Modeling of Time Geographical Kernel Density Function under Network Constraints
Time geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/3/184 |
| _version_ | 1827648651897339904 |
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| author | Zhangcai Yin Kuan Huang Shen Ying Wei Huang Ziqiang Kang |
| author_facet | Zhangcai Yin Kuan Huang Shen Ying Wei Huang Ziqiang Kang |
| author_sort | Zhangcai Yin |
| collection | DOAJ |
| description | Time geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods construct a kernel density function under network constraints based on the principle of least effort and consider that each point of the shortest path between anchor points has the same density value. This, however, ignores the attenuation effect with the distance to the anchor point according to the first law of geography. For this reason, this article studies the kernel function framework based on the unity of the principle of least effort and the first law of geography, and it establishes a mechanism for fusing the extended traditional model with the attenuation model with the distance to the anchor point, thereby forming a kernel density function of time geography under network constraints that can approximate the theoretical prototype of the Brownian bridge and providing a theoretical basis for reducing the uncertainty of the density estimation of the transportation network space. Finally, the empirical comparison with taxi trajectory data shows that the proposed model is effective. |
| first_indexed | 2024-03-09T19:44:06Z |
| format | Article |
| id | doaj.art-238d495c895b43e5b86005dc8e8312a5 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-09T19:44:06Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-238d495c895b43e5b86005dc8e8312a52023-11-24T01:28:29ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-03-0111318410.3390/ijgi11030184Modeling of Time Geographical Kernel Density Function under Network ConstraintsZhangcai Yin0Kuan Huang1Shen Ying2Wei Huang3Ziqiang Kang4School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, ChinaTime geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods construct a kernel density function under network constraints based on the principle of least effort and consider that each point of the shortest path between anchor points has the same density value. This, however, ignores the attenuation effect with the distance to the anchor point according to the first law of geography. For this reason, this article studies the kernel function framework based on the unity of the principle of least effort and the first law of geography, and it establishes a mechanism for fusing the extended traditional model with the attenuation model with the distance to the anchor point, thereby forming a kernel density function of time geography under network constraints that can approximate the theoretical prototype of the Brownian bridge and providing a theoretical basis for reducing the uncertainty of the density estimation of the transportation network space. Finally, the empirical comparison with taxi trajectory data shows that the proposed model is effective.https://www.mdpi.com/2220-9964/11/3/184time geographykernel density estimationpotential network areaspace–time trajectory |
| spellingShingle | Zhangcai Yin Kuan Huang Shen Ying Wei Huang Ziqiang Kang Modeling of Time Geographical Kernel Density Function under Network Constraints ISPRS International Journal of Geo-Information time geography kernel density estimation potential network area space–time trajectory |
| title | Modeling of Time Geographical Kernel Density Function under Network Constraints |
| title_full | Modeling of Time Geographical Kernel Density Function under Network Constraints |
| title_fullStr | Modeling of Time Geographical Kernel Density Function under Network Constraints |
| title_full_unstemmed | Modeling of Time Geographical Kernel Density Function under Network Constraints |
| title_short | Modeling of Time Geographical Kernel Density Function under Network Constraints |
| title_sort | modeling of time geographical kernel density function under network constraints |
| topic | time geography kernel density estimation potential network area space–time trajectory |
| url | https://www.mdpi.com/2220-9964/11/3/184 |
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