{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T20:39:24Z","timestamp":1783197564923,"version":"3.54.6"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Natural Science Foundation of China","award":["42171415"],"award-info":[{"award-number":["42171415"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The possibility of moving objects accessing different types of points of interest (POIs) at specific times is not always the same, so quantitative time geography research needs to consider the actual POI semantic information, including POI attributes and time information. Existing methods allocate probabilities to position points, including POIs, based on space\u2013time position information, but ignore the semantic information of POIs. The accessing activities of moving objects in different POIs usually have obvious time characteristics, such as dinner usually taking place around 6 PM. In this paper, building upon existing probabilistic time geographic methods, we introduce POI attributes and their time preferences to propose a probabilistic time geographic model for assigning probabilities to POI accesses. This model provides a comprehensive measure of position probability with space\u2013time uncertainty between known trajectory points, incorporating time, space, and semantic information, thereby avoiding data gaps caused by single-dimensional information. Experimental results demonstrate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/ijgi13010022","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T05:21:38Z","timestamp":1704691298000},"page":"22","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Probabilistic Time Geographic Modeling Method Considering POI Semantics"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6872-3317","authenticated-orcid":false,"given":"Ai-Sheng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhang-Cai","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8066-9203","authenticated-orcid":false,"given":"Shen","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Winter, S. (2009, January 4\u20136). Towards a Probabilistic Time Geography. Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Washinghton, DC, USA.","DOI":"10.1145\/1653771.1653861"},{"key":"ref_2","unstructured":"Downs, J.A. (2010). Geographic Information Science: 6th International Conference, GIScience 2010, Zurich, Switzerland, 14\u201317 September 2010, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1080\/13658816.2017.1421764","article-title":"Testing time-geographic density estimation for home range analysis using an agent-based model of animal movement","volume":"32","author":"Downs","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1080\/13658816.2018.1428748","article-title":"Random encounters in probabilistic time geography","volume":"32","author":"Yin","year":"2018","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_5","first-page":"1006","article-title":"Adaptive-Velocity Time-Geographic Density Estimation for Mapping the Potential and Probable Locations of Mobile Objects","volume":"41","author":"Downs","year":"2014","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_6","first-page":"381","article-title":"Necessary Space-Time Conditions for Human Interaction","volume":"32","author":"Miller","year":"2005","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yin, Z., Huang, K., Ying, S., Huang, W., and Kang, Z. (2022). Modeling of Time Geographical Kernel Density Function under Network Constraints. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11030184"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/BF01936872","article-title":"What about People in Regional Science?","volume":"24","year":"1970","journal-title":"Pap. Reg. Sci. Assoc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1023\/A:1007067322523","article-title":"Time-geography \u2014At the end of its beginning","volume":"48","author":"Lenntorp","year":"1999","journal-title":"GeoJournal"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"141","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_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jtrangeo.2012.04.007","article-title":"Guest editorial introduction: Time geography\u2014Its past, present and future","volume":"23","author":"Shaw","year":"2012","journal-title":"J. Transp. Geogr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1111\/j.1538-4632.2005.00575.x","article-title":"A measurement theory for time geography","volume":"37","author":"Miller","year":"2005","journal-title":"Geogr. Anal."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1002\/jwmg.845","article-title":"Home range and habitat analysis using dynamic time geography","volume":"79","author":"Long","year":"2015","journal-title":"J. Wildl. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1111\/tgis.12666","article-title":"A note on measuring the volume of space-time prisms and the area of their spatial projections","volume":"24","author":"Elias","year":"2020","journal-title":"Trans. GIS"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s40462-019-0158-4","article-title":"Potential path volume (PPV): A geometric estimator for space use in 3D","volume":"7","author":"Long","year":"2019","journal-title":"Mov. Ecol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1080\/13658811003619150","article-title":"Directed movements in probabilistic time geography","volume":"24","author":"Winter","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.apgeog.2014.08.010","article-title":"Quantifying spatio-temporal interactions of animals using probabilistic space\u2013time prisms","volume":"55","author":"Downs","year":"2014","journal-title":"Appl. Geogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1080\/13658816.2013.850170","article-title":"Voxel-based probabilistic space-time prisms for analysing animal movements and habitat use","volume":"28","author":"Downs","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1080\/13658816.2013.830308","article-title":"Simulating Visit Probability Distributions within Planar Space-Time Prisms","volume":"28","author":"Song","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1080\/13658816.2013.818151","article-title":"Toward a kinetic-based probabilistic time geography","volume":"28","author":"Long","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"You, Q., and Krumm, J. (2014). Transit Tomography Using Probabilistic Time Geography: Planning Routes without a Road Map, Taylor & Francis, Inc.","DOI":"10.1080\/17489725.2014.963180"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1080\/13658816.2015.1005094","article-title":"Points of interest recommendation from GPS trajectories","volume":"29","author":"Liu","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci. IJGIS"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"101597","DOI":"10.1016\/j.compenvurbsys.2021.101597","article-title":"Assessing the influence of point-of-interest features on the classification of place categories","volume":"86","author":"Milias","year":"2021","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.future.2019.05.065","article-title":"A novel next new point-of-interest recommendation system based on simulated user travel decision-making process","volume":"100","author":"Jiao","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"34749","DOI":"10.1007\/s11042-023-14862-8","article-title":"Automatic construction of POI address lists at city streets from geo-tagged photos and web data: A case study of San Jose City","volume":"82","author":"Bui","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jiao, H., and Xiao, M. (2022). Delineating Urban Community Life Circles for Large Chinese Cities Based on Mobile Phone Data and POI Data\u2014The Case of Wuhan. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11110548"},{"key":"ref_27","first-page":"20","article-title":"Research on Spatial Pattern and Its Industrial Distribution of Commercial Space in Mianyang Based on POI Data","volume":"8","author":"Zheng","year":"2020","journal-title":"J. Data Anal. Inf. Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lim, N., Hooi, B., Ng, S.-K., Goh, Y.L., Weng, R., and Tan, R. (2022, January 11\u201315). Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.","DOI":"10.1145\/3477495.3531989"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2873055","article-title":"Joint Modeling of User Check-in Behaviors for Real-time Point-of-Interest Recommendation","volume":"35","author":"Yin","year":"2017","journal-title":"ACM Trans. Inf. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103240","DOI":"10.1016\/j.jtrangeo.2021.103240","article-title":"Assessing individual activity-related exposures to traffic congestion using GPS trajectory data","volume":"98","author":"Kan","year":"2022","journal-title":"J. Transp. Geogr."},{"key":"ref_31","unstructured":"Zeng, L., Liu, Y., Qing, R., Zhong, K., Liu, M., Liao, Z., and Zhao, Y. (2022). Advances in Intelligent Automation and Soft Computing, Springer International Publishin."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1111\/j.1467-9671.2008.01107.x","article-title":"Network Density Estimation: A GIS Approach for Analysing Point Patterns in a Network Space","volume":"12","author":"Borruso","year":"2008","journal-title":"Trans. GIS"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.jtrangeo.2010.07.003","article-title":"Dynamic accessibility mapping using floating car data: A network-constrained density estimation approach","volume":"19","author":"Li","year":"2011","journal-title":"J. Transp. Geogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.jtrangeo.2012.03.017","article-title":"Probabilistic potential path trees for visualizing and analyzing vehicle tracking data","volume":"23","author":"Downs","year":"2012","journal-title":"J. Transp. Geogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.trc.2017.01.020","article-title":"Real-time trip purpose prediction using online location-based search and discovery services","volume":"77","author":"Ermagun","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5481","DOI":"10.5194\/gmd-15-5481-2022","article-title":"Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","volume":"14","author":"Hodson","year":"2022","journal-title":"Geosci. Model Dev."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/1\/22\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:41:58Z","timestamp":1760103718000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/13\/1\/22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,8]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["ijgi13010022"],"URL":"https:\/\/doi.org\/10.3390\/ijgi13010022","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,8]]}}}