{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T21:05:06Z","timestamp":1769893506864,"version":"3.49.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42361078"],"award-info":[{"award-number":["42361078"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Fund for Graduate Innovation of East China University of Technology in 2024","award":["YC2024-S486"],"award-info":[{"award-number":["YC2024-S486"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10618-025-01179-3","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T13:33:20Z","timestamp":1765200800000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A regional representation method based on modeling bidirectional interrelationships between pairwise POI categories"],"prefix":"10.1007","volume":"40","author":[{"given":"Yongbin","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xusheng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonghai","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingyun","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,8]]},"reference":[{"issue":"7","key":"1179_CR1","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s10661-022-10172-y","volume":"194","author":"F Adiguzel","year":"2022","unstructured":"Adiguzel F, Cetin M, Dogan M, Gungor S, Kose M, Bozdogan Sert E, Kaya E (2022) The assessment of the thermal behavior of an urban park surface in a dense urban area for planning decisions. Environ Monit Assess 194(7):519","journal-title":"Environ Monit Assess"},{"key":"1179_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Santamar\u00eda J, Fadhel MA, Al-Amidie M, Farhan L (2021) Review of deep learning: concepts, cnn architectures, challenges, applications, future directions. J Big Data 8:1\u201374","journal-title":"J Big Data"},{"issue":"6","key":"1179_CR3","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1016\/j.cels.2021.05.017","volume":"12","author":"T Bepler","year":"2021","unstructured":"Bepler T, Berger B (2021) Learning the protein language: Evolution, structure, and function. Cell Syst 12(6):654\u2013669","journal-title":"Cell Syst"},{"issue":"Jan","key":"1179_CR4","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993\u20131022","journal-title":"J Mach Learn Res"},{"key":"1179_CR5","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1109\/JSTARS.2020.3044250","volume":"14","author":"C Chen","year":"2020","unstructured":"Chen C, Yan J, Wang L, Liang D, Zhang W (2020) Classification of urban functional areas from remote sensing images and time-series user behavior data. IEEE J Sel Top Appl Earth Observations and Remote Sensing 14:1207\u20131221","journal-title":"IEEE J Sel Top Appl Earth Observations and Remote Sensing"},{"key":"1179_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2022.103715","volume":"79","author":"H Chen","year":"2022","unstructured":"Chen H, Deng Q, Zhou Z, Ren Z, Shan X (2022) Influence of land cover change on spatio-temporal distribution of urban heat island\u2013a case in wuhan main urban area. Sustain Cities Soc 79:103715","journal-title":"Sustain Cities Soc"},{"key":"1179_CR7","doi-asserted-by":"crossref","unstructured":"Deng M, Chen C, Zhang W, Zhao J, Yang W, Guo S, Pu H, Luo J (2024) Hyperregion: Integrating graph and hypergraph contrastive learning for region embeddings. IEEE Trans Mobile Comput","DOI":"10.1109\/TMC.2024.3515154"},{"key":"1179_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.enbuild.2024.114513","volume":"319","author":"S Du","year":"2024","unstructured":"Du S, Zhang Y, Sun W, Liu B (2024) Quantifying heterogeneous impacts of 2d\/3d built environment on carbon emissions across urban functional zones: A case study in beijing, china. Energy Buildings 319:114513","journal-title":"Energy Buildings"},{"issue":"3","key":"1179_CR9","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1111\/tgis.12289","volume":"21","author":"S Gao","year":"2017","unstructured":"Gao S, Janowicz K, Couclelis H (2017) Extracting urban functional regions from points of interest and human activities on location-based social networks. Trans GIS 21(3):446\u2013467","journal-title":"Trans GIS"},{"key":"1179_CR10","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"issue":"4","key":"1179_CR11","doi-asserted-by":"publisher","first-page":"3313","DOI":"10.1109\/TKDE.2021.3130191","volume":"35","author":"J Gui","year":"2021","unstructured":"Gui J, Sun Z, Wen Y, Tao D, Ye J (2021) A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Trans Knowl Data Eng 35(4):3313\u20133332","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1179_CR12","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inform Process Syst 30"},{"key":"1179_CR13","doi-asserted-by":"publisher","first-page":"217222","DOI":"10.1109\/ACCESS.2020.3041924","volume":"8","author":"X Han","year":"2020","unstructured":"Han X, Hu X, Wu H, Shen B, Wu J (2020) Risk prediction of theft crimes in urban communities: An integrated model of lstm and st-gcn. IEEE Access 8:217222\u2013217230","journal-title":"IEEE Access"},{"key":"1179_CR14","unstructured":"Hassani K, Khasahmadi AH (2020) Contrastive multi-view representation learning on graphs. In: International Conference on Machine Learning, pp. 4116\u20134126. PMLR"},{"key":"1179_CR15","doi-asserted-by":"crossref","unstructured":"Hou Z, Liu X, Cen Y, Dong Y, Yang H, Wang C, Tang J (2022) Graphmae: Self-supervised masked graph autoencoders. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 594\u2013604","DOI":"10.1145\/3534678.3539321"},{"key":"1179_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2019.101442","volume":"80","author":"S Hu","year":"2020","unstructured":"Hu S, He Z, Wu L, Yin L, Xu Y, Cui H (2020) A framework for extracting urban functional regions based on multiprototype word embeddings using points-of-interest data. Comput Environ Urban Syst 80:101442","journal-title":"Comput Environ Urban Syst"},{"issue":"10","key":"1179_CR17","doi-asserted-by":"publisher","first-page":"1905","DOI":"10.1080\/13658816.2022.2040510","volume":"36","author":"W Huang","year":"2022","unstructured":"Huang W, Cui L, Chen M, Zhang D, Yao Y (2022) Estimating urban functional distributions with semantics preserved poi embedding. Int J Geogr Inf Sci 36(10):1905\u20131930","journal-title":"Int J Geogr Inf Sci"},{"key":"1179_CR18","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.isprsjprs.2022.11.021","volume":"196","author":"W Huang","year":"2023","unstructured":"Huang W, Zhang D, Mai G, Guo X, Cui L (2023) Learning urban region representations with pois and hierarchical graph infomax. ISPRS J Photogramm Remote Sens 196:134\u2013145","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"1179_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.habitatint.2023.102973","volume":"143","author":"L Ivan","year":"2024","unstructured":"Ivan L, Dikken J, Van Hoof J (2024) Unveiling the experienced age-friendliness of older people in bucharest: A comprehensive study using the validated romanian age-friendly cities and communities questionnaire and cluster analysis. Habitat Int 143:102973","journal-title":"Habitat Int"},{"key":"1179_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2024.105321","volume":"104","author":"J Jiao","year":"2024","unstructured":"Jiao J, Jin Y, Yang R (2024) An approach to exploring the spatial distribution and influencing factors of urban problems based on land use types. Sustain Cities Soc 104:105321","journal-title":"Sustain Cities Soc"},{"issue":"3","key":"1179_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103673","volume":"61","author":"J Jin","year":"2024","unstructured":"Jin J, Song Y, Kan D, Zhang B, Lyu Y, Zhang J, Lu H (2024) Learning context-aware region similarity with effective spatial normalization over point-of-interest data. Inform Process Manag 61(3):103673","journal-title":"Inform Process Manag"},{"key":"1179_CR22","doi-asserted-by":"crossref","unstructured":"Jing M, Zhu Y, Zang T, Yu J, Tang F (2022) Graph contrastive learning with adaptive augmentation for recommendation. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 13713 pp. 590\u2013605. Springer","DOI":"10.1007\/978-3-031-26387-3_36"},{"key":"1179_CR23","doi-asserted-by":"publisher","first-page":"102602","DOI":"10.1109\/ACCESS.2025.3577202","volume":"13","author":"N Kim","year":"2025","unstructured":"Kim N, Yoon Y (2025) Effective urban region representation learning using heterogeneous urban graph attention network (hugat). IEEE Access 13:102602\u2013102612","journal-title":"IEEE Access"},{"key":"1179_CR24","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"1179_CR25","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"key":"1179_CR26","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.jtrangeo.2014.04.018","volume":"38","author":"J Lee","year":"2014","unstructured":"Lee J, Abdel-Aty M, Jiang X (2014) Development of zone system for macro-level traffic safety analysis. J Transp Geogr 38:13\u201321","journal-title":"J Transp Geogr"},{"key":"1179_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110176","volume":"138","author":"P Li","year":"2023","unstructured":"Li P, Pei Y, Li J (2023) A comprehensive survey on design and application of autoencoder in deep learning. Appl Soft Comput 138:110176","journal-title":"Appl Soft Comput"},{"key":"1179_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.107811","volume":"160","author":"X Ma","year":"2020","unstructured":"Ma X, Lin Y, Nie Z, Ma H (2020) Structural damage identification based on unsupervised feature-extraction via variational auto-encoder. Measurement 160:107811","journal-title":"Measurement"},{"key":"1179_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2021.101651","volume":"88","author":"H Niu","year":"2021","unstructured":"Niu H, Silva EA (2021) Delineating urban functional use from points of interest data with neural network embedding: A case study in greater london. Comput Environ Urban Syst 88:101651","journal-title":"Comput Environ Urban Syst"},{"key":"1179_CR30","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532\u20131543","DOI":"10.3115\/v1\/D14-1162"},{"key":"1179_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.uclim.2024.102016","volume":"56","author":"J Rajeswari","year":"2024","unstructured":"Rajeswari J, Fountoukis C, Siddique A, Moosakutty S, Mohieldeen Y, Ayoub MA, Alfarra MR (2024) Urban heat island phenomenon in a desert, coastal city: The impact of urbanization. Urban Climate 56:102016","journal-title":"Urban Climate"},{"key":"1179_CR32","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.jtrangeo.2019.05.004","volume":"78","author":"S Sarjala","year":"2019","unstructured":"Sarjala S (2019) Built environment determinants of pedestrians\u2019 and bicyclists\u2019 route choices on commute trips: Applying a new grid-based method for measuring the built environment along the route. J Transp Geogr 78:56\u201369","journal-title":"J Transp Geogr"},{"issue":"01","key":"1179_CR33","first-page":"15","volume":"46","author":"Q Tang","year":"2024","unstructured":"Tang Q, Wu H (2024) Node representation learning based on mutual informationmaximization and cluster perception. J Yunnan University Nat Sci Ed 46(01):15\u201322","journal-title":"J Yunnan University Nat Sci Ed"},{"key":"1179_CR34","unstructured":"Veli\u010dkovi\u0107 P, Fedus W, Hamilton WL, Li\u00f2 P, Bengio Y, Hjelm RD (2018) Deep graph infomax. arXiv preprint arXiv:1809.10341"},{"issue":"6","key":"1179_CR35","doi-asserted-by":"publisher","first-page":"3503","DOI":"10.1007\/s10618-024-01049-4","volume":"38","author":"P Wang","year":"2024","unstructured":"Wang P, Sun J, Chen W, Zhao L (2024) Towards effective urban region-of-interest demand modeling via graph representation learning. Data Min Knowl Disc 38(6):3503\u20133530","journal-title":"Data Min Knowl Disc"},{"issue":"2","key":"1179_CR36","first-page":"318","volume":"52","author":"Y Wenhao","year":"2023","unstructured":"Wenhao Y, Cheng W, Jiaxin C (2023) Predicting the unbalanced labels of pois on digital maps using hierarchical model. Acta Geodaetica et Cartographica Sinica 52(2):318","journal-title":"Acta Geodaetica et Cartographica Sinica"},{"key":"1179_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compenvurbsys.2022.101807","volume":"95","author":"Y Xu","year":"2022","unstructured":"Xu Y, Zhou B, Jin S, Xie X, Chen Z, Hu S, He N (2022) A framework for urban land use classification by integrating the spatial context of points of interest and graph convolutional neural network method. Comput Environ Urban Syst 95:101807","journal-title":"Comput Environ Urban Syst"},{"key":"1179_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.tranpol.2022.08.002","volume":"127","author":"B Yang","year":"2022","unstructured":"Yang B, Tian Y, Wang J, Hu X, An S (2022) How to improve urban transportation planning in big data era? a practice in the study of traffic analysis zone delineation. Transp Policy 127:1\u201314","journal-title":"Transp Policy"},{"issue":"4","key":"1179_CR39","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1080\/13658816.2016.1244608","volume":"31","author":"Y Yao","year":"2017","unstructured":"Yao Y, Li X, Liu X, Liu P, Liang Z, Zhang J, Mai K (2017) Sensing spatial distribution of urban land use by integrating points-of-interest and google word2vec model. Int J Geogr Inf Sci 31(4):825\u2013848","journal-title":"Int J Geogr Inf Sci"},{"key":"1179_CR40","doi-asserted-by":"crossref","unstructured":"Yuan J, Zheng Y, Xie X (2012) Discovering regions of different functions in a city using human mobility and pois. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 186\u2013194","DOI":"10.1145\/2339530.2339561"},{"issue":"9","key":"1179_CR41","doi-asserted-by":"publisher","first-page":"9031","DOI":"10.1109\/TKDE.2022.3220874","volume":"35","author":"L Zhang","year":"2022","unstructured":"Zhang L, Long C, Cong G (2022) Region embedding with intra and inter-view contrastive learning. IEEE Trans Knowl Data Eng 35(9):9031\u20139036","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"3","key":"1179_CR42","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10618-025-01094-7","volume":"39","author":"M Zhang","year":"2025","unstructured":"Zhang M, Liao X, Wang X, Wang X, Jin L (2025) Multi-neighbor social recommendation with attentional graph convolutional network. Data Min Knowl Disc 39(3):21","journal-title":"Data Min Knowl Disc"},{"issue":"7","key":"1179_CR43","first-page":"907","volume":"49","author":"C Zhanlong","year":"2020","unstructured":"Zhanlong C, Lulin Z, Wenhao Y, Liang W, Zhong X (2020) Identification of the urban functional regions considering the potential context of interest points. Acta Geodaetica et Cartographica Sinica 49(7):907","journal-title":"Acta Geodaetica et Cartographica Sinica"},{"key":"1179_CR44","first-page":"4981","volume":"37","author":"S Zhou","year":"2023","unstructured":"Zhou S, He D, Chen L, Shang S, Han P (2023) Heterogeneous region embedding with prompt learning. Proc AAAI Conf Artif Intell 37:4981\u20134989","journal-title":"Proc AAAI Conf Artif Intell"},{"issue":"1","key":"1179_CR45","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1007\/s41651-024-00211-2","volume":"9","author":"X Zhou","year":"2025","unstructured":"Zhou X, Tan Y, Yu Z, Li X, Su Y, Wu J (2025) A method for mining spatial co-location patterns based on contextual similarity among categories. J Geovis Spat Anal 9(1):9","journal-title":"J Geovis Spat Anal"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01179-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-025-01179-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-025-01179-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T08:53:55Z","timestamp":1769849635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-025-01179-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["1179"],"URL":"https:\/\/doi.org\/10.1007\/s10618-025-01179-3","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,8]]},"assertion":[{"value":"17 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"9"}}