{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T22:48:37Z","timestamp":1785192517975,"version":"3.55.0"},"reference-count":28,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nanyang Polytechnic Institute Doctoral Research Initiation Fund Project: Research on the Synergistic Development of Municipal Social Governance and Social Policies","award":["NGBJ-2020-22"],"award-info":[{"award-number":["NGBJ-2020-22"]}]},{"name":"Project of Soft Science of Nanyang City:Innovative Research on Enhancing the Governance Level and Governance Capacity of Urban and Rural Grassroots in Nanyang Cit","award":["RKX021"],"award-info":[{"award-number":["RKX021"]}]},{"name":"Project of Soft Science in Henan Province: Research on the Causes and Optimisation Path of Formalism in Digital Governance at the Grassroots Level in Henan Province","award":["242400410469"],"award-info":[{"award-number":["242400410469"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>As digital governance progresses rapidly, constructing digital portraits of residents has become instrumental in enhancing local-level administrative capabilities. Nonetheless, traditional K-means clustering algorithms struggle with the classification of high-dimensional and complex data, thereby limiting their effectiveness. To address this issue, this paper proposes a novel hybrid algorithm\u2014PSO-KM\u2014that integrates Particle Swarm Optimization with K-means to improve both accuracy and computational efficiency in clustering resident profile data. Drawing on comprehensive resident information collected in 2023 from a community management system, the method leverages PSO\u2019s global optimization abilities alongside K-means\u2019 iterative refinement to dynamically update cluster centroids. Performance evaluation shows a significant uplift in clustering metrics, with a silhouette score of 0.752\u2009\u00b1\u20090.021 and inter-cluster distance of 1.493\u2009\u00b1\u20090.036. Comparative analysis against conventional and advanced methods (e.g., GA-K-means, DBSCAN) reveals that PSO-KM delivers superior outcomes. Among different feature categories, behavioral data yield the best classification performance, with a silhouette value of 0.184, highlighting the discriminatory power of dynamic behavioral traits. Furthermore, segmentation results disclose varying dominant features across income brackets: demographic factors are primary for low-income groups, behavioral metrics dominate middle-income segments, while social network indicators are key for high-income populations. These insights confirm PSO-KM\u2019s potential in refining digital profiling processes and fostering the advancement of grassroots digital governance practices.<\/jats:p>","DOI":"10.1371\/journal.pone.0329123","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T19:20:31Z","timestamp":1755199231000},"page":"e0329123","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Application of PSO-integrated K-means algorithm in resident digital portrait classification"],"prefix":"10.1371","volume":"20","author":[{"given":"Hongwei","family":"Yue","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hejuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5896-8326","authenticated-orcid":true,"given":"Yuqiao","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,14]]},"reference":[{"issue":"3","key":"pone.0329123.ref001","doi-asserted-by":"crossref","first-page":"257","DOI":"10.59613\/k8s6s859","article-title":"Harnessing digital transformation for improved public service delivery: lessons from global administrative practices","volume":"1","author":"SH Idrus","year":"2024","journal-title":"join"},{"issue":"2","key":"pone.0329123.ref002","doi-asserted-by":"crossref","DOI":"10.1177\/20539517211036799","article-title":"Digital phenotyping and data inheritance","volume":"8","author":"S Green","year":"2021","journal-title":"Big Data & Society"},{"key":"pone.0329123.ref003","doi-asserted-by":"crossref","unstructured":"Khalsan M, Mu M, Al-shamery ES, Ajit S, Machado L, Agyeman MO. 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