Length-squared L-function for identifying clustering pattern of network-constrained flows

References

• Adrienko, N., and G. Adrienko. 2011. “Spatial Generalization and Aggregation of Massive Movement Data.” IEEE Transactions on Visualization and Computer Graphics 17 (2): 205–19. https://doi.org/10.1109/TVCG.2010.44.
   PubMed Web of Science ®Google Scholar
• Berglund, Svante, and Anders Karlström. 1999. “Identifying Local Spatial Association in Flow Data.” Journal of Geographical Systems 1 (3): 219–36. https://doi.org/10.1007/s101090050013.
   Google Scholar
• Besag, Julian E. 1977. “Comment on'Modeling Spatial Patterns’ by BD Ripley.” Journal of the Royal Statistical Society: Series B (Methodological) 39 (2): 192–212. https://doi.org/10.1111/j.2517-6161.1977.tb01616.x.
   Google Scholar
• Chen, Zhenhua, Zhaoya Gong, Shan Yang, Qiwei Ma, and Changcheng Kan. 2020. “Impact of Extreme Weather Events on Urban Human Flow: A Perspective from Location-Based Service Data.” Computers, Environment and Urban Systems 83: 101520. https://doi.org/10.1016/j.compenvurbsys.2020.101520.
   PubMed Web of Science ®Google Scholar
• Chen, Chao, Daqing Zhang, Zhi-Hua Zhou, Nan Li, Tülin Atmaca, and Shijian Li. 2013. “B-Planner: Night bus Route Planning Using Large-Scale Taxi GPS Traces.” Paper Presented at the 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), 18-22 March 2013.
   Google Scholar
• Dijkstra, Edsger W. 1959. “A Note on two Problems in Connexion with Graphs.” Numerische Mathematik 1 (1): 269–71. https://doi.org/10.1007/BF01386390.
   Google Scholar
• Doantam, Phan, Xiao Ling, R. Yeh, and P. Hanrahan. 2005. “Flow map Layout”. Paper Presented at the IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., 23-25 Oct. 2005.
   Google Scholar
• Fang, Zidong, Ci Song, Hua Shu, Jie Chen, Tianyu Liu, Xi Wang, Xiao Chen, Xiaorui Yan, and Tao Pei. 2023. “Length L-Function for Network-Constrained Point Data.” Transactions in GIS 27 (2): 476–93. https://doi.org/10.1111/tgis.13035.
   Web of Science ®Google Scholar
• Gao, Yizhao, Ting Li, Shaowen Wang, Myeong-Hun Jeong, and Kiumars Soltani. 2018. “A Multidimensional Spatial Scan Statistics Approach to Movement Pattern Comparison.” International Journal of Geographical Information Science 32 (7): 1304–25. https://doi.org/10.1080/13658816.2018.1426859.
   Web of Science ®Google Scholar
• Guo, Diansheng, Xi Zhu, Hai Jin, Peng Gao, and Clio Andris. 2012. “Discovering Spatial Patterns in Origin-Destination Mobility Data.” Transactions in GIS 16 (3): 411–29. https://doi.org/10.1111/j.1467-9671.2012.01344.x.
   Web of Science ®Google Scholar
• Haase, Peter. 1995. “Spatial Pattern Analysis in Ecology Based on Ripley's K-Function: Introduction and Methods of Edge Correction.” Journal of Vegetation Science 6 (4): 575–82. https://doi.org/10.2307/3236356.
   Web of Science ®Google Scholar
• Hohl, Alexander, Wenwu Tang, Irene Casas, Xun Shi, and Eric Delmelle. 2022. “Detecting Space–Time Patterns of Disease Risk Under Dynamic Background Population.” Journal of Geographical Systems 24 (3): 389–417. https://doi.org/10.1007/s10109-022-00377-7.
   PubMed Web of Science ®Google Scholar
• Jiang, Jingyu, Xi Wang, Tianyu Liu, Zidong Fang, Tao Pei, Ci Song, and Peijun Du. 2022. “Topological Relationship Model for Geographical Flows.” Cartography and Geographic Information Science, 1–17. https://doi.org/10.1080/15230406.2022.2104377.
   Web of Science ®Google Scholar
• Kan, Zihan, Mei-Po Kwan, and Luliang Tang. 2021. “Ripley’s K-Function for Network-Constrained Flow Data.” Geographical Analysis 54 (4): 769–88. https://doi.org/10.1111/gean.12300.
   Web of Science ®Google Scholar
• Kiskowski, Maria A., John F. Hancock, and Anne K. Kenworthy. 2009. “On the Use of Ripley's K-Function and Its Derivatives to Analyze Domain Size.” Biophysical Journal 97 (4): 1095–103. https://doi.org/10.1016/j.bpj.2009.05.039.
   PubMed Web of Science ®Google Scholar
• Lee, Jae-Gil, Jiawei Han, and Kyu-Young Whang. 2007. “Trajectory Clustering: A Partition-and-Group Framework.” Proceedings of the ACM SIGMOD International Conference on Management of Data, https://doi.org/10.1145/1247480.1247546.
   Google Scholar
• Liu, Yu, Daoqin Tong, and Xi Liu. 2015. “Measuring Spatial Autocorrelation of Vectors.” Geographical Analysis 47 (3): 300–19. https://doi.org/10.1111/gean.12069.
   Web of Science ®Google Scholar
• Lu, Yongmei, and Xuwei Chen. 2007. “On the False Alarm of Planar K-Function When Analyzing Urban Crime Distributed Along Streets.” Social Science Research 36 (2): 611–32. https://doi.org/10.1016/j.ssresearch.2006.05.003.
   Web of Science ®Google Scholar
• Nanni, Mirco, and Dino Pedreschi. 2006. “Time-focused Clustering of Trajectories of Moving Objects.” Journal of Intelligent Information Systems 27 (3): 267–89. https://doi.org/10.1007/s10844-006-9953-7.
   Web of Science ®Google Scholar
• Okabe, Atsuyuki, and Kokichi Sugihara. 2012. “Modeling Spatial Events on and Alongside Networks.” In Spatial Analysis Along Networks, 25–44. John Wiley & Sons.
   Google Scholar
• Okabe, Atsuyuki, and Ikuho Yamada. 2001. “The K-Function Method on a Network and Its Computational Implementation.” Geographical Analysis 33 (3): 271–90. https://doi.org/10.1111/j.1538-4632.2001.tb00448.x.
   Web of Science ®Google Scholar
• Pei, Tao, Weiyi Wang, Hengcai Zhang, Ting Ma, Yunyan Du, and Chenghu Zhou. 2015. “Density-based Clustering for Data Containing two Types of Points.” International Journal of Geographical Information Science 29 (2): 175–93. https://doi.org/10.1080/13658816.2014.955027.
   Web of Science ®Google Scholar
• Pei, Tao, A. Xing Zhu, Chenghu Zhou, Baolin Li, and Chengzhi Qin. 2007. “Delineation of Support Domain of Feature in the Presence of Noise.” Computers & Geosciences 33 (7): 952–65. https://doi.org/10.1016/j.cageo.2006.11.010.
   Web of Science ®Google Scholar
• Ripley, B. D. 1976. “The Second-Order Analysis of Stationary Point Processes.” Journal of Applied Probability 13 (2): 255–66. https://doi.org/10.2307/3212829.
   Web of Science ®Google Scholar
• Shu, Hua, Tao Pei, Ci Song, Xiao Chen, Sihui Guo, Yaxi Liu, Jie Chen, Xi Wang, and Chenghu Zhou. 2021. “L-Function of Geographical Flows.” International Journal of Geographical Information Science 35 (4): 689–716. https://doi.org/10.1080/13658816.2020.1749277.
   Web of Science ®Google Scholar
• Song, Ci, Tao Pei, Ting Ma, Yunyan Du, Hua Shu, Sihui Guo, and Zide Fan. 2019. “Detecting Arbitrarily Shaped Clusters in Origin-Destination Flows Using ant Colony Optimization.” International Journal of Geographical Information Science 33 (1): 134–54. https://doi.org/10.1080/13658816.2018.1516287.
   Web of Science ®Google Scholar
• Sterner, Robert W., Christine A. Ribic, and George E. Schatz. 1986. “Testing for Life Historical Changes in Spatial Patterns of Four Tropical Tree Species.” The Journal of Ecology 74 (3): 621–33. https://doi.org/10.2307/2260386.
   Web of Science ®Google Scholar
• Tao, Ran, and Jean-Claude Thill. 2016. “Spatial Cluster Detection in Spatial Flow Data.” Geographical Analysis 48 (4): 355–72. https://doi.org/10.1111/gean.12100.
   Web of Science ®Google Scholar
• Tao, Ran, Jean-Claude Thill, and Ikuho Yamada. 2015. “Detecting Clustering Scales with the Incremental K-Function: Comparison Tests on Actual and Simulated Geospatial Datasets.” In Information Fusion and Geographic Information Systems (IF&GIS’ 2015): Deep Virtualization for Mobile GIS, edited by Vasily Popovich, Christophe Claramunt, Manfred Schrenk, Kyrill Korolenko, and Jérôme Gensel, 93–107. Cham: Springer International Publishing.
   Google Scholar
• Wood, Jo, Jason Dykes, and Aidan Slingsby. 2010. “Visualisation of Origins, Destinations and Flows with OD Maps.” The Cartographic Journal 47 (2): 117–29. https://doi.org/10.1179/000870410X12658023467367.
   Web of Science ®Google Scholar
• Yamada, Ikuho, and Jean-Claude Thill. 2004. “Comparison of Planar and Network K-Functions in Traffic Accident Analysis.” Journal of Transport Geography 12 (2): 149–58. https://doi.org/10.1016/j.jtrangeo.2003.10.006.
   Google Scholar
• Yamada, Ikuho, and Jean-Claude Thill. 2007. “Local Indicators of Network-Constrained Clusters in Spatial Point Patterns.” Geographical Analysis 39 (3): 268–92. https://doi.org/10.1111/j.1538-4632.2007.00704.x.
   Web of Science ®Google Scholar
• Yan, Xiaorui, Tao Pei, Hua Shu, Ci Song, Mingbo Wu, Zidong Fang, and Jie Chen. 2023a. “Spatiotemporal Flow L-Function: A new Method for Identifying Spatiotemporal Clusters in Geographical Flow Data.” International Journal of Geographical Information Science, https://doi.org/10.1080/13658816.2023.2204345. inpress.
   PubMed Web of Science ®Google Scholar
• Yan, Xiaorui, Tao Pei, Ci Song, and Xiaohan Liu. 2023b. “Estimating Spatiotemporal Aggregation Scales by Revisiting the Spatiotemporal L-Function.” Transactions in GIS 27 (2): 592–604. https://doi.org/10.1111/tgis.13034.
   Web of Science ®Google Scholar
• Zhao, Pengxiang, Xintao Liu, Wenzhong Shi, Tao Jia, Wengen Li, and Min Chen. 2020. “An Empirical Study on the Intra-Urban Goods Movement Patterns Using Logistics big Data.” International Journal of Geographical Information Science 34 (6): 1089–116. https://doi.org/10.1080/13658816.2018.1520236.
   Web of Science ®Google Scholar
• Zhu, Xi, and Diansheng Guo. 2014. “Mapping Large Spatial Flow Data with Hierarchical Clustering.” Transactions in GIS 3: 421–35. https://doi.org/10.1111/tgis.12100.
   Google Scholar
• Zhu, Bing, Qixing Huang, Leonidas Guibas, and Lin Zhang. 2013. “Urban Population Migration Pattern Mining Based on Taxi Trajectories.” Paper Presented at the 3rd International Workshop on Mobile Sensing: The Future, Brought to you by big Sensor Data, Philadelphia, USA.
   Google Scholar