{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T08:58:07Z","timestamp":1764061087649,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T00:00:00Z","timestamp":1655337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Beijing Natural Science Foundation","award":["#8222009","#202137","#JDJQ20200306"],"award-info":[{"award-number":["#8222009","#202137","#JDJQ20200306"]}]},{"name":"Training Program for Talents by Xicheng, Beijing","award":["#8222009","#202137","#JDJQ20200306"],"award-info":[{"award-number":["#8222009","#202137","#JDJQ20200306"]}]},{"name":"Pyramid Talent Training Project of BUCEA","award":["#8222009","#202137","#JDJQ20200306"],"award-info":[{"award-number":["#8222009","#202137","#JDJQ20200306"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The identification of urban functional regions (UFRs) is important for urban planning and sustainable development. Because this involves a set of interrelated processes, it is difficult to identify UFRs using only single data sources. Data fusion methods have the potential to improve the identification accuracy. However, the use of existing fusion methods remains challenging when mining shared semantic information among multiple data sources. In order to address this issue, we propose a context-coupling matrix factorization (CCMF) method which considers contextual relationships. This method was designed based on the fact that the contextual relationships embedded in all of the data are shared and complementary to one another. An empirical study was carried out by fusing point-of-interest (POI) data and taxi origin\u2013destination (OD) data in Beijing, China. There are three steps in CCMF. First, contextual information is extracted from POI and taxi OD trajectory data. Second, fusion is performed using contextual information. Finally, spectral clustering is used to identify the functional regions. The results show that the proposed method achieved an overall accuracy (OA) of 90% and a kappa of 0.88 in the study area. The results were compared with the results obtained using single sources of non-fused data and other fusion methods in order to validate the effectiveness of our method. The results demonstrate that an improvement in the OA of about 5% in comparison to a similar method in the literature could be achieved using this method.<\/jats:p>","DOI":"10.3390\/ijgi11060351","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T01:48:12Z","timestamp":1655430492000},"page":"351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Context-Aware Matrix Factorization for the Identification of Urban Functional Regions with POI and Taxi OD Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1270-5353","authenticated-orcid":false,"given":"Changfeng","family":"Jing","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]},{"given":"Yanru","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]},{"given":"Hongyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]},{"given":"Mingyi","family":"Du","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]},{"given":"Shishuo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]},{"given":"Xian","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]},{"given":"Jie","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.landurbplan.2012.02.012","article-title":"Urban land uses and traffic \u201csource-sink areas\u201d: Evidence from GPS-enabled taxi data in Shanghai","volume":"106","author":"Liu","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.engappai.2014.06.019","article-title":"Spectral clustering for sensing urban land use using Twitter activity","volume":"35","year":"2014","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ge, P., He, J., Zhang, S., Zhang, L., and She, J. (2019). An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8020090"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gao, Q., Fu, J., Yu, Y., and Tang, X. (2019). Identification of urban regions\u2019 functions in Chengdu, China, based on vehicle trajectory data. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0215656"},{"key":"ref_6","first-page":"2281","article-title":"Urban Functional Area Identification Method Based on Mobile Big Data","volume":"31","author":"Xiao","year":"2019","journal-title":"Xitong Fangzhen Xuebao\/J. Syst. Simul."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1177\/2399808320935467","article-title":"Urban function recognition by integrating social media and street-level imagery","volume":"48","author":"Ye","year":"2021","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Yu, M., Li, J., Lv, Y., Xing, H., and Wang, H. (2021). Functional Area Recognition and Use-Intensity Analysis Based on Multi-Source Data: A Case Study of Jinan, China. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100640"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dai, P., Jing, C., Du, M., and Zhou, W. (2015, January 8\u201310). A method based on spatial analyst to detect hot spot of urban component management events. Proceedings of the 2015 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), Fuzhou, China.","DOI":"10.1109\/ICSDM.2015.7298025"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qian, Z., Liu, X., Tao, F., and Zhou, T. (2020). Identification of Urban Functional Areas by Coupling Satellite Images and Taxi GPS Trajectories. Remote Sens., 12.","DOI":"10.3390\/rs12152449"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.inffus.2019.12.001","article-title":"A survey on machine learning for data fusion","volume":"57","author":"Meng","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jia, Y., Ge, Y., Ling, F., Guo, X., Wang, J., Wang, L., Chen, Y., and Li, X. (2018). Urban land use mapping by combining remote sensing imagery and mobile phone positioning data. Remote Sens., 10.","DOI":"10.3390\/rs10030446"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.compenvurbsys.2018.06.005","article-title":"Integrating landscape metrics and socioeconomic features for urban functional region classification","volume":"72","author":"Xing","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhu, J., Tao, C., Lin, X., Peng, J., Huang, H., Chen, L., and Wang, Q. (2021). A multiple subspaces-based model: Interpreting urban functional regions with big geospatial data. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10020066"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"28735","DOI":"10.1109\/ACCESS.2020.2972309","article-title":"Fine-Grained Spatiotemporal Dynamics of Inbound Tourists Based on Geotagged Photos: A Case Study in Beijing, China","volume":"8","author":"Jing","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"991","DOI":"10.14358\/PERS.69.9.991","article-title":"Spatial metrics and image texture for mapping urban land use","volume":"69","author":"Herold","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, T., Tsou, M.H., Li, H., Jiang, W., and Guo, F. (2016). Mapping dynamic urban land use patterns with crowdsourced geo-tagged social media (Sina-Weibo) and commercial points of interest collections in Beijing, China. Sustainability, 8.","DOI":"10.3390\/su8111202"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Papadakis, E., Gao, S., and Baryannis, G. (2019). Combining design patterns and topic modeling to discover regions that support particular functionality. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8090385"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.isprsjprs.2020.02.014","article-title":"Deep learning-based remote and social sensing data fusion for urban region function recognition","volume":"163","author":"Cao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bao, H., Ming, D., Guo, Y., Zhang, K., Zhou, K., and Du, S. (2020). DFCNN-based semantic recognition of urban functional zones by integrating remote sensing data and POI data. Remote Sens., 12.","DOI":"10.3390\/rs12071088"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1080\/13658816.2014.913794","article-title":"A new insight into land use classification based on aggregated mobile phone data","volume":"28","author":"Pei","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Toole, J.L., Ulm, M., Gonz\u00e1lez, M.C., and Bauer, D. (2012, January 12). Inferring land use from mobile phone activity. Proceedings of the ACM SIGKDD International Workshop on Urban Computing, Beijing, China.","DOI":"10.1145\/2346496.2346498"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1504","DOI":"10.1111\/tgis.12654","article-title":"How urban places are visited by social groups? Evidence from matrix factorization on mobile phone data","volume":"24","author":"Kang","year":"2020","journal-title":"Trans. GIS"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.compenvurbsys.2016.08.002","article-title":"Understanding operation behaviors of taxicabs in cities by matrix factorization","volume":"60","author":"Kang","year":"2016","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.inffus.2020.11.004","article-title":"Multi-source information fusion based on rough set theory: A review","volume":"68","author":"Zhang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bayoudh, K., Knani, R., Hamdaoui, F., and Mtibaa, A. (2021). A survey on deep multimodal learning for computer vision: Advances, trends, applications, and datasets. Vis. Comput., 1\u201332.","DOI":"10.1007\/s00371-021-02166-7"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/s00521-004-0463-7","article-title":"A review of data fusion models and architectures: Towards engineering guidelines","volume":"14","author":"Esteban","year":"2005","journal-title":"Neural Comput. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.landurbplan.2004.12.005","article-title":"Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing","volume":"75","author":"Xiao","year":"2006","journal-title":"Landsc. Urban Plan."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Assem, H., Xu, L., Buda, T.S., and O\u2019Sullivan, D. (2016, January 6\u20138). Spatio-Temporal clustering approach for detecting functional regions in cities. Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA.","DOI":"10.1109\/ICTAI.2016.0063"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TBDATA.2015.2465959","article-title":"Methodologies for Cross-Domain Data Fusion: An Overview","volume":"1","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Big Data"},{"key":"ref_35","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_36","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_37","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1080\/13658816.2020.1711915","article-title":"Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata","volume":"34","author":"Zhai","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cao, K., Guo, H., and Zhang, Y. (2019). Comparison of approaches for urban functional zones classification based on multi-source geospatial data: A case study in Yuzhong District, Chongqing, China. Sustainability, 11.","DOI":"10.3390\/su11030660"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, B., Wang, Z., Mu, H., Sun, L., and Hu, F. (2019). Identification of Urban Functional Regions Based on Floating Car Track Data and POI Data. Sustainability, 11.","DOI":"10.3390\/su11236541"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7336","DOI":"10.1109\/JSTARS.2021.3091848","article-title":"An SOE-Based Learning Framework Using Multisource Big Data for Identifying Urban Functional Zones","volume":"14","author":"Feng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.rse.2019.04.014","article-title":"Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution","volume":"228","author":"Srivastava","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Gauvin, L., Panisson, A., and Cattuto, C. (2014). Detecting the community structure and activity patterns of temporal networks: A non-negative tensor factorization approach. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0086028"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, W., Ji, C., Yu, H., Zhao, Y., and Chai, Y. (2021). Interpersonal and intrapersonal variabilities in daily activity-travel patterns: A Networked spatiotemporal analysis. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10030148"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101374","DOI":"10.1016\/j.compenvurbsys.2019.101374","article-title":"Functional urban land use recognition integrating multi-source geospatial data and cross-correlations","volume":"78","author":"Zhang","year":"2019","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_45","first-page":"66","article-title":"Study of Urban Functional Zoning Based on Nuclear Density and Fusion Data","volume":"35","author":"Wang","year":"2019","journal-title":"Geogr. Geo-Inf. Sci."},{"key":"ref_46","first-page":"81","article-title":"A Poi Data-Based Study on Urban Functional Areas of the Resourcesbased City: A Case Study of Benxi, Liaoning","volume":"35","author":"Xue","year":"2020","journal-title":"Hum. Geogr."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kim, K., and Lee, K. (2018). Handling points of interest (POIs) on a mobile web map service linked to indoor geospatial objects: A case study. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7060216"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4591","DOI":"10.1109\/TII.2019.2893714","article-title":"Deep Matrix Factorization with Implicit Feedback Embedding for Recommendation System","volume":"15","author":"Yi","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_49","first-page":"1","article-title":"Generalizing DTW to the multi-dimensional case requires an adaptive approach","volume":"31","author":"Hu","year":"2016","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ma, H., Yang, H., Lyu, M.R., and King, I. (2008, January 26\u201330). SoRec: Social recommendation using probabilistic matrix factorization. Proceedings of the 17th ACM Conference on Information and Knowledge Management, Napa Valley, CA, USA.","DOI":"10.1145\/1458082.1458205"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"139668","DOI":"10.1109\/ACCESS.2019.2943600","article-title":"Double Regularization Matrix Factorization Recommendation Algorithm","volume":"7","author":"Du","year":"2019","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"298","DOI":"10.21136\/CMJ.1973.101168","article-title":"Algebraic connectivity of graphs","volume":"23","author":"Fiedler","year":"1973","journal-title":"Czechoslov. Math. J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhou, M., Lu, L., Guo, H., Weng, Q., Cao, S., Zhang, S., and Li, Q. (2021). Urban sprawl and changes in land-use efficiency in the beijing\u2013tianjin\u2013hebei region, china from 2000 to 2020: A spatiotemporal analysis using earth observation data. Remote Sens., 13.","DOI":"10.3390\/rs13152850"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sun, Z., Jiao, H., Wu, H., Peng, Z., and Liu, L. (2021). Block2vec: An approach for identifying urban functional regions by integrating sentence embedding model and points of interest. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10050339"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"He, X., Yuan, X., Zhang, D., Zhang, R., Li, M., and Zhou, C. (2021). Delineation of Urban Agglomeration Boundary Based on Multisource Big Data Fusion\u2014A Case Study of Guangdong\u2013Hong Kong\u2013Macao Greater Bay Area (GBA). Remote Sens., 13.","DOI":"10.3390\/rs13091801"},{"key":"ref_57","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_58","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":"GIScience Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111838","DOI":"10.1016\/j.rse.2020.111838","article-title":"Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities","volume":"247","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/6\/351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:33:16Z","timestamp":1760139196000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/11\/6\/351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,16]]},"references-count":59,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["ijgi11060351"],"URL":"https:\/\/doi.org\/10.3390\/ijgi11060351","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2022,6,16]]}}}