{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T02:05:49Z","timestamp":1776650749944,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot\/cold spot detection techniques are proposed in literature; clustering methods are generally applied in order to extract hot and cold spots as polygons on the maps; the more precise the determination of the area of the hot (cold) spots, the greater the computational complexity of the clustering algorithm. Furthermore, these methods do not take into account the hidden information provided by users through social networks, which is significant for detecting the presence of hot\/cold spots based on the emotional reactions of citizens. To overcome these critical points, we propose a GIS-based hot and cold spot detection framework encapsulating a classification model of emotion categories of documents extracted from social streams connected to the investigated phenomenon is implemented. The study area is split into subzones; residents\u2019 postings during a predetermined time period are retrieved and analyzed for each subzone. The proposed model measures for each subzone the prevalence of pleasant and unpleasant emotional categories in different time frames; with the aid of a fuzzy-based emotion classification approach, subzones in which unpleasant\/pleasant emotions prevail over the analyzed time period are labeled as hot\/cold spots. A strength of the proposed framework is to significantly reduce the CPU time of cluster-based hot and cold spot detection methods as it does not require detecting the exact geometric shape of the spot. Our framework was tested to detect hot and cold spots related to citizens\u2019 discomfort due to heatwaves in the study area made up of the municipalities of the northeastern area of the province of Naples (Italy). The results show that the hot spots, where the greatest discomfort is felt, correspond to areas with a high population\/building density. On the contrary, cold spots cover urban areas having a lower population density.<\/jats:p>","DOI":"10.3390\/fi15010023","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T02:44:03Z","timestamp":1672627443000},"page":"23","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A GIS-Based Hot and Cold Spots Detection Method by Extracting Emotions from Social Streams"],"prefix":"10.3390","volume":"15","author":[{"given":"Barbara","family":"Cardone","sequence":"first","affiliation":[{"name":"Dipartimento di Architettura, Universit\u00e0 degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5690-5384","authenticated-orcid":false,"given":"Ferdinando","family":"Di Martino","sequence":"additional","affiliation":[{"name":"Dipartimento di Architettura, Universit\u00e0 degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy"},{"name":"Centro di Ricerca Interdipartimentale \u201cAlberto Calza Bini\u201d, Universit\u00e0 degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vittorio","family":"Miraglia","sequence":"additional","affiliation":[{"name":"Dipartimento di Architettura, Universit\u00e0 degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.dcan.2021.10.003","article-title":"A survey on deep learning for textual emotion analysis in social networks","volume":"8","author":"Peng","year":"2021","journal-title":"Digit. Commun. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1186\/s40537-021-00419-9","article-title":"A survey on data-efficient algorithms in big data era","volume":"8","author":"Adadi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1007\/s10115-013-0706-y","article-title":"Self-labeled techniques for semi-supervised learning: Taxonomy. software and empirical study","volume":"42","author":"Triguero","year":"2013","journal-title":"Knowl. Inf. Syst."},{"key":"ref_5","unstructured":"Aggarwal, C.C., and Reddy, C.K. (2014). A Survey of Stream Clustering Algorithms. Data Clustering. Algorithms and Applications, Chapman and Hall\/CRC. [1st ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1002\/int.22105","article-title":"A lightweight clustering-based approach to discover different emotional shades from social message streams","volume":"34","author":"Senatore","year":"2019","journal-title":"Int. J. Intell. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"The fuzzy C-means Clustering Algorithm","volume":"10","author":"Bezek","year":"1984","journal-title":"Comput. Geosci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1109\/TFUZZ.2002.805901","article-title":"Fuzzy clustering with volume prototype and adaptive cluster merging","volume":"10","author":"Kaymak","year":"2002","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1007\/s12065-021-00603-z","article-title":"GIS-based fuzzy sentiment analysis framework to classify urban elements according to the orientations of citizens and tourists expressed in social networks","volume":"15","author":"Cardone","year":"2022","journal-title":"Evol. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6944","DOI":"10.1002\/int.22575","article-title":"Improving the emotion-based classification by exploiting the fuzzy entropy in FCM clustering","volume":"36","author":"Cardone","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ins.2022.02.028","article-title":"A fuzzy partition-based method to classify social messages assessing their emotional relevance","volume":"594","author":"Cardone","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cardone, B., and Di Martino, F. (2022). A GIS-Based Fuzzy Multiclassification Framework Applied for Spatiotemporal Analysis of Phenomena in Urban Contexts. Information, 13.","DOI":"10.3390\/info13050248"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.1538-4632.1992.tb00261.x","article-title":"The Analysis of Spatial Association by Use of Distance Statistics","volume":"24","author":"Getis","year":"1992","journal-title":"Geogr. Anal."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"Local Indicators of Spatial Association-LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100410","DOI":"10.1016\/j.envc.2021.100410","article-title":"Drought hot spot analysis using local indicators of spatial autocorrelation: An experience from Bangladesh","volume":"6","year":"2022","journal-title":"Environ. Chall."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1057\/palgrave.sj.8350066","article-title":"The Utility of Hotspot Mapping for Predicting Spatial Patterns of Crime","volume":"21","author":"Chainey","year":"2008","journal-title":"Secur. J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Devroye, L., and Rugosi, G. (2001). Combinatorial Methods in Density Estimation, Springer. [2001st ed.].","DOI":"10.1007\/978-1-4613-0125-7"},{"key":"ref_19","first-page":"281","article-title":"Some Methods for Classification and Analysis of Multivariate Observations","volume":"Volume 1","author":"Neyman","year":"1967","journal-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability"},{"key":"ref_20","first-page":"4","article-title":"Crime Analysis Using K-Means Clustering","volume":"83","author":"Agarval","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_21","first-page":"5","article-title":"Detecting Hot Spots on Crime Data Using Data Mining and Geographical Information System","volume":"8","author":"Sing","year":"2013","journal-title":"Int. J. Stat. Math."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1016\/j.procs.2020.03.357","article-title":"A Clustering Based Hot Spot Identification Approach for Crime Prediction","volume":"167","author":"Hajela","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/JSTARS.2012.2210699","article-title":"Hot Spot Analysis of Vegetation Fires and Intensity in the Indian Region","volume":"6","author":"Vadrevu","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9","DOI":"10.25008\/ijadis.v1i1.13","article-title":"Application of K-Means Clustering Algorithm for Determination of Fire-Prone Areas Utilizing Hot Spots in West Kalimantan Province","volume":"1","author":"Khairani","year":"2020","journal-title":"Int. J. Adv. Data Inf. Syst."},{"key":"ref_25","unstructured":"Kaufman, L., and Rousseeuw, P.J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis, John Wiley & Sons. [2nd ed.]."},{"key":"ref_26","first-page":"38","article-title":"Implementation of k-Medoids Clustering Algorithm to Cluster Crime Patterns in Yogyakarta","volume":"1","author":"Hardika","year":"2019","journal-title":"Int. J. Appl. Sci. Smart Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1007\/978-981-13-9942-8_34","article-title":"Rough-Set Based Hot Spot Detection in Spatial Data","volume":"Volume 1046","author":"Singh","year":"2019","journal-title":"Advances in Computing and Data Sciences"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TFUZZ.2012.2201485","article-title":"Fuzzy c-Means Algorithms for Very Large Data","volume":"20","author":"Havens","year":"2012","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"374","DOI":"10.14429\/dsj.68.12518","article-title":"Application of Spatio-Temporal Fuzzy C-Means Clustering for Crime Spot Detection","volume":"68","author":"Ansari","year":"2018","journal-title":"Def. Sci. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1007\/s40815-019-00673-3","article-title":"PCPD: A Parallel Crime Pattern Discovery System for Large-Scale Spatio-temporal Data Based on Fuzzy Clustering","volume":"21","author":"Win","year":"2019","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_31","first-page":"307","article-title":"Fuzzy Cluster\u2013Based Method of Hot Spot Detection with Limited Information","volume":"7","author":"Bandyopadhyaya","year":"2015","journal-title":"J. Transp. Saf. Secur."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9420","DOI":"10.1038\/s41598-021-88822-3","article-title":"A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN","volume":"11","author":"Huang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.patcog.2016.03.008","article-title":"A fast DBSCAN clustering algorithm by accelerating neighbor searching using groups method","volume":"58","author":"Kumar","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"309","DOI":"10.3311\/PPtr.9911","article-title":"Self-Organizing Tree Algorithm (SOTA) Clustering for Defining Level of Service (LOS) Criteria of Urban Streets","volume":"47","author":"Das","year":"2019","journal-title":"Period. Polytech. Transp. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cardone, B., and Di Martino, F. (2022). Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation\u2014An Application in Crime Analysis. Electronics, 11.","DOI":"10.3390\/electronics11030370"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2377","DOI":"10.1007\/s00500-013-1211-7","article-title":"Spatio-temporal hot spots and Application on a Disease Analysis Case via GIS","volume":"18","author":"Sessa","year":"2014","journal-title":"Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","article-title":"Term-weighting approaches in automatic text retrieval","volume":"24","author":"Salton","year":"1988","journal-title":"Inf. Process. Manag."},{"key":"ref_38","first-page":"53","article-title":"Identifying crime clusters: The spatial principles","volume":"28","author":"Chakravorty","year":"1995","journal-title":"Middle States Geogr."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Plutchik, R., and Kellerman, H. (1980). A General Psychoevolutionary Theory of Emotion, Academic Press. Theories of Emotion.","DOI":"10.1016\/B978-0-12-558701-3.50007-7"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/1\/23\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:48Z","timestamp":1760147328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/1\/23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,30]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["fi15010023"],"URL":"https:\/\/doi.org\/10.3390\/fi15010023","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,30]]}}}