A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection

References

• Anselin L. 1995. Local indicators of spatial association – LISA. Geogr Anal. 27(2):93–115.
   Web of Science ®Google Scholar
• Chang HW, Tai YC, Hsu JYJ. 2009. Context-aware taxi demand hotspots prediction. Int J Bus Intel Data Min. 5(1):3–18.
   Google Scholar
• El Mahrsi MK, Rossi F. 2012 Sep. Graph-based approaches to clustering network-constrained trajectory data. International workshop on new frontiers in mining complex patterns. Berlin, Heidelberg: Springer; p. 124–137.
   Google Scholar
• Ester M, Kriegel HP, Sander J, Xu X. 1996 Aug. A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd. 96(34):226–231.
   Google Scholar
• Ferdous R. 2009 Nov. An efficient k-means algorithm integrated with Jaccard distance measure for document clustering. First Asian Himalayas International Conference on Internet (AH-ICI). Kathmundu: IEEE; p. 1–6.
   Google Scholar
• Girvan M, Newman ME. 2002. Community structure in social and biological networks. Proc Nat Acad Sci. 99(12):7821–7826.10.1073/pnas.122653799
   PubMed Web of Science ®Google Scholar
• Gower JC, Ross GJS. 1969. Minimum spanning trees and single linkage cluster analysis. Appl Stat. 54–64.10.2307/2346439
   Google Scholar
• Han B, Liu L, Omiecinski E. 2015. Road-network aware trajectory clustering: integrating locality, flow, and density. IEEE Trans Mobile Comput. 14(2):416–429.
   Web of Science ®Google Scholar
• Huttenlocher DP, Klanderman GA, Rucklidge WJ. 1993. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 15(9):850–863.10.1109/34.232073
   Web of Science ®Google Scholar
• Jaccard P, 1901. Comparative study of floral distribution in a portion of the Alps and Jura. Bull Vaud S Nat Sci. 37:547–579.
   Google Scholar
• Jiang B. 2013. Head/tail breaks: a new classification scheme for data with a heavy-tailed distribution. Prof Geogr. 65(3):482–494.10.1080/00330124.2012.700499
   Web of Science ®Google Scholar
• Jiang B. 2016. Head/tail breaks for visualization of city structure and dynamics. European Handbook of Crowdsourced Geographic: Information, p. 169–183.10.5334/bax
   Google Scholar
• Jiang B, Liu X. 2012. Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information. Int J Geogr Inf Sci. 26(2):215–229.10.1080/13658816.2011.575074
   Web of Science ®Google Scholar
• Li B, Zhang D, Sun L, Chen C, Li S, Qi G, Yang Q. 2011 Mar. Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). Seattle (WA): IEEE; p. 63–68. 10.1109/PERCOMW.2011.5766967
   Google Scholar
• Liu X, Ban Y, 2013. Uncovering spatio-temporal cluster patterns using massive floating car data.ISPRS Int J Geo-Inf, 2(2): 371–384. 10.3390/ijgi2020371
   Web of Science ®Google Scholar
• Mohaymany AS, Shahri M, Mirbagheri B. 2013. GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-spatial Inf Sci. 16(2):113–119.10.1080/10095020.2013.766396
   Google Scholar
• Moons E, Brijs T, Wets G. 2009. Improving Moran’s index to identify hot spots in traffic safety. Geocomput Urban Plann. 117–132.10.1007/978-3-540-89930-3
   Google Scholar
• Nie K, Wang Z, Du Q, Ren F, Tian Q. 2015. A network-constrained integrated method for detecting spatial cluster and risk location of traffic crash: A case study from Wuhan, China. Sustainability. 7(3):2662–2677.10.3390/su7032662
   Web of Science ®Google Scholar
• Okabe A, Satoh T, Sugihara K. 2009. A kernel density estimation method for networks, its computational method and a GIS-based tool. Int J Geogr Inf Sci. 23(1):7–32.10.1080/13658810802475491
   Web of Science ®Google Scholar
• Patel V, Mehta R. 2012. Data clustering: integrating different distance measures with modified k-means algorithm. Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011); Dec 20–22, 2011; Berlin, Heidelberg: Springer. p. 691–700.10.1007/978-81-322-0491-6
   Google Scholar
• Pei T, Wang W, Zhang H, Ma T, Du Y, Zhou C. 2015. Density-based clustering for data containing two types of points. Int J Geogr Inf Sci. 29(2):175–193.10.1080/13658816.2014.955027
   Web of Science ®Google Scholar
• Rosvall M, Bergstrom CT. 2008. Maps of random walks on complex networks reveal community structure. Proc Nat Acad Sci. 105(4):1118–1123.10.1073/pnas.0706851105
   PubMed Web of Science ®Google Scholar
• Rui Y, Yang Z, Qian T, Khalid S, Xia N, Wang J. 2016. Network-constrained and category-based point pattern analysis for Suguo retail stores in Nanjing, China. Int J Geogr Inf Sci. 30(2):186–199.10.1080/13658816.2015.1080829
   Web of Science ®Google Scholar
• Scholz RW, Lu Y. 2014. Detection of dynamic activity patterns at a collective level from large-volume trajectory data. Int J Geogr Inf Sci. 28(5):946–963.10.1080/13658816.2013.869819
   Web of Science ®Google Scholar
• Shannon CE and Weaver W. 1998. The mathematical theory of communication. University of Illinois Press.
   Google Scholar
• Shen Y, Zhao L, Fan J. 2015. Analysis and visualization for hot spot based route recommendation using short-dated taxi GPS traces. Information. 6(2):134–151.10.3390/info6020134
   Google Scholar
• Shen J, Liu X, Chen M. 2017. Discovering spatial and temporal patterns from taxi-based Floating Car Data: a case study from Nanjing. GISci Remote Sens. 54(5):617–638.
   Web of Science ®Google Scholar
• Steenberghen T, Dufays T, Thomas I, Flahaut B. 2004. Intra-urban location and clustering of road accidents using GIS: a Belgian example. Int J Geogr Inf Sci. 18(2):169–181.10.1080/13658810310001629619
   Web of Science ®Google Scholar
• Steenberghen T, Aerts K, Thomas I. 2010. Spatial clustering of events on a network. J Transport Geogr. 18(3):411–418.10.1016/j.jtrangeo.2009.08.005
   Web of Science ®Google Scholar
• Tang L, Kan Z, Zhang X, Sun F, Yang X, Li Q. 2016. A network Kernel Density Estimation for linear features in space–time analysis of big trace data. Int J Geogr Inf Sci. 30(9):1717–1737.10.1080/13658816.2015.1119279
   Web of Science ®Google Scholar
• Xie Z, Yan J. 2008. Kernel density estimation of traffic accidents in a network space. Comput Environ Urban Syst. 32(5):396–406.10.1016/j.compenvurbsys.2008.05.001
   Web of Science ®Google Scholar
• Xie Z, Yan J. 2013. Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. J Transport Geogr. 31:64–71.10.1016/j.jtrangeo.2013.05.009
   Web of Science ®Google Scholar
• Yamada I, Thill JC. 2010. Local indicators of network-constrained clusters in spatial patterns represented by a link attribute. Annal Assoc Am Geogr. 100(2):269–285.10.1080/00045600903550337
   Web of Science ®Google Scholar
• Yu W, Ai T, Liu P, He Y. 2015. Network kernel density estimation for the analysis of facility POI hotspots. Acta Geodaetica et Cartographica Sinica. 44(12):1378–1383.
   Google Scholar
• Yue Y, Zhuang Y, Li Q and Mao Q. 2009 Aug. Mining time-dependent attractive areas and movement patterns from taxi trajectory data. 17th International Conference on Geoinformatics, 2009; Fairfax, VA: IEEE; p. 1–6.
   Google Scholar
• Zhao P, Qin K, Ye X, Wang Y, Chen Y. 2017. A trajectory clustering approach based on decision graph and data field for detecting hotspots. Int J Geogr Inf Sci. 31(6):1101–1127.
   Web of Science ®Google Scholar