Ulutagay G. Robustness of Density-Based Clustering Methods with Various Neighborhood Relations[J]. Fuzzy Sets and Systems, 2009, 160(24):3 601-3 615
[30] Guo Renzhong. Spatial Analysis[M]. 2nd ed. Beijing: Higher Education Press, 2001 (郭仁忠. 空间分析[M]. 2版.北京: 高等教育出版社, 2001)
[31] Yu Wenhao, Ai Tinghua, Yang Min, et al. Detecting “Hot Spots” of Facility POIs Based on Kernel Density Estimation and Spatial Autocorrelation Technique[J]. Geomatics and Information Science of Wuhan University, 2016, 41(2):221-227(禹文豪, 艾廷华, 杨敏,等. 利用核密度与空间自相关进行城市设施兴趣点分布热点探测[J]. 武汉大学学报·信息科学版, 2016, 41(2):221-227)