{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:16:09Z","timestamp":1760235369976,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T00:00:00Z","timestamp":1629158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Henan Province","award":["182300410005"],"award-info":[{"award-number":["182300410005"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801388"],"award-info":[{"award-number":["41801388"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fitting nonlinear spatio-temporal relationships and incorporates users\u2019 geographic interactions to construct spatial relationships in the form of graphs as the input. The model consists of a graph convolutional network (GCN) and a gated recurrent unit (GRU). The GCN, which is efficient at processing graphs, extracts spatial features. These features are then input into the GRU, which extracts their temporal features. Finally, the GRU output is passed through a fully connected layer to obtain the predictions. We validated this model using a social media check-in dataset and found that the geographical interactions graph construction method performs better than the baselines. This indicates that our model is appropriate for fitting the complex nonlinear spatio-temporal relationships that characterize user mobility and helps improve prediction accuracy when considering geographic flows.<\/jats:p>","DOI":"10.3390\/ijgi10080555","type":"journal-article","created":{"date-parts":[[2021,8,17]],"date-time":"2021-08-17T21:27:40Z","timestamp":1629235660000},"page":"555","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting User Activity Intensity Using Geographic Interactions Based on Social Media Check-In Data"],"prefix":"10.3390","volume":"10","author":[{"given":"Jing","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Data and Target Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Wenyue","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Haiyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Data and Target Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3332-9668","authenticated-orcid":false,"given":"Anzhu","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Jia","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Data and Target Engineering, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1080\/13658816.2020.1808896","article-title":"A BiLSTM-CNN model for predicting users\u2019 next locations based on geotagged social media","volume":"35","author":"Bao","year":"2021","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1109\/TKDE.2012.153","article-title":"T-finder: A recommender system for finding passengers and vacant taxis","volume":"25","author":"Yuan","year":"2013","journal-title":"IEEE Trans. Knowl. DataEng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.trc.2015.03.014","article-title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data","volume":"54","author":"Ma","year":"2015","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhou, X., and Noulas, A. (2018, January 13). Predicting the Spatio-Temporal Evolution of Chronic Diseases in Population with Human Mobility Data. Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/497"},{"key":"ref_5","unstructured":"Balcan, D., Colizza, V., and Goncalves, B. (2010, January 15\u201319). Multiscale mobility networks and the large scale spreading of infectious diseases. Proceedings of the APS March Meeting, Portland, OR, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Leskovec, J., and Horvitz, E. (2008, January 21\u201325). Planetary-scale views on a large instant-messaging network. Proceedings of the 17th International Conference on World Wide Web, Beijing, China.","DOI":"10.1145\/1367497.1367620"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Vaccari, A., Liu, L., and Biderman, A. (2009, January 4\u20137). A holistic framework for the study of urban traces and the profiling of urban processes and dynamics. Proceedings of the 2009 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA.","DOI":"10.1109\/ITSC.2009.5309518"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TITS.2012.2209201","article-title":"Land-Use Classification Using Taxi GPS Traces","volume":"14","author":"Gang","year":"2013","journal-title":"IEEE Trans. Intell. Transp.Syst."},{"key":"ref_9","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_10","first-page":"665","article-title":"Research on Human Mobility in Big Data Era","volume":"16","author":"Feng","year":"2014","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1080\/13658816.2020.1718153","article-title":"Exploring the heterogeneity of human urban movements using geo-tagged tweets","volume":"34","author":"Ding","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Scellato, S., Musolesi, M., and Mascolo, C. (2011). NextPlace: A Spatio-Temporal Prediction Framework for Pervasive Systems, Springer.","DOI":"10.1007\/978-3-642-21726-5_10"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liao, D., Liu, W., and Zhong, Y. (2018, January 13). Predicting Activity and Location with Multi-task Context Aware Recurrent Neural Network. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/477"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1080\/19475683.2020.1768438","article-title":"Integration of spatialization and individualization: The future of epidemic modelling for communicable diseases","volume":"26","author":"Li","year":"2020","journal-title":"Ann. GIS"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"573","DOI":"10.3390\/ijerph15040573","article-title":"Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data","volume":"15","author":"Bin","year":"2018","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Cho, E., Myers, S.A., and Leskovec, J. (2011, January 21\u201324). Friendship and mobility: User movement in location-based social networks. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA.","DOI":"10.1145\/2020408.2020579"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s11704-011-1192-6","article-title":"Prediction of urban human mobility using large-scale taxi traces and its applications","volume":"6","author":"Li","year":"2012","journal-title":"Front. Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liang, V.C. (2016, January 16\u201320). Mercury: Metro density prediction with recurrent neural network on streaming CDR data. Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland.","DOI":"10.1109\/ICDE.2016.7498348"},{"key":"ref_19","unstructured":"Hoang, M.X., Zheng, Y., and Singh, A.K. (November, January 31). FCCF: Forecasting citywide crowd flows based on big data. Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Burlingame, CA, USA."},{"key":"ref_20","first-page":"2009","article-title":"Integrating social network data into GISystems","volume":"30","author":"Andris","year":"2016","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.jtrangeo.2008.11.012","article-title":"A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical\u2013virtual space","volume":"17","author":"Shaw","year":"2009","journal-title":"J. Transp. Geogr."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Crivellari, A., and Beinat, E. (2019). From motion activity to geo-embeddings: Generating and exploring vector representations of locations, traces and visitors through large-scale mobility data. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030134"},{"key":"ref_23","first-page":"522","article-title":"A spatio-temporal network for human activity prediction based on deep learning","volume":"50","author":"Li","year":"2021","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1080\/13658816.2018.1460753","article-title":"Fine-grained prediction of urban population using mobile phone location data","volume":"32","author":"Chen","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_25","unstructured":"Wang, S., Yao, Z., and Yang, S. (2018). Discovering Urban Travel Demands Through Dynamic Zone Correlation in Location-Based Social Networks. Joint European Conference on Machine Learning & Knowledge Discovery in Databases, Springer."},{"key":"ref_26","first-page":"408","article-title":"Understanding place characteristics in geographic contexts through graph convolutional neural networks","volume":"110","author":"Zhu","year":"2020","journal-title":"Ann. Am. Assoc. Geogr."},{"key":"ref_27","unstructured":"Castells, M. (1996). Rise of the Network Society: The Information Age: Economy, Society and Culture, Blackwell Publishers, Inc."},{"key":"ref_28","unstructured":"Xiu, C.L., and Wei, Y. (2015). City and Regional Structure from the View of \u201cSpace of Flows\u201d, Science Press."},{"key":"ref_29","first-page":"30","article-title":"The concept and classification of spatial patterns of geographical flow","volume":"22","author":"Pei","year":"2020","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_30","first-page":"29","article-title":"Trajectory data mining: An overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Deng, D., Shahabi, C., and Demiryurek, U. (2016, January 13\u201317). Latent Space Model for Road Networks to Predict Time-Varying Traffic. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939860"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Deveaud, R., Albakour, M.D., and Macdonald, C. (2015, January 19\u201323). Experiments with a Venue-Centric Model for Personalised and Time-Aware Venue Suggestion. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, Australia.","DOI":"10.1145\/2806416.2806484"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","article-title":"A tutorial on support vector regression","volume":"14","author":"Smola","year":"2014","journal-title":"Stats Comput."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., and Qi, D. (2016, January 4\u20139). Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1080\/13658816.2019.1652303","article-title":"A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes","volume":"34","author":"Ren","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yao, H., Wu, F., and Ke, J. (2018, January 2\u20137). Deep multi-view spatial-temporal network for taxi demand prediction. Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","article-title":"T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction","volume":"21","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.isprsjprs.2019.02.010","article-title":"A graph convolutional neural network for classification of building patterns using spatial vector data","volume":"150","author":"Yan","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1080\/13658816.2017.1413192","article-title":"Inferring spatial interaction patterns from sequential snapshots of spatial distributions","volume":"32","author":"Zhu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_40","unstructured":"Bruna, J., Zaremba, W., Szlam, A., and Lecun, Y. (2014, January 14\u201316). Spectral networks and locally connected networks on graphs. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_41","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-supervised classification with graph convolutional networks. Proceedings of the International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_42","unstructured":"Hoang, N.T. (2021, January 10\u201315). Takanori Maehara, Revisiting Graph Neural Networks: Graph Filtering Perspective. Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan, Italy."},{"key":"ref_43","unstructured":"Chung, J., Gulcehre, C., and Cho, K. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1080\/00045608.2015.1081120","article-title":"Modeling and visualizing regular human mobility patterns with uncertainty: An example using Twitter data","volume":"105","author":"Huang","year":"2015","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_45","unstructured":"Cheng, Z., Caverlee, J., Lee, K., and Sui, D. (2011, January 17\u201321). Exploring millions of footprints in location sharing services. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, Barcelona, Spain."},{"key":"ref_46","first-page":"82","article-title":"A summary of traffic flow forecasting methods","volume":"21","author":"Liu","year":"2004","journal-title":"Highway Transp. Res. Dev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1016\/0306-4573(88)90021-0","article-title":"Term-weighting approaches in automatic text retrieval","volume":"24","author":"Salton","year":"1988","journal-title":"Inf. Process. Manag."},{"key":"ref_48","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7\u20139). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/555\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:45:50Z","timestamp":1760165150000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/10\/8\/555"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,17]]},"references-count":48,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["ijgi10080555"],"URL":"https:\/\/doi.org\/10.3390\/ijgi10080555","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2021,8,17]]}}}