{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:26:48Z","timestamp":1776731208323,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Natural Science Foundation for Distinguished Young Scholars of Henan Province","award":["212300410014"],"award-info":[{"award-number":["212300410014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI linear location relationships and do not comprehensively mine POI spatial information, such as spatial interaction information. This results in less accurate modeling of the relationship between POI-based and urban function types. In addition, they all use Euclidean distance for proximity assessment, which is not realistic. This paper proposes an urban functional area identification method that considers the nonlinear spatial relationship between POIs. First, POI adjacency is determined according to road network constraints, which forms the basis of a co-occurrence matrix. Then, a Global Vectors (GloVe) model is used to train POI category vectors and the feature vectors for each basic research unit are obtained using weighted averages. This is followed by clustering analysis, which is realized by a K-Means++ algorithm. Lastly, the functional areas are labeled according to the POI category ratio, enrichment factors, and mobile phone signal heat data. The model was tested experimentally, using core areas of Zhengzhou City in China as an example. When the results were compared with a Baidu map, we confirmed that making full use of nonlinear spatial relationships between POIs delivers high levels of identification accuracy for urban functional areas.<\/jats:p>","DOI":"10.3390\/ijgi11100498","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T23:13:27Z","timestamp":1664147607000},"page":"498","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A GloVe Model for Urban Functional Area Identification Considering Nonlinear Spatial Relationships between Points of Interest"],"prefix":"10.3390","volume":"11","author":[{"given":"Yue","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haizhong","family":"Qian","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijian","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Batty, M. (2009). Cities as Complex Systems: Scaling, Interaction, Networks, Dynamics and Urban Morphologies, Springer.","DOI":"10.1007\/978-0-387-30440-3_69"},{"key":"ref_2","first-page":"1426","article-title":"Urban Spatial Structure","volume":"36","author":"Anas","year":"1998","journal-title":"J. Econ. Lit."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Couch, C. (2017). Urban Planning: An introduction, Bloomsbury Publishing.","DOI":"10.1007\/978-1-137-42758-8"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.rse.2017.06.039","article-title":"Using multi-source geospatial big data to identify the structure of polycentric cities","volume":"202","author":"Cai","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1080\/01431161.2012.714510","article-title":"Automated urban land-use classification with remote sensing","volume":"34","author":"Hu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"215943","DOI":"10.1109\/ACCESS.2020.3041645","article-title":"Land Use Classification Using High-Resolution Remote Sensing Images Based on Structural Topic Model","volume":"8","author":"Shao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1080\/00045608.2015.1018773","article-title":"Social Sensing: A New Approach to Understanding Our Socioeconomic Environments","volume":"105","author":"Liu","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1145\/2629592","article-title":"Urban Computing: Concepts, Methodologies, and Applications","volume":"5","author":"Zheng","year":"2014","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., and Xie, X. (2012, January 12\u201316). Discovering regions of different functions in a city using human mobility and POIs. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China.","DOI":"10.1145\/2339530.2339561"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yi, D., Yang, J., Liu, J., Liu, Y., and Zhang, J. (2019). Quantitative Identification of Urban Functions with Fishers\u2019 Exact Test and POI Data Applied in Classifying Urban Districts: A Case Study within the Sixth Ring Road in Beijing. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120555"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Wang, Z., Ma, D., Sun, D., and Zhang, J. (2021). Identification and analysis of urban functional area in Hangzhou based on OSM and POI data. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0251988"},{"key":"ref_16","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_17","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_18","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_19","doi-asserted-by":"crossref","unstructured":"da Silva Lopes, H.T., Remoaldo, P.C.A.C., and Ribeiro, V. (2018). The use of photos of the social networks in shaping a new tourist destination: Analysis of clusters in a GIS environment. Spatial Analysis, Modelling and Planning, IntechOpen.","DOI":"10.5772\/intechopen.78598"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xue, F., Li, X., Lu, W., Webster, C.J., Chen, Z., and Lin, L. (2021). Big Data-Driven Pedestrian Analytics: Unsupervised Clustering and Relational Query Based on Tencent Street View Photographs. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10080561"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1341","DOI":"10.1111\/tgis.12281","article-title":"An analysis of movement patterns between zones using taxi GPS data","volume":"21","author":"Chen","year":"2017","journal-title":"Trans. GIS"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jtrangeo.2015.01.016","article-title":"Revealing travel patterns and city structure with taxi trip data","volume":"43","author":"Liu","year":"2015","journal-title":"J. Transp. Geogr."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1080\/13658816.2015.1086923","article-title":"Incorporating spatial interaction patterns in classifying and understanding urban land use","volume":"30","author":"Liu","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, X., Tian, Y., Zhang, X., and Wan, Z. (2020). Identification of Urban Functional Regions in Chengdu Based on Taxi Trajectory Time Series Data. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9030158"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Zhang, C., Xu, L., Yan, Z., and Wu, S. (2021). A GloVe-Based POI Type Embedding Model for Extracting and Identifying Urban Functional Regions. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10060372"},{"key":"ref_28","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed representations of words and phrases and their compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.isprsjprs.2017.09.007","article-title":"Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data","volume":"132","author":"Zhang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Burger, W., Burge, M.J., Burge, M.J., and Burge, M.J. (2009). Principles of Digital Image Processing, Springer.","DOI":"10.1007\/978-1-84800-191-6"},{"key":"ref_31","first-page":"1631","article-title":"Recursive deep models for semantic compositionality over a sentiment treebank","volume":"1631","author":"Socher","year":"2013","journal-title":"EMNLP"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_33","first-page":"45","article-title":"Corpus-based approaches to semantic interpretation in NLP","volume":"18","author":"Ng","year":"1997","journal-title":"AI Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1111\/1467-9671.00058","article-title":"Defining and Delineating the Central Areas of Towns for Statistical Monitoring Using Continuous Surface Representations","volume":"4","author":"Unwin","year":"2000","journal-title":"Trans. GIS"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.eswa.2015.10.010","article-title":"Spatial co-location pattern mining for location-based services in road networks","volume":"46","author":"Yu","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1016\/j.eswa.2014.09.011","article-title":"Chinese comments sentiment classification based on word2vec and SVMperf","volume":"42","author":"Zhang","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_37","first-page":"10","article-title":"Semantic text similarity using corpus-based word similarity and string similarity","volume":"2","author":"Islam","year":"2008","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"ref_38","first-page":"56","article-title":"Semantic information mining and remote sensing classification of urban functional areas","volume":"36","author":"Ya","year":"2019","journal-title":"J. Univ. Chin. Acad. Sci."},{"key":"ref_39","first-page":"1113","article-title":"Spatial Distribution and Interaction Analysis of Urban Functional Areas Based on Multi-source Data","volume":"43","author":"Yanyan","year":"2018","journal-title":"Geomat. Inf. Sci. Wuhan Univ."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/10\/498\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:38:44Z","timestamp":1760143124000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/10\/498"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,24]]},"references-count":39,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["ijgi11100498"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11100498","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,24]]}}}