{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T12:14:46Z","timestamp":1769084086827,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Special Projects of the Ministry of Public Security","award":["2021JC35"],"award-info":[{"award-number":["2021JC35"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The determination of a reasonable spatial analysis unit is an essential step in urban functional zone (UFZ) division, which significantly affects the results. However, most studies on the division of functional zones are based on excessively large spatial units, such as blocks or traffic analysis zones (TAZs), which easily overlook the detailed characteristics of urban regions and introduce bias to the research conclusion. To address this issue, a refined zone segmentation method, namely, the Voronoi diagram for the polygon method, was proposed to generate refined spatial analysis units. Afterward, the functional topics of the spatial analysis unit were classified by a multiclass support vector machine (SVM) to produce the final UFZ map, where the functional topics of each spatial unit were obtained by coupling latent Dirichlet allocation (LDA). To verify the effectiveness of the proposed method, experiments were conducted in Beijing, China. The results indicated that the proposed segmentation method can generate fine-scale spatial units and provide fine-grained and higher accuracy UFZs (overall accuracy = 84%; kappa = 0.82).<\/jats:p>","DOI":"10.3390\/ijgi11080421","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T21:20:59Z","timestamp":1658784059000},"page":"421","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Refined Urban Functional Zone Mapping by Integrating Open-Source Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Yue","family":"Deng","sequence":"first","affiliation":[{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of Three-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0999-4905","authenticated-orcid":false,"given":"Rixing","family":"He","sequence":"additional","affiliation":[{"name":"College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Key Laboratory of Three-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112480","DOI":"10.1016\/j.rse.2021.112480","article-title":"Mapping large-scale and fine-grained urban functional zones from VHR images using a multi-scale semantic segmentation network and object based approach","volume":"261","author":"Du","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1080\/13658816.2013.800871","article-title":"Toward mapping land-use patterns from volunteered geographic information","volume":"27","author":"Helbich","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1111\/tgis.12289","article-title":"Extracting urban functional regions from points of interest and human activities on location-based social networks","volume":"21","author":"Gao","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"101651","DOI":"10.1016\/j.compenvurbsys.2021.101651","article-title":"Delineating urban functional use from points of interest data with neural network embedding: A case study in Greater London","volume":"88","author":"Niu","year":"2021","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1080\/13658816.2016.1244608","article-title":"Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model","volume":"31","author":"Yao","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.isprsjprs.2021.03.019","article-title":"Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery","volume":"175","author":"Huang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tu, W., Hu, Z., Li, L., Cao, J., Jiang, J., Li, Q., and Li, Q. (2018). Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data. Remote Sens., 10.","DOI":"10.3390\/rs10010141"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1080\/13658816.2017.1324976","article-title":"Classifying urban land use by integrating remote sensing and social media data","volume":"31","author":"Liu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1111\/j.1467-985X.2012.01054.x","article-title":"Development of a geographical framework for census workplace data","volume":"176","author":"Martin","year":"2013","journal-title":"J. R. Stat. Soc. Ser. A (Stat. Soc.)"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1191\/0309132505ph528pr","article-title":"Geographical Information Systems: A renaissance of geodemographics for public service delivery","volume":"29","author":"Longley","year":"2005","journal-title":"Prog. Hum. Geogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TKDE.2014.2345405","article-title":"Discovering Urban Functional Zones Using Latent Activity Trajectories","volume":"27","author":"Yuan","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1080\/13658816.2017.1356464","article-title":"Coupling mobile phone and social media data: A new approach to understanding urban functions and diurnal patterns","volume":"31","author":"Tu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Song, J., Lin, T., Li, X., and Prishchepov, A.V. (2018). Mapping Urban Functional Zones by Integrating Very High Spatial Resolution Remote Sensing Imagery and Points of Interest: A Case Study of Xiamen, China. Remote Sens., 10.","DOI":"10.3390\/rs10111737"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hu, Y., and Han, Y. (2019). Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone. Sustainability, 11.","DOI":"10.3390\/su11051385"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2018.11.008","article-title":"Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs","volume":"74","author":"Zhai","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1109\/18.165464","article-title":"Random texts exhibit Zipf\u2019s-law-like word frequency distribution","volume":"38","author":"Li","year":"1992","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Thill, J.-C. (2018). Discovering Functional Zones in a City Using Human Movements and Points of Interest. Spatial Analysis and Location Modeling in Urban and Regional Systems, Springer.","DOI":"10.1007\/978-3-642-37896-6"},{"key":"ref_18","first-page":"68","article-title":"Quantitative identification and visualization of urban functional area based on POI data","volume":"41","author":"Chi","year":"2016","journal-title":"J. Geomat."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hu, T., Yang, J., Li, X., and Gong, P. (2016). Mapping Urban Land Use by Using Landsat Images and Open Social Data. Remote Sens., 8.","DOI":"10.3390\/rs8020151"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1068\/a160017","article-title":"Ecological fallacies and the analysis of areal census data","volume":"16","author":"Openshaw","year":"1984","journal-title":"Environ. Plan. A"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"112993","DOI":"10.1016\/j.rse.2022.112993","article-title":"Graph-based block-level urban change detection using Sentinel-2 time series","volume":"274","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111458","DOI":"10.1016\/j.rse.2019.111458","article-title":"SO\u2013CNN based urban functional zone fine division with VHR remote sensing image","volume":"236","author":"Zhou","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101806","DOI":"10.1016\/j.scs.2019.101806","article-title":"Spatial pattern of leisure activities among residents in Beijing, China: Exploring the impacts of urban environment","volume":"52","author":"Liu","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"04020007","DOI":"10.1061\/(ASCE)UP.1943-5444.0000566","article-title":"Crowdsourced Data Mining for Urban Activity: Review of Data Sources, Applications, and Methods","volume":"146","author":"Niu","year":"2020","journal-title":"J. Urban Plan. Dev."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, X., Hu, T., Gong, P., Du, S., Chen, B., Li, X., and Dai, Q. (2021). Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sens., 13.","DOI":"10.3390\/rs13030477"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"084596","DOI":"10.1117\/1.JRS.8.084596","article-title":"Long-term effects of land use\/land cover change on surface runoff in urban areas of Beijing, China","volume":"8","author":"Sun","year":"2013","journal-title":"J. Appl. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhao, L., and Li, Y. (December, January 30). Study on Urban Road Network Traffic District Division based on Clustering Analysis. Proceedings of the 2018 Chinese Automation Congress (CAC), Xi\u2019an, China.","DOI":"10.1109\/CAC.2018.8623160"},{"key":"ref_28","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. Urban Plan."},{"key":"ref_29","first-page":"993","article-title":"Latent dirichlet allocation","volume":"30","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1080\/15481603.2020.1724707","article-title":"Large-scale urban functional zone mapping by integrating remote sensing images and open social data","volume":"57","author":"Du","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101441","DOI":"10.1016\/j.compenvurbsys.2019.101441","article-title":"Morphological tessellation as a way of partitioning space: Improving consistency in urban morphology at the plot scale","volume":"80","author":"Fleischmann","year":"2020","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Q., Huang, H., Wu, W., Du, X., and Wang, H. (2017). The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China. Remote Sens., 9.","DOI":"10.3390\/rs9090865"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Deng, Y., Liu, J., Luo, A., Wang, Y., Xu, S., Ren, F., and Su, F. (2020). Spatial Mismatch between the Supply and Demand of Urban Leisure Services with Multisource Open Data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9080466"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set","volume":"57","author":"Ji","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, K., Fu, K., Gao, X., Yan, M., Sun, X., and Zhang, H. (2017, January 23\u201328). Building extraction from remote sensing images with deep learning in a supervised manner. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127295"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/421\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:56:15Z","timestamp":1760140575000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/8\/421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,25]]},"references-count":36,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["ijgi11080421"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11080421","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,25]]}}}