{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T18:57:35Z","timestamp":1766516255734,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T00:00:00Z","timestamp":1537833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Knowledge discovery about people and cities from emerging location data has been an active research field but is still relatively unexplored. In recent years, a considerable amount of work has been developed around the use of social media data, most of which focusses on mining the content, with comparatively less attention given to the location information. Furthermore, what aggregated scale spatial patterns show still needs extensive discussion. This paper proposes a tweet-topic-function-structure framework to reveal spatial patterns from individual tweets at aggregated spatial levels, combining an unsupervised learning algorithm with spatial measures. Two-year geo-tweets collected in Greater London were analyzed as a demonstrator of the framework and as a case study. The results indicate, at a disaggregated level, that the distribution of topics possess a fair degree of spatial randomness related to tweeting behavior. When aggregating tweets by zones, the areas with the same topics form spatial clusters but of entangled urban functions. Furthermore, hierarchical clustering generates a clear spatial structure with orders of centers. Our work demonstrates that although uncertainties exist, geo-tweets should still be a useful resource for informing spatial planning, especially for the strategic planning of economic clusters.<\/jats:p>","DOI":"10.3390\/ijgi7100386","type":"journal-article","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T11:12:26Z","timestamp":1537873946000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Profiling the Spatial Structure of London: From Individual Tweets to Aggregated Functional Zones"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3582-1266","authenticated-orcid":false,"given":"Chen","family":"Zhong","sequence":"first","affiliation":[{"name":"Department of Geography, King\u2019s College London, London WC2R 2LS, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7801-2517","authenticated-orcid":false,"given":"Shi","family":"Zeng","sequence":"additional","affiliation":[{"name":"Centre for Advanced Spatial Analysis, University College London, London WC1E 6BT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0255-4037","authenticated-orcid":false,"given":"Wei","family":"Tu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Information Smart Sensing and Services, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0735-1116","authenticated-orcid":false,"given":"Mitsuo","family":"Yoshida","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Aichi 441-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sakaki, T., Okazaki, M., and Matsuo, Y. (2010, January 26\u201330). Earthquake Shakes Twitter Users: Real-Time Event Detection by Social Sensors. Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, USA.","DOI":"10.1145\/1772690.1772777"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Simon, T., Goldberg, A., Aharonson-Daniel, L., Leykin, D., and Adini, B. (2014). Twitter in the cross fire\u2014The use of social media in the Westgate Mall terror attack in Kenya. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0104136"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"44052","DOI":"10.1038\/srep44052","article-title":"Individual movement strategies revealed through novel clustering of emergent movement patterns","volume":"7","author":"Valle","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Maeda, T.N., Yoshida, M., Toriumi, F., and Ohashi, H. (2018). Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7030099"},{"key":"ref_5","first-page":"30","article-title":"Assessing the usability of georeferenced tweets for the extraction of travel patterns: A case study for Austria and Florida","volume":"2014","author":"Hochmair","year":"2014","journal-title":"GI_Forum"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1080\/13658816.2017.1282615","article-title":"Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data","volume":"31","author":"Yin","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Ratti, C., Sobolevsky, S., Calabrese, F., Andris, C., Reades, J., Martino, M., Claxton, R., and Strogatz, S.H. (2010). Redrawing the map of Great Britain from a network of human interactions. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0014248"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.compenvurbsys.2015.09.007","article-title":"Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data","volume":"54","author":"Steiger","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_10","first-page":"137","article-title":"Using mobility data as proxy for measuring urban vitality","volume":"16","author":"Sulis","year":"2018","journal-title":"J. Spat. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cities.2016.03.013","article-title":"Urban function connectivity: Characterisation of functional urban streets with social media check-in data","volume":"55","author":"Shen","year":"2016","journal-title":"Cities"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhong, C., Batty, M., Manley, E., Wang, J., Wang, Z., Chen, F., and Schmitt, G. (2016). Variability in regularity: Mining temporal mobility patterns in London, Singapore and Beijing using smart-card data. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0149222"},{"key":"ref_13","unstructured":"Twitter (2018, August 20). Annual Report 2018. Available online: https:\/\/investor.twitterinc.com\/financial-information\/annual-reports."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s10708-011-9438-2","article-title":"Harvesting ambient geospatial information from social media feeds","volume":"78","author":"Stefanidis","year":"2013","journal-title":"GeoJournal"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1111\/tgis.12132","article-title":"An advanced systematic literature review on spatiotemporal analyses of Twitter data","volume":"19","author":"Steiger","year":"2015","journal-title":"Trans. GIS"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1080\/13658816.2016.1145225","article-title":"Activity patterns, socioeconomic status and urban spatial structure: What can social media data tell us?","volume":"30","author":"Huang","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1080\/15230406.2016.1229221","article-title":"Geosocial capta in geographical research\u2014A critical analysis","volume":"45","author":"Rzeszewski","year":"2018","journal-title":"Cartogr. Geogr. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Jensen, E.A. (2017). Putting the methodological brakes on claims to measure national happiness through Twitter: Methodological limitations in social media analytics. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0180080"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hong, L., Ahmed, A., Gurumurthy, S., Smola, A.J., and Tsioutsiouliklis, K. (2012, January 16\u201320). Discovering geographical topics in the twitter stream. Proceedings of the 21st international conference on World Wide Web, Lyon, France.","DOI":"10.1145\/2187836.2187940"},{"key":"ref_20","unstructured":"Morstatter, F., Pfeffer, J., Liu, H., and Carley, K.M. (2013, January 8\u201311). Is the Sample Good Enough? Comparing Data from Twitter\u2019s Streaming API with Twitter\u2019s Firehose. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media, Cambridge, MA, USA."},{"key":"ref_21","first-page":"3","article-title":"Population bias in geotagged tweets","volume":"1","author":"Malik","year":"2015","journal-title":"People"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lenormand, M., Tugores, A., Colet, P., and Ramasco, J.J. (2014). Tweets on the road. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0105407"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Blanford, J.I., Huang, Z., Savelyev, A., and MacEachren, A.M. (2015). Geo-located tweets. enhancing mobility maps and capturing cross-border movement. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0129202"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1177\/2043820613513390","article-title":"Big data, smart cities and city planning","volume":"3","author":"Batty","year":"2013","journal-title":"Dialogues Hum. Geogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.cstp.2017.08.004","article-title":"Tweeting about public transit\u2014Gleaning public perceptions from a social media microblog","volume":"5","author":"Casas","year":"2017","journal-title":"Case Stud. Transp. Policy"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.compenvurbsys.2018.03.010","article-title":"Analyzing the spread of tweets in response to Paris attacks","volume":"71","author":"Cvetojevic","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1553\/giscience2015s525","article-title":"Uncovering latent mobility patterns from twitter during mass events","volume":"1","author":"Steiger","year":"2015","journal-title":"GI_Forum"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yin, J., Gao, Y., Du, Z., and Wang, S. (2016). Exploring multi-scale spatiotemporal twitter user mobility patterns with a visual-analytics approach. ISPRS Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5100187"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MCSE.2013.70","article-title":"Thematic patterns in georeferenced tweets through space-time visual analytics","volume":"15","author":"Andrienko","year":"2013","journal-title":"Comput. Sci. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Q., and Shan, J. (2017). Discover patterns and mobility of Twitter users\u2014A study of four US college cities. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6020042"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rzeszewski, M., and Beluch, L. (2017). Spatial characteristics of twitter users\u2014Toward the understanding of geosocial media production. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6080236"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.trc.2017.10.005","article-title":"Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method","volume":"85","author":"Zhang","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"H\u00fcbl, F., Cvetojevic, S., Hochmair, H., and Paulus, G. (2017). Analyzing refugee migration patterns using geo-tagged tweets. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6100302"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zagheni, E., Garimella, V.R.K., and Weber, I. (2014, January 7\u201311). Inferring international and internal migration patterns from twitter data. Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea.","DOI":"10.1145\/2567948.2576930"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.compenvurbsys.2016.04.002","article-title":"The geography of Twitter topics in London","volume":"58","author":"Lansley","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1080\/13658816.2015.1089441","article-title":"Geo-temporal Twitter demographics","volume":"30","author":"Longley","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1694","DOI":"10.1080\/13658816.2015.1099658","article-title":"Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks","volume":"30","author":"Steiger","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., and Newth, D. (2015). Understanding human mobility from Twitter. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0131469"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1007\/s11116-016-9747-x","article-title":"Spatiotemporal variation in travel regularity through transit user profiling","volume":"45","author":"Manley","year":"2016","journal-title":"Transportation"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., and Mascolo, C. (2012). A tale of many cities: Universal patterns in human urban mobility. PLoS ONE, 7.","DOI":"10.1371\/annotation\/ca85bf7a-7922-47d5-8bfb-bcdf25af8c72"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20130789","DOI":"10.1098\/rsif.2013.0789","article-title":"The scaling of human interactions with city size","volume":"11","author":"Bettencourt","year":"2014","journal-title":"J. R. Soc. Interface"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1126\/science.1177170","article-title":"Limits of predictability in human mobility","volume":"327","author":"Song","year":"2010","journal-title":"Science"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"20140834","DOI":"10.1098\/rsif.2014.0834","article-title":"Universal predictability of mobility patterns in cities","volume":"11","author":"Yan","year":"2014","journal-title":"J. R. Soc. Interface"},{"key":"ref_44","unstructured":"Gensim (2018, August 20). Python Wrapper for Latent Dirichlet Allocation (LDA) from MALLET. Available online: https:\/\/radimrehurek.com\/gensim\/models\/ldamallet.html."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rey, S.J., and Anselin, L. (2010). PySAL: A Python library of spatial analytical methods. Handbook of Applied Spatial Analysis, Springer.","DOI":"10.1007\/978-3-642-03647-7_11"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local indicators of spatial association\u2014LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_47","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5645","DOI":"10.1016\/j.eswa.2015.02.055","article-title":"An analysis of the coherence of descriptors in topic modeling","volume":"42","author":"Greene","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_49","unstructured":"Fang, A., Macdonald, C., Ounis, I., and Habel, P. (2018, January 17\u201321). Examining the coherence of the top ranked tweet topics. Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, Pisa, Italy."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/biomet\/37.1-2.17","article-title":"Notes on continuous stochastic phenomena","volume":"37","author":"Moran","year":"1950","journal-title":"Biometrika"},{"key":"ref_51","unstructured":"Ministry of Housing Communities & Local Government (2006). National Land Use Database: Land Use and Land Cover Classification."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compenvurbsys.2014.07.004","article-title":"Inferring building functions from a probabilistic model using public transportation data","volume":"48","author":"Zhong","year":"2014","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_53","first-page":"220","article-title":"Christaller\u2019s central place theory","volume":"65","author":"Getis","year":"1966","journal-title":"J. Geogr."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jiang, S., Fiore, G.A., Yang, Y., Ferreira, J., Frazzoli, E., and Gonz\u00e1lez, M.C. (2013, January 11). A review of urban computing for mobile phone traces: Current methods, challenges and opportunities. Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, Chicago, IL, USA.","DOI":"10.1145\/2505821.2505828"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.jocs.2015.04.021","article-title":"Measuring variability of mobility patterns from multiday smart-card data","volume":"9","author":"Zhong","year":"2015","journal-title":"J. Comput. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sloan, L., and Morgan, J. (2015). Who tweets with their location? Understanding the relationship between demographic characteristics and the use of geoservices and geotagging on Twitter. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142209"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/10\/386\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:24Z","timestamp":1760196144000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/10\/386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,25]]},"references-count":56,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["ijgi7100386"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7100386","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2018,9,25]]}}}