Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing
Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unrema...
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doaj-fadb0b7cfd6a4431bdc588b4e02405a42020-11-25T04:01:22ZengMDPI AGSustainability2071-10502020-11-01129662966210.3390/su12229662Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of BeijingDisheng Yi0Yusi Liu1Jiahui Qin2Jing Zhang3College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaCollege of Resources Environment and Tourism, Capital Normal University, Beijing 100048, ChinaExploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.https://www.mdpi.com/2071-1050/12/22/9662hotspotsspatio-temporal data fieldspatial interactionurban travellingtrajectory data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Disheng Yi Yusi Liu Jiahui Qin Jing Zhang |
spellingShingle |
Disheng Yi Yusi Liu Jiahui Qin Jing Zhang Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing Sustainability hotspots spatio-temporal data field spatial interaction urban travelling trajectory data |
author_facet |
Disheng Yi Yusi Liu Jiahui Qin Jing Zhang |
author_sort |
Disheng Yi |
title |
Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing |
title_short |
Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing |
title_full |
Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing |
title_fullStr |
Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing |
title_full_unstemmed |
Identifying Urban Traveling Hotspots Using an Interaction-Based Spatio-Temporal Data Field and Trajectory Data: A Case Study within the Sixth Ring Road of Beijing |
title_sort |
identifying urban traveling hotspots using an interaction-based spatio-temporal data field and trajectory data: a case study within the sixth ring road of beijing |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2020-11-01 |
description |
Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management. |
topic |
hotspots spatio-temporal data field spatial interaction urban travelling trajectory data |
url |
https://www.mdpi.com/2071-1050/12/22/9662 |
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