Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data
Data-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/10/8/545 |
| _version_ | 1797523635799326720 |
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| author | Shaojun Liu Yi Long Ling Zhang Hao Liu |
| author_facet | Shaojun Liu Yi Long Ling Zhang Hao Liu |
| author_sort | Shaojun Liu |
| collection | DOAJ |
| description | Data-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable development, etc. However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. Existing approaches for modeling and identifying human activities based on massive social sensing data rely on a large number of valid supervised samples or limited prior knowledge. This paper proposes an effective methodology for building human activity chains based on mobile phone signaling data and labeling activity purpose semantics to analyze human activity patterns, spatiotemporal behavior, and urban dynamics. We fully verified the effectiveness and accuracy of the proposed method in human daily activity process construction and activity purpose identification through accuracy comparison and spatial-temporal distribution exploration. This study further confirms the possibility of using big data to observe urban human spatiotemporal behavior. |
| first_indexed | 2024-03-10T08:45:51Z |
| format | Article |
| id | doaj.art-6164f7d904304a0099f79f63fee92174 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T08:45:51Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-6164f7d904304a0099f79f63fee921742023-11-22T07:53:29ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-08-0110854510.3390/ijgi10080545Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling DataShaojun Liu0Yi Long1Ling Zhang2Hao Liu3Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaKey Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, ChinaData-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable development, etc. However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. Existing approaches for modeling and identifying human activities based on massive social sensing data rely on a large number of valid supervised samples or limited prior knowledge. This paper proposes an effective methodology for building human activity chains based on mobile phone signaling data and labeling activity purpose semantics to analyze human activity patterns, spatiotemporal behavior, and urban dynamics. We fully verified the effectiveness and accuracy of the proposed method in human daily activity process construction and activity purpose identification through accuracy comparison and spatial-temporal distribution exploration. This study further confirms the possibility of using big data to observe urban human spatiotemporal behavior.https://www.mdpi.com/2220-9964/10/8/545mobile phone signaling datamobile sensinghuman behaviorurban dynamicsgraph neural network |
| spellingShingle | Shaojun Liu Yi Long Ling Zhang Hao Liu Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data ISPRS International Journal of Geo-Information mobile phone signaling data mobile sensing human behavior urban dynamics graph neural network |
| title | Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data |
| title_full | Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data |
| title_fullStr | Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data |
| title_full_unstemmed | Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data |
| title_short | Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data |
| title_sort | semantic enhancement of human urban activity chain construction using mobile phone signaling data |
| topic | mobile phone signaling data mobile sensing human behavior urban dynamics graph neural network |
| url | https://www.mdpi.com/2220-9964/10/8/545 |
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