{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:06:54Z","timestamp":1760238414544,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2019YFB2103102"],"award-info":[{"award-number":["2019YFB2103102"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.<\/jats:p>","DOI":"10.3390\/ijgi11070397","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T22:06:00Z","timestamp":1657749960000},"page":"397","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6156-8906","authenticated-orcid":false,"given":"Xiaoqi","family":"Shen","sequence":"first","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Otto Poon Charitable Foundation Smart City Research Institute, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4023-9142","authenticated-orcid":false,"given":"Zhewei","family":"Liu","sequence":"additional","affiliation":[{"name":"Otto Poon Charitable Foundation Smart City Research Institute, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anshu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Otto Poon Charitable Foundation Smart City Research Institute, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8556-6422","authenticated-orcid":false,"given":"Lukang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanxin","family":"Zeng","sequence":"additional","affiliation":[{"name":"Otto Poon Charitable Foundation Smart City Research Institute, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shekhar, S., Gunturi, V., Evans, M.R., and Yang, K. 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