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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Main Authors: Disheng Yi, Yusi Liu, Jiahui Qin, Jing Zhang
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/12/22/9662
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spelling 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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