Probabilistic Time Geographic Modeling Method Considering POI Semantics
Bibliographic Data
| ID | 22032559 |
|---|---|
| Authors | Ai-Sheng Wang (0009-0004-6872-3317, Wuhan University of Technology), Zhangcai Yin (0000-0001-5061-8050, Wuhan University of Technology, corresponding author), Zhang-Cai Yin (Wuhan University of Technology), Shen Ying (0000-0001-8066-9203, Wuhan University) |
| Year | 2024 |
| Volume | 13 |
| Issue | 1 |
| Pages | 22 |
| Publication date | 2024-01-08 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi13010022 |
| OpenAlex | W4390663048 |
| Language | EN |
| Citations received | 1 |
| References cited | 31 |
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–time 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–time 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
Data mining · Geography · Information retrieval · Point of interest · Probabilistic logic · Statistical model · Time geography · Trajectory · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Human Mobility and Location-Based Analysis · Mathematics · Artificial Intelligence
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |