{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:08:55Z","timestamp":1760234935078,"version":"build-2065373602"},"reference-count":79,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2017YFB0503604; 2016YFE0200400"],"award-info":[{"award-number":["2017YFB0503604; 2016YFE0200400"]}]},{"name":"National Natural Science Foundation of China","award":["41971405; 41901394"],"award-info":[{"award-number":["41971405; 41901394"]}]},{"name":"China Scholarship Council","award":["201906270227"],"award-info":[{"award-number":["201906270227"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The acquisition of human trajectories facilitates movement data analytics and location-based services, but gaps in trajectories limit the extent in which many tracking datasets can be utilized. We present a model to estimate place visit probabilities at time points within a gap, based on empirical mobility patterns derived from past trajectories. Different from previous models, our model makes use of prior information from historical data to build a chain of empirically biased random walks. Therefore, it is applicable to gaps of varied lengths and can be fitted to empirical data conveniently. In this model, long gaps are broken into a chain of multiple episodes according to past patterns, while short episodes are estimated with anisotropic location transition probabilities. Experiments show that our model is able to hit almost 60% of the ground truth for short gaps of several minutes and over 40% for longer gaps up to weeks. In comparison, existing models are only able to hit less than 10% and 1% for short and long gaps, respectively. Visit probability distributions estimated by the model are useful for generating paths in data gaps, and have potential for disaggregated movement data analysis in uncertain environments.<\/jats:p>","DOI":"10.3390\/ijgi10070456","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T10:06:34Z","timestamp":1625220394000},"page":"456","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modelling Place Visit Probability Sequences during Trajectory Data Gaps Based on Movement History"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1760-0865","authenticated-orcid":false,"given":"Chang","family":"Ren","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"},{"name":"Department of Geography & Environment, Western University, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3523-8994","authenticated-orcid":false,"given":"Luliang","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jed","family":"Long","sequence":"additional","affiliation":[{"name":"Department of Geography & Environment, Western University, London, ON N6A 3K7, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihan","family":"Kan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Analysis and Visualisation of Movement: An Interdisciplinary Review","volume":"3","author":"Buchin","year":"2015","journal-title":"Mov. 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