Modelling and application of a spectral clustering method for shared bicycle trajectories

References

• Aïmeur E, Brassard G, Gambs S. 2007. Quantum clustering algorithms. Proceedings of the 24th International Conference on Machine Learning; June 20–24; Oregon State University. New York, NY: Association for Computing Machinery; p. 1–8. doi: 10.1145/1273496.1273497.
   Google Scholar
• Alemayehu TF, Bitsuamlak GT. 2022. Autonomous urban topology generation for urban flow modelling. Sustain Cities Soc. 87:104181. doi: 10.1016/j.scs.2022.104181.
   Web of Science ®Google Scholar
• Bäcklund H, Hedblom A, Neijman N. 2011. A density-based spatial clustering of application with noise. Data Min TNM033. 33:11–30.
   Google Scholar
• Bagirov AM, Aliguliyev RM, Sultanova N. 2023. Finding compact and well-separated clusters: clustering using silhouette coefficients. Pattern Recognit. 135:109144. doi: 10.1016/j.patcog.2022.109144.
   Web of Science ®Google Scholar
• Berglund S, Karlstrom A. 1999. Identifying local spatial association in flow data. J Geograph Syst. 1(3):219–236. doi: 10.1007/s101090050013.
   Google Scholar
• Birant D, Kut A. 2007. ST-DBSCAN: an algorithm for clustering spatial–temporal data. Data Knowledge Eng. 60(1):208–221. doi: 10.1016/j.datak.2006.01.013.
   Web of Science ®Google Scholar
• Bureva V, Traneva V, Zoteva D, Tranev S. 2021. Generalized net model simulation of cluster analysis using CLIQUE: clustering in quest. In: Dimov I, Fidanova S, editors. Advances in high performance computing. Berlin, Germany: Springer International Publishing; p. 48–60.
   Google Scholar
• Cai J, Kwan MP. 2022. Discovering co-location patterns in multivariate spatial flow data. Int J Geograph Inform Sci. 36(4):720–748. doi: 10.1080/13658816.2021.1980217.
   Web of Science ®Google Scholar
• Charreire H, Weber C, Chaix B, Salze P, Casey R, Banos A, Badariotti D, Kesse-Guyot E, Hercberg S, Simon C, et al. 2012. Identifying built environmental patterns using cluster analysis and GIS: relationships with walking, cycling and body mass index in French adults. Int J Behav Nutr Phys Act. 9(1):1. doi: 10.1186/1479-5868-9-59.
   PubMedGoogle Scholar
• Dempster AP, Laird NM, Rubin DB. 1977. Maximum likelihood from incomplete data via the EM algorithm. J Royal Stat Soc B (Methodological). 39(1):1–22. doi: 10.1111/j.2517-6161.1977.tb01600.x.
   Web of Science ®Google Scholar
• Du KL. 2010. Clustering: a neural network approach. Neural Netw. 23(1):89–107. doi: 10.1016/j.neunet.2009.08.007.
   PubMed Web of Science ®Google Scholar
• Dunn JC. 1974. Well-separated clusters and optimal fuzzy partitions. J Cybernet. 4(1):95–104. doi: 10.1080/01969727408546059.
   Google Scholar
• Fang M, Tang L, Kan Z, Yang X, Pei T, Li Q, Li C. 2021. An adaptive origin-destination flows cluster-detecting method to identify urban mobility trends. arXiv preprint arXiv:2106.05436. doi: 10.48550/arXiv.2106.05436.
   Google Scholar
• Fotheringham AS. 1984. Spatial flows and spatial patterns. Environ Plan A. 16(4):529–543. doi: 10.1068/a160529.
   Web of Science ®Google Scholar
• Gallego FJ, Batista F, Rocha C, Mubareka S. 2011. Disaggregating population density of the European Union with CORINE land cover. Int J Geograph Inform Sci. 25(12):2051–2069. doi: 10.1080/13658816.2011.583653.
   Web of Science ®Google Scholar
• Gong P, Chen B, Li X, Liu H, Wang J, Bai Y, Chen J, Chen X, Fang L, Feng S, et al. 2020. Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Sci Bull. 65(3):182–187. doi: 10.1016/j.scib.2019.12.007.
   PubMed Web of Science ®Google Scholar
• Graser A, Schmidt J, Roth F, Brändle N. 2019. Untangling origin-destination flows in geographic information systems. Inf Vis. 18(1):153–172. doi: 10.1177/1473871617738122.
   Web of Science ®Google Scholar
• Guo B, Pei T, Song C, Shu H, Wu M, Guo S, Jiang J, Du P. 2023. Trend surface analysis of geographic flows. Int J Geograph Inform Sci. 37(1):118–137. doi: 10.1080/13658816.2022.2129660.
   Web of Science ®Google Scholar
• Guo D, Zhu X, Jin H, Gao P, Andris C. 2012. Discovering spatial patterns in origin-destination mobility data. Trans GIS. 16(3):411–429. doi: 10.1111/j.1467-9671.2012.01344.x.
   Web of Science ®Google Scholar
• Huang Z. 1998. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min Knowledge Discov. 2(3):283–304. doi: 10.1023/A:1009769707641.
   Web of Science ®Google Scholar
• Jia H, Ding S, Xu X, Nie R. 2014. The latest research progress on spectral clustering. Neural Comput & Applic. 24(7–8):1477–1486. doi: 10.1007/s00521-013-1439-2.
   Web of Science ®Google Scholar
• Karim MR, Beyan O, Zappa A, Costa IG, Rebholz-Schuhmann D, Cochez M, Decker S. 2021. Deep learning-based clustering approaches for bioinformatics. Brief Bioinform. 22(1):393–415. doi: 10.1093/bib/bbz170.
   PubMed Web of Science ®Google Scholar
• Kermani S, Samadzadehaghdam N, EtehadTavakol M. 2015. Automatic color segmentation of breast infrared images using a Gaussian mixture model. Optik. 126(21):3288–3294. doi: 10.1016/j.ijleo.2015.08.007.
   Web of Science ®Google Scholar
• Kim J, Jung I. 2017. Evaluation of the gini coefficient in spatial scan statistics for detecting irregularly shaped clusters. PLoS One. 12(1):e0170736. doi: 10.1371/journal.pone.0170736.
   PubMed Web of Science ®Google Scholar
• Lee JG, Han J, Whang KY. 2007. Trajectory clustering: a partition-and-group framework. Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data; June 11–14; Beijing, China. New York, NY: Association for Computing Machinery; p. 593–604. doi: 10.1145/1247480.1247546.
   Google Scholar
• LeSage JP, Fischer MM. 2010. Spatial econometric methods for modeling origin-destination flows. In: Fischer MM, Getis A, editors. Handbook of applied spatial analysis: software tools, methods and applications. Berlin, Germany: Springer; p. 409–433.
   Google Scholar
• Li W, Wang S, Zhang X, Jia Q, Tian Y. 2020. Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories. Int J Geograph Inform Sci. 34(12):2451–2474. doi: 10.1080/13658816.2020.1712401.
   Web of Science ®Google Scholar
• Liu Q, Yang J, Deng M, Song C, Liu W. 2021. SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows. Int J Geograph Inform Sci. 36(2):253–279. doi: 10.1080/13658816.2021.1899184.
   Web of Science ®Google Scholar
• Liu Y, Tong D, Liu X. 2015. Measuring spatial autocorrelation of vectors. Geog Anal. 47(3):300–319. doi: 10.1111/gean.12069.
   Web of Science ®Google Scholar
• Lorenzo L, Arroyo J. 2022. Analysis of the cryptocurrency market using different prototype-based clustering techniques. Financ Innov. 8(1):7. doi: 10.1186/s40854-021-00310-9.
   Web of Science ®Google Scholar
• Lorenzo L, Arroyo J. 2023. Online risk-based portfolio allocation on subsets of crypto assets applying a prototype-based clustering algorithm. Financ Innov. 9(1):25. doi: 10.1186/s40854-022-00438-2.
   Web of Science ®Google Scholar
• Lu Y, Wang L, Lu J, Yang J, Shen C. 2014. Multiple kernel clustering based on centered kernel alignment. Pattern Recogn. 47(11):3656–3664. doi: 10.1016/j.patcog.2014.05.005.
   Web of Science ®Google Scholar
• Ma D, Tanizaki H. 2022. Intraday patterns of price clustering in Bitcoin. Financ Innov. 8(1):4. doi: 10.1186/s40854-021-00307-4.
   Web of Science ®Google Scholar
• Markou MT, Kassomenos P. 2010. Cluster analysis of five years of back trajectories arriving in Athens, Greece. Atmos Res. 98(2–4):438–457. doi: 10.1016/j.atmosres.2010.08.006.
   Web of Science ®Google Scholar
• Nascimento MCV, de Carvalho ACPLF. 2011. Spectral methods for graph clustering – A survey. Eur J Oper Res. 211(2):221–231. doi: 10.1016/j.ejor.2010.08.012.
   Web of Science ®Google Scholar
• Oyelade J, Isewon I, Oladipupo O, Emebo O, Omogbadegun Z, Aromolaran O, Uwoghiren E, Olaniyan D, Olawole O. 2019. Data clustering: algorithms and its applications. 2019 19th International Conference on Computational Science and Its Applications (ICCSA); July 1–4; St. Petersburg, Russia. Piscataway, NJ: Institute of Electrical and Electronics Engineers; p. 71–81. doi: 10.1109/ICCSA.2019.000-1.
   Google Scholar
• Pei T, Shu H, Guo S, Song C, Chen J, Liu Y, Wang X. 2020. The concept and classification of spatial patterns of geographical flow. J Geo- Inform Sci. 22(1):30–40. doi: 10.12082/dqxxkx.2020.190736.
   Google Scholar
• Qiao D, Yang X, Liang Y, Hao X. 2022. Rapid trajectory clustering based on neighbor spatial analysis. Pattern Recog Lett. 156:167–173. doi: 10.1016/j.patrec.2022.03.010.
   Web of Science ®Google Scholar
• Ros F, Riad R, Guillaume S. 2023. PDBI: a partitioning Davies-Bouldin index for clustering evaluation. Neurocomputing. 528:178–199. doi: 10.1016/j.neucom.2023.01.043.
   Web of Science ®Google Scholar
• Rousseeuw PJ. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math. 20:53–65. doi: 10.1016/0377-0427(87)90125-7.
   Web of Science ®Google Scholar
• Shen P, Ouyang L, Wang C, Shi Y, Su Y. 2020. Cluster and characteristic analysis of Shanghai metro stations based on metro card and land-use data. Geo-Spat Inf Sci. 23(4):352–361. doi: 10.1080/10095020.2020.1846463.
   Google Scholar
• Shi C, Wei B, Wei S, Wang W, Liu H, Liu J. 2021. A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm. J Wireless Com Netw. 2021(1):31. doi: 10.1186/s13638-021-01910-w.
   Web of Science ®Google Scholar
• Shu H, Pei T, Song C, Chen X, Guo S, Liu Y, Chen J, Wang X, Zhou C. 2021. L-function of geographical flows. Int J Geograph Inform Sci. 35(4):689–716. doi: 10.1080/13658816.2020.1749277.
   Web of Science ®Google Scholar
• Smyth P. 2000. Model selection for probabilistic clustering using cross-validated likelihood. Stat Comput. 10(1):63–72. doi: 10.1023/A:1008940618127.
   Web of Science ®Google Scholar
• Song C, Pei T, Ma T, Du Y, Shu H, Guo S, Fan Z. 2019. Detecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization. Int J Geograph Inform Sci. 33(1):134–154. doi: 10.1080/13658816.2018.1516287.
   Web of Science ®Google Scholar
• Tao R, Thill JC. 2019. Flow Cross K-function: a bivariate flow analytical method. Int J Geograph Inform Sci. 33(10):2055–2071. doi: 10.1080/13658816.2019.1608362.
   Web of Science ®Google Scholar
• Tao R, Thill JC. 2020. BiFlowLISA: measuring spatial association for bivariate flow data. Comput Environ Urban Syst. 83:101519. doi: 10.1016/j.compenvurbsys.2020.101519.
   Web of Science ®Google Scholar
• Tao R, Thill JC, Depken C, Kashiha M. 2017. FlowHDBSCAN: a hierarchical and density-based spatial flow clustering method. Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics; November 7–10; Redondo Beach CA USA. New York, NY: Association for Computing Machinery; p. 1–8.
   Google Scholar
• Tao R, Thill JC. 2016. Spatial cluster detection in spatial flow data. Geog Anal. 48(4):355–372. doi: 10.1111/gean.12100.
   Web of Science ®Google Scholar
• Tran TN, Wehrens R, Buydens LMC. 2006. KNN-kernel density-based clustering for high-dimensional multivariate data. Comput Stat Data Anal. 51(2):513–525. doi: 10.1016/j.csda.2005.10.001.
   Web of Science ®Google Scholar
• Wang T, Ni S, Qin T, Cao D. 2022. TransGAT: a dynamic graph attention residual networks for traffic flow forecasting. Sustain Comput Inform Syst. 36:100779. doi: 10.1016/j.suscom.2022.100779.
   Web of Science ®Google Scholar
• Yan X, Pei T, Shu H, Song C, Wu M, Fang Z, Chen J. 2023. Spatiotemporal flow L-function: a new method for identifying spatiotemporal clusters in geographical flow data. Int J Geograph Inform Sci. 37(7):1615–1639. doi: 10.1080/13658816.2023.2204345.
   Web of Science ®Google Scholar
• Yang MS. 1993. A survey of fuzzy clustering. Math Comput Modell. 18(11):1–16. doi: 10.1016/0895-7177(93)90202-A.
   Google Scholar
• Zheng Z, Ling X, Wang P, Xiao J, Zhang F. 2020. Hybrid model for predicting anomalous large passenger flow in urban metros. IET Intell Transp Syst. 14(14):1987–1996. doi: 10.1049/iet-its.2020.0054.
   Web of Science ®Google Scholar
• Zhao P, Liu X, Shen J, Chen M. 2019. A network distance and graph-partitioning-based clustering method for improving the accuracy of urban hotspot detection. Geocarto Int. 34(3):293–315. doi: 10.1080/10106049.2017.1404140.
   Web of Science ®Google Scholar
• Zhou M, Yang M, Chen Z. 2023. Flow colocation quotient: measuring bivariate spatial association for flow data. Comput Environ Urban Syst. 99:101916. doi: 10.1016/j.compenvurbsys.2022.101916.
   Web of Science ®Google Scholar
• Zhou Z, Si G, Sun H, Qu K, Hou W. 2022. A robust clustering algorithm based on the identification of core points and KNN kernel density estimation. Expert Syst Appl. 195:116573. doi: 10.1016/j.eswa.2022.116573.
   Web of Science ®Google Scholar
• Zhu X, Guo D. 2014. Mapping large spatial flow data with hierarchical clustering. Trans GIS. 18(3):421–435. doi: 10.1111/tgis.12100.
   Web of Science ®Google Scholar
• Zhu X, Guo D, Koylu C, Chen C. 2019. Density-based multi-scale flow mapping and generalization. Comput Environ Urban Syst. 77:101359. doi: 10.1016/j.compenvurbsys.2019.101359.
   Web of Science ®Google Scholar
• Zou Q, Lin G, Jiang X, Liu X, Zeng X. 2020. Sequence clustering in bioinformatics: an empirical study. Brief Bioinform. 21(1):1–10. doi: 10.1093/bib/bby090.
   PubMed Web of Science ®Google Scholar