{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T11:42:32Z","timestamp":1769946152225,"version":"3.49.0"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T00:00:00Z","timestamp":1639353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871290; 41401432"],"award-info":[{"award-number":["41871290; 41401432"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning","award":["No.2020B121202019"],"award-info":[{"award-number":["No.2020B121202019"]}]},{"name":"The Science and Technology Foundation of Guangzhou Urban Planning &amp; Design Survey Research Institute","award":["RDI2210205064"],"award-info":[{"award-number":["RDI2210205064"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data.<\/jats:p>","DOI":"10.3390\/ijgi10120834","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T01:20:41Z","timestamp":1639444841000},"page":"834","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0398-4255","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"first","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"},{"name":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"},{"name":"Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou 510060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanping","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"},{"name":"Guangdong Provincial Institute of Land Surveying & Planning, Guangzhou 510062, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4703-5660","authenticated-orcid":false,"given":"Shaoying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziwei","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Chai","sequence":"additional","affiliation":[{"name":"School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.landurbplan.2016.12.001","article-title":"Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method","volume":"160","author":"Chen","year":"2017","journal-title":"Landsc. 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