{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T02:45:47Z","timestamp":1768617947962,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41971353"],"award-info":[{"award-number":["41971353"]}]},{"name":"National Natural Science Foundation of China","award":["41730105"],"award-info":[{"award-number":["41730105"]}]},{"name":"National Natural Science Foundation of China","award":["2021JJ20058"],"award-info":[{"award-number":["2021JJ20058"]}]},{"name":"National Natural Science Foundation of China","award":["2020JJ4695"],"award-info":[{"award-number":["2020JJ4695"]}]},{"name":"National Natural Science Foundation of China","award":["KT202110"],"award-info":[{"award-number":["KT202110"]}]},{"name":"National Natural Science Foundation of China","award":["KT202002"],"award-info":[{"award-number":["KT202002"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["41971353"],"award-info":[{"award-number":["41971353"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["41730105"],"award-info":[{"award-number":["41730105"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2021JJ20058"],"award-info":[{"award-number":["2021JJ20058"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2020JJ4695"],"award-info":[{"award-number":["2020JJ4695"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["KT202110"],"award-info":[{"award-number":["KT202110"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["KT202002"],"award-info":[{"award-number":["KT202002"]}]},{"name":"water conservancy science and technology project of Guizhou Province","award":["41971353"],"award-info":[{"award-number":["41971353"]}]},{"name":"water conservancy science and technology project of Guizhou Province","award":["41730105"],"award-info":[{"award-number":["41730105"]}]},{"name":"water conservancy science and technology project of Guizhou Province","award":["2021JJ20058"],"award-info":[{"award-number":["2021JJ20058"]}]},{"name":"water conservancy science and technology project of Guizhou Province","award":["2020JJ4695"],"award-info":[{"award-number":["2020JJ4695"]}]},{"name":"water conservancy science and technology project of Guizhou Province","award":["KT202110"],"award-info":[{"award-number":["KT202110"]}]},{"name":"water conservancy science and technology project of Guizhou Province","award":["KT202002"],"award-info":[{"award-number":["KT202002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Identifying spatial communities in vehicle movements is vital for sensing human mobility patterns and urban structures. Spatial community detection has been proven to be an NP-Hard problem. Heuristic algorithms were widely used for detecting spatial communities. However, the spatial communities identified by existing heuristic algorithms are usually locally optimal and unstable. To alleviate these limitations, this study developed a hybrid heuristic algorithm by combining multi-level merging and consensus clustering. We first constructed a weighted spatially embedded network with road segments as vertices and the numbers of vehicle trips between the road segments as weights. Then, to jump out of the local optimum trap, a new multi-level merging approach, i.e., iterative local moving and global perturbation, was proposed to optimize the objective function (i.e., modularity) until a maximum of modularity was obtained. Finally, to obtain a representative and reliable spatial community structure, consensus clustering was performed to generate a more stable spatial community structure out of a set of community detection results. Experiments on Beijing taxi trajectory data show that the proposed method outperforms a state-of-the-art method, spatially constrained Leiden (Scleiden), because the proposed method can escape from the local optimum solutions and improve the stability of the identified spatial community structure. The spatial communities identified by the proposed method can reveal the polycentric structure and human mobility patterns in Beijing, which may provide useful references for human-centric urban planning.<\/jats:p>","DOI":"10.3390\/rs14174144","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T02:55:34Z","timestamp":1661309734000},"page":"4144","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Detecting Spatial Communities in Vehicle Movements by Combining Multi-Level Merging and Consensus Clustering"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4684-8504","authenticated-orcid":false,"given":"Qiliang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410006, China"}]},{"given":"Zhaoyi","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410006, China"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geo-Informatics, Central South University, Changsha 410006, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"ref_1","first-page":"881","article-title":"Analysis of human mobility patterns from GPS trajectories and contextual information","volume":"30","author":"Vandrol","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.compenvurbsys.2018.12.001","article-title":"Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling","volume":"74","author":"Liu","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.apgeog.2017.07.001","article-title":"Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data","volume":"86","author":"Zhu","year":"2017","journal-title":"Appl. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jtrangeo.2015.01.016","article-title":"Revealing travel patterns and city structure with taxi trip data","volume":"43","author":"Liu","year":"2015","journal-title":"J. Transp. Geogr."},{"key":"ref_5","first-page":"1","article-title":"Urban computing: Concepts, methodologies, and applications","volume":"5","author":"Zheng","year":"2014","journal-title":"ACM Trans. Intell. Syst. Technol. TIST"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1080\/00045608.2015.1018773","article-title":"Social sensing: A new approach to understanding our socioeconomic environments","volume":"105","author":"Liu","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1080\/13658816.2018.1434889","article-title":"Detecting spatial community structure in movements","volume":"32","author":"Guo","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1080\/13658816.2014.999244","article-title":"Finding community structure in spatially constrained complex networks","volume":"29","author":"Chen","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1111\/tgis.12722","article-title":"Network Optimization Approach to Delineating Health Care Service Areas: Spatially Constrained Louvain and Leiden Algorithms","volume":"25","author":"Wang","year":"2021","journal-title":"Trans. GIS"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1080\/13658816.2014.914521","article-title":"Detecting the dynamics of urban structure through spatial network analysis","volume":"28","author":"Zhong","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jtrangeo.2018.05.002","article-title":"Do different datasets tell the same story about urban mobility\u2014A comparative study of public transit and taxi usage","volume":"70","author":"Zhang","year":"2018","journal-title":"J. Transp. Geogr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.physa.2018.02.062","article-title":"Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach","volume":"501","author":"Lu","year":"2018","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","article-title":"Community detection in graphs","volume":"486","author":"Fortunato","year":"2010","journal-title":"Phys. Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100885","DOI":"10.1016\/j.swevo.2021.100885","article-title":"A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions","volume":"63","author":"Attea","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3172867","article-title":"Community discovery in dynamic networks: A survey","volume":"51","author":"Rossetti","year":"2018","journal-title":"ACM Comput. Surv."},{"key":"ref_16","first-page":"583","article-title":"Cluster ensembles-a knowledge reuse framework for combining multiple partitions","volume":"3","author":"Strehl","year":"2002","journal-title":"J. Mach. Learn. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"026113","DOI":"10.1103\/PhysRevE.69.026113","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"066111","DOI":"10.1103\/PhysRevE.70.066111","article-title":"Finding community structure in very large networks","volume":"70","author":"Clauset","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3091106","article-title":"Metrics for community analysis: A survey","volume":"50","author":"Chakraborty","year":"2017","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"026105","DOI":"10.1103\/PhysRevE.81.026105","article-title":"Edge ratio and community structure in networks","volume":"81","author":"Cafieri","year":"2010","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2658","DOI":"10.1073\/pnas.0400054101","article-title":"Defining and identifying communities in networks","volume":"101","author":"Radicchi","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1073\/pnas.0706851105","article-title":"Maps of random walks on complex networks reveal community structure","volume":"105","author":"Rosvall","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Rosvall, M., and Bergstrom, C.T. (2011). Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems. PLoS ONE, 6.","DOI":"10.1371\/journal.pone.0018209"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1051\/ro\/2015046","article-title":"Combined neighborhood tabu search for community detection in complex networks","volume":"50","author":"Gach","year":"2016","journal-title":"RAIRO-Oper. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"026130","DOI":"10.1103\/PhysRevE.80.026130","article-title":"Iterated tabu search for identifying community structure in complex networks","volume":"80","author":"Lu","year":"2009","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pons, P., and Latapy, M. (2005, January 26\u201328). Computing communities in large networks using random walks. Proceedings of the 20th International Symposium on Computer and Information Sciences, Istanbul, Turkey.","DOI":"10.1007\/11569596_31"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113184","DOI":"10.1016\/j.eswa.2020.113184","article-title":"Multiplex community detection in complex networks using an evolutionary approach","volume":"146","author":"Karimi","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.physa.2015.02.020","article-title":"An ant colony based algorithm for overlapping community detection in complex networks","volume":"427","author":"Zhou","year":"2015","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1111\/gean.12278","article-title":"A Spatial Scan Statistic to Detect Spatial Communities of Vehicle Movements on Urban Road Networks","volume":"54","author":"Liu","year":"2021","journal-title":"Geogr. Anal."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7663","DOI":"10.1073\/pnas.1018962108","article-title":"Uncovering space-independent communities in spatial networks","volume":"108","author":"Expert","year":"2011","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1111\/tgis.12042","article-title":"Discovering Spatial Interaction Communities from Mobile Phone Data","volume":"17","author":"Gao","year":"2013","journal-title":"Trans. GIS"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wan, Y., and Liu, Y. (2018). DASSCAN: A Density and Adjacency Expansion-Based Spatial Structural Community Detection Algorithm for Networks. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040159"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5233","DOI":"10.1038\/s41598-019-41695-z","article-title":"From Louvain to Leiden: Guaranteeing well-connected communities","volume":"9","author":"Traag","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1038\/srep00336","article-title":"Consensus clustering in complex networks","volume":"2","author":"Lancichinetti","year":"2012","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/S0968-090X(00)00026-7","article-title":"Some map matching algorithms for personal navigation assistants","volume":"8","author":"White","year":"2000","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Louren\u00e7o, H.R., Martin, O.C., and St\u00fctzle, T. (2003). Iterated local search. Handbook of Metaheuristics, Springer.","DOI":"10.1007\/0-306-48056-5_11"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"016110","DOI":"10.1103\/PhysRevE.74.016110","article-title":"Statistical mechanics of community detection","volume":"74","author":"Reichardt","year":"2006","journal-title":"Phys. Rev. E Stat. Nonlinear Soft Matter Phys."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"016104","DOI":"10.1103\/PhysRevE.77.016104","article-title":"Identifying network communities with a high resolution","volume":"77","author":"Ruan","year":"2008","journal-title":"Phys. Rev. E"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1137\/S003614450342480","article-title":"The structure and function of complex networks","volume":"45","author":"Newman","year":"2003","journal-title":"SIAM Rev."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"P09008","DOI":"10.1088\/1742-5468\/2005\/09\/P09008","article-title":"Comparing community structure identification","volume":"2005","author":"Danon","year":"2005","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2017.06.039","article-title":"Using multi-source geospatial big data to identify the structure of polycentric cities","volume":"202","author":"Cai","year":"2017","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4144\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:14:14Z","timestamp":1760141654000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4144"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,23]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174144"],"URL":"https:\/\/doi.org\/10.3390\/rs14174144","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,23]]}}}