{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:27:41Z","timestamp":1762507661779,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T00:00:00Z","timestamp":1550016000000},"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>Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged tweets provides the ability to discover events and their locations. DBSCAN (density-based spatial clustering of applications with noise), which has been widely used to retrieve events from geotagged tweets, cannot efficiently detect clusters when there is significant spatial heterogeneity in the dataset, as it is the case for Twitter data where the distribution of users, as well as the intensity of publishing tweets, varies over the study areas. This study proposes VDCT (Varied Density-based spatial Clustering for Twitter data) algorithm that extracts clusters from geotagged tweets by considering spatial heterogeneity. The algorithm employs exponential spline interpolation to determine different search radiuses for cluster detection. Moreover, in addition to spatial proximity, textual similarities among tweets are also taken into account by the algorithm. In order to examine the efficiency of the algorithm, geotagged tweets collected during a hurricane in the United States were used for event detection. The output clusters of VDCT have been compared to those of DBSCAN. Visual and quantitative comparison of the results proved the feasibility of the proposed method.<\/jats:p>","DOI":"10.3390\/ijgi8020082","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T03:21:46Z","timestamp":1550114506000},"page":"82","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data"],"prefix":"10.3390","volume":"8","author":[{"given":"Zeinab","family":"Ghaemi","sequence":"first","affiliation":[{"name":"Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3071-5563","authenticated-orcid":false,"given":"Mahdi","family":"Farnaghi","sequence":"additional","affiliation":[{"name":"Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 1996715433, Iran"},{"name":"GIS Center, Department of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.eswa.2016.02.028","article-title":"A rule dynamics approach to event detection in twitter with its application to sports and politics","volume":"55","author":"Gaber","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Serrano, E., Iglesias, C.A., and Garijo, M. (2015). A survey of Twitter rumor spreading simulations. Computational Collective Intelligence, Springer.","DOI":"10.1007\/978-3-319-24069-5_11"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.compenvurbsys.2018.07.003","article-title":"Identifying spatiotemporal urban activities through linguistic signatures","volume":"72","author":"Fu","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.dss.2014.02.003","article-title":"Predicting crime using Twitter and kernel density estimation","volume":"61","author":"Gerber","year":"2014","journal-title":"Decis. Support Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Relia, K., Akbari, M., Duncan, D., and Chunara, R. (2018). Socio-spatial Self-organizing Maps: Using Social Media to Assess Relevant Geographies for Exposure to Social Processes. arXiv.","DOI":"10.1145\/3274414"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Akbari, M., Relia, K., Elghafari, A., and Chunara, R. (2018, January 25\u201328). From the User to the Medium: Neural Profiling Across Web Communities. Proceedings of the Twelfth International AAAI Conference on Web and Social Media, Palo Alto, CA, USA.","DOI":"10.1609\/icwsm.v12i1.15063"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1111\/coin.12017","article-title":"A survey of techniques for event detection in twitter","volume":"31","author":"Atefeh","year":"2015","journal-title":"Comput. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.eswa.2017.11.055","article-title":"I-TWEC: Interactive clustering tool for Twitter","volume":"96","author":"Erpam","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mohammadinia, A., Alimohammadi, A., and Saeidian, B. (2017). Efficiency of Geographically Weighted Regression in Modeling Human Leptospirosis Based on Environmental Factors in Gilan Province, Iran. Geosciences, 7.","DOI":"10.3390\/geosciences7040136"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saeidian, B., Mesgari, M., Pradhan, B., and Ghodousi, M. (2018). Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7080292"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.atmosenv.2018.03.015","article-title":"Prediction of hourly PM2. 5 using a space-time support vector regression model","volume":"181","author":"Yang","year":"2018","journal-title":"Atmos. Environ."},{"key":"ref_12","first-page":"431","article-title":"Geographically weighted regression","volume":"47","author":"Brunsdon","year":"1998","journal-title":"J. R. Stat. Soc. Ser. D (Stat.)"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1177\/0894439316671698","article-title":"The digital divide among Twitter users and its implications for social research","volume":"35","author":"Blank","year":"2017","journal-title":"Soc. Sci. Comput. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sloan, L., Morgan, J., Burnap, P., and Williams, M. (2015). Who tweets? Deriving the demographic characteristics of age, occupation and social class from Twitter user meta-data. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0115545"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5153\/sro.3001","article-title":"Knowing the tweeters: Deriving sociologically relevant demographics from Twitter","volume":"18","author":"Sloan","year":"2013","journal-title":"Sociol. Res. Online"},{"key":"ref_16","first-page":"25","article-title":"Understanding the Demographics of Twitter Users","volume":"11","author":"Mislove","year":"2011","journal-title":"ICWSM"},{"key":"ref_17","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the KDD 1996, Portland, OR, USA."},{"key":"ref_18","first-page":"59","article-title":"A survey on density based clustering algorithms for mining large spatial databases","volume":"31","author":"Parimala","year":"2011","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.patrec.2016.08.010","article-title":"Tweet-scan: An event discovery technique for geo-located tweets","volume":"93","author":"Capdevila","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Capdevila, J., Pericacho, G., Torres, J., and Cerquides, J. (2016, January 14\u201316). Scaling dbscan-like algorithms for event detection systems in twitter. Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing, Granada, Spain.","DOI":"10.1007\/978-3-319-49583-5_27"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nakahori, K., and Yamaguchi, S. (2017, January 12\u201314). A method to discover spots from Twitter for tour miner. Proceedings of the 2017 IEEE International Symposium on Consumer Electronics (ISCE), Taibei, Taiwan.","DOI":"10.1109\/ISCE.2017.8355539"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9623","DOI":"10.1016\/j.eswa.2012.02.136","article-title":"Mining spatio-temporal information on microblogging streams using a density-based online clustering method","volume":"39","author":"Lee","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.ins.2016.01.014","article-title":"User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks","volume":"340","author":"Arcaini","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Nguyen, M.D., and Shin, W.-Y. (August, January 31). DBSTexC: Density-Based Spatio-Textual Clustering on Twitter. Proceedings of Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia.","DOI":"10.1145\/3110025.3110096"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Idrissi, A., Rehioui, H., Laghrissi, A., and Retal, S. (2015, January 21\u201323). An improvement of DENCLUE algorithm for the data clustering. Proceedings of the 2015 5th International Conference on Information & Communication Technology and Accessibility (ICTA), Marrakech, Morocco.","DOI":"10.1109\/ICTA.2015.7426936"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Liu, P., Zhou, D., and Wu, N. (2007, January 8\u201311). VDBSCAN: Varied density based spatial clustering of applications with noise. Proceedings of the 2007 International Conference on Service Systems and Service Management, Chengdu, China.","DOI":"10.1109\/ICSSSM.2007.4280175"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ram, A., Sharma, A., Jalal, A.S., Agrawal, A., and Singh, R. (2009, January 6\u20137). An enhanced density based spatial clustering of applications with noise. Proceedings of the 2009 Advance Computing Conference, Patiala, India.","DOI":"10.1109\/IADCC.2009.4809235"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1016\/j.ipm.2017.01.002","article-title":"Paraphrase identification and semantic text similarity analysis in Arabic news tweets using lexical, syntactic, and semantic features","volume":"53","author":"Jaradat","year":"2017","journal-title":"Inf. Process. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lee, H., Kihm, J., Choo, J., Stasko, J., and Park, H. (2012). iVisClustering: An interactive visual document clustering via topic modeling. Comput. Graph. Forum, 1155\u20131164.","DOI":"10.1111\/j.1467-8659.2012.03108.x"},{"key":"ref_30","unstructured":"Hurlock, J., and Wilson, M.L. (2011, January 17\u201321). Searching Twitter: Separating the Tweet from the Chaff. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zuo, Y., Wu, J., Zhang, H., Lin, H., Wang, F., Xu, K., and Xiong, H. (2016, January 24\u201327). Topic modeling of short texts: A pseudo-document view. Proceedings of the 22nd ACM Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939880"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Fu, C., Samet, H., and Sankaranarayanan, J. (2014, January 4). WeiboStand: Capturing Chinese breaking news using Weibo tweets. Proceedings of the 7th ACM Sigspatial International Workshop on Location-Based Social Networks, Dallas\/Fort Worth, TX, USA.","DOI":"10.1145\/2755492.2755499"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., and Sperling, J. (2009, January 4\u20136). Twitterstand: News in tweets. Proceedings of the 17th Acm sigspatial International Conference on Advances in Geographic Information Systems, Seattle, WA, USA.","DOI":"10.1145\/1653771.1653781"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.patrec.2016.10.014","article-title":"Unsupervised varied density based clustering algorithm using spline","volume":"93","author":"Louhichi","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_35","first-page":"1775","article-title":"A Technical Survey on DBSCAN Clustering Algorithm","volume":"4","author":"Suthar","year":"2013","journal-title":"Int. J. Sci. Eng. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial-temporal data","volume":"60","author":"Birant","year":"2007","journal-title":"Data Knowl. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional binary search trees used for associative searching","volume":"18","author":"Bentley","year":"1975","journal-title":"Commun. ACM"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1145\/355744.355745","article-title":"An algorithm for finding best matches in logarithmic expected time","volume":"3","author":"Friedman","year":"1977","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Garcia, J.C., Avenda\u00f1o, A., and Vaca, C. (2018, January 27\u201329). Where to go in Brooklyn: NYC Mobility Patterns from Taxi Rides. Proceedings of the World Conference on Information Systems and Technologies, Naples, Italy.","DOI":"10.1007\/978-3-319-77703-0_20"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1002\/sapm1966451312","article-title":"An interpolation curve using a spline in tension","volume":"45","author":"Schweikert","year":"1966","journal-title":"J. Math. Phys."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bronshtein, I.N., Semendyayev, K.A., Musiol, G., and Muehlig, H. (2004). Tables. Handbook of Mathematics, Springer.","DOI":"10.1007\/978-3-662-05382-9"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1007\/s10661-018-6659-6","article-title":"LaSVM-based big data learning system for dynamic prediction of air pollution in Tehran","volume":"190","author":"Ghaemi","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_43","first-page":"31","article-title":"Optimum allocation of water to the cultivation farms using Genetic Algorithm","volume":"40","author":"Saeidian","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Davies, D.L., and Bouldin, D.W. (1979). A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell., 224\u2013227.","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1080\/01969727408546059","article-title":"Well-separated clusters and optimal fuzzy partitions","volume":"4","author":"Dunn","year":"1974","journal-title":"J. Cybern."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chellal, A., Boughanem, M., and Dousset, B. (2017, January 8\u201313). Word similarity based model for tweet stream prospective notification. Proceedings of the European Conference on Information Retrieval, Aberdeen, UK.","DOI":"10.1007\/978-3-319-56608-5_62"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.patrec.2016.06.012","article-title":"Representation learning for very short texts using weighted word embedding aggregation","volume":"80","author":"Demeester","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ozdikis, O., Senkul, P., and Oguztuzun, H. (2014). Context based semantic relations in tweets. State of the Art Applications of Social Network Analysis, Springer.","DOI":"10.1007\/978-3-319-05912-9_2"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, W., Callison-Burch, C., and Dolan, B. (2015, January 4\u20135). SemEval-2015 Task 1: Paraphrase and semantic similarity in Twitter (PIT). Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, CO, USA.","DOI":"10.18653\/v1\/S15-2001"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s10707-012-0173-8","article-title":"An algorithm for local geoparsing of microtext","volume":"17","author":"Gelernter","year":"2013","journal-title":"GeoInformatica"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.neucom.2014.12.043","article-title":"Topic based context-aware travel recommendation method exploiting geotagged photos","volume":"155","author":"Xu","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.14778\/2536274.2536307","article-title":"Eventweet: Online localized event detection from twitter","volume":"6","author":"Abdelhaq","year":"2013","journal-title":"Proc. VLDB Endow."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, L., Sun, X., and Zhuge, H. (2013, January 3\u20134). Location-driven geographical topic discovery. Proceedings of the 2013 Ninth International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China.","DOI":"10.1109\/SKG.2013.20"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/2\/82\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:48Z","timestamp":1760185908000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/8\/2\/82"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,13]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["ijgi8020082"],"URL":"https:\/\/doi.org\/10.3390\/ijgi8020082","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2019,2,13]]}}}