{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T13:09:12Z","timestamp":1783948152632,"version":"3.55.0"},"reference-count":33,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171451"],"award-info":[{"award-number":["42171451"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transactions in GIS"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Hotspot detection from geo\u2010referenced urban data is critical for smart city research, such as traffic management and policy making. However, the classical clustering or classification approach for hotspot detection mainly aims at identifying \u201chotspot areas\u201d rather than specific points, and the setting of global parameters such as search bandwidth can lead to inaccurate results when processing multi\u2010density urban data. In this article, a data\u2010driven adaptive hotspot detection (AHD) approach based on kernel density analysis is proposed and applied to various spatial objects. The adaptive search bandwidth is automatically calculated depending on the local density. Window detection is used to extract the specific hotspots in AHD, thus realizing a small\u2010scale characterization of urban hotspots. Through the trajectory data of Harbin City taxis and New York City crime data, Geo\u2010information Tupu is used to analyze the obtained specific hotspots and verify the effectiveness of AHD, providing new ideas for further research.<\/jats:p>","DOI":"10.1111\/tgis.13137","type":"journal-article","created":{"date-parts":[[2024,2,3]],"date-time":"2024-02-03T03:26:05Z","timestamp":1706930765000},"page":"303-325","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A data\u2010driven adaptive geospatial hotspot detection approach in smart cities"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0245-2241","authenticated-orcid":false,"given":"Yuchen","family":"Yan","sequence":"first","affiliation":[{"name":"School of Traffic Science and Engineering Harbin Institute of Technology  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Quan","sequence":"additional","affiliation":[{"name":"School of Traffic Science and Engineering Harbin Institute of Technology  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Traffic Science and Engineering Harbin Institute of Technology  Harbin 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