Adapting node–place model to predict and monitor COVID-19 footprints and transmission risks
The node–place model has been widely used to classify and evaluate transit stations, which sheds light on individuals’ travel behaviors and supports urban planning through effectively integrating land use and transportation development. This study adapts this model to investigate whether and how nod...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Communications in Transportation Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772424723000215 |
| _version_ | 1797398404755619840 |
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| author | Jiali Zhou Mingzhi Zhou Jiangping Zhou Zhan Zhao |
| author_facet | Jiali Zhou Mingzhi Zhou Jiangping Zhou Zhan Zhao |
| author_sort | Jiali Zhou |
| collection | DOAJ |
| description | The node–place model has been widely used to classify and evaluate transit stations, which sheds light on individuals’ travel behaviors and supports urban planning through effectively integrating land use and transportation development. This study adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city. Moreover, the unique metric drawn from detailed visit history of the infected, i.e., the COVID-19 footprints, is proposed and exploited. This study then empirically uses the adapted model to examine the station-level factors affecting the local COVID-19 footprints. The model accounts for traditional measures of the node and place as well as actual human mobility patterns associated with the node and place. It finds that stations with high node, place, and human mobility indices normally have more COVID-19 footprints in proximity. A multivariate regression is fitted to see whether and to what degree different indices and indicators can predict the COVID-19 footprints. The results indicate that many of the place, node, and human mobility indicators significantly impact the concentration of COVID-19 footprints. These are useful for policy-makers to predict and monitor hotspots for COVID-19 and other pandemics’ transmission. |
| first_indexed | 2024-03-09T01:25:11Z |
| format | Article |
| id | doaj.art-32dd14bfc8944469b12240593ab18963 |
| institution | Directory Open Access Journal |
| issn | 2772-4247 |
| language | English |
| last_indexed | 2024-03-09T01:25:11Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Communications in Transportation Research |
| spelling | doaj.art-32dd14bfc8944469b12240593ab189632023-12-10T06:19:12ZengElsevierCommunications in Transportation Research2772-42472023-12-013100110Adapting node–place model to predict and monitor COVID-19 footprints and transmission risksJiali Zhou0Mingzhi Zhou1Jiangping Zhou2Zhan Zhao3Corresponding author.; Department of Urban Planning and Design, The University of Hong Kong, 999077, Hong Kong, ChinaDepartment of Urban Planning and Design, The University of Hong Kong, 999077, Hong Kong, ChinaDepartment of Urban Planning and Design, The University of Hong Kong, 999077, Hong Kong, ChinaDepartment of Urban Planning and Design, The University of Hong Kong, 999077, Hong Kong, ChinaThe node–place model has been widely used to classify and evaluate transit stations, which sheds light on individuals’ travel behaviors and supports urban planning through effectively integrating land use and transportation development. This study adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city. Moreover, the unique metric drawn from detailed visit history of the infected, i.e., the COVID-19 footprints, is proposed and exploited. This study then empirically uses the adapted model to examine the station-level factors affecting the local COVID-19 footprints. The model accounts for traditional measures of the node and place as well as actual human mobility patterns associated with the node and place. It finds that stations with high node, place, and human mobility indices normally have more COVID-19 footprints in proximity. A multivariate regression is fitted to see whether and to what degree different indices and indicators can predict the COVID-19 footprints. The results indicate that many of the place, node, and human mobility indicators significantly impact the concentration of COVID-19 footprints. These are useful for policy-makers to predict and monitor hotspots for COVID-19 and other pandemics’ transmission.http://www.sciencedirect.com/science/article/pii/S2772424723000215Node–place modelLand useTransportHuman mobilityCOVID-19 |
| spellingShingle | Jiali Zhou Mingzhi Zhou Jiangping Zhou Zhan Zhao Adapting node–place model to predict and monitor COVID-19 footprints and transmission risks Communications in Transportation Research Node–place model Land use Transport Human mobility COVID-19 |
| title | Adapting node–place model to predict and monitor COVID-19 footprints and transmission risks |
| title_full | Adapting node–place model to predict and monitor COVID-19 footprints and transmission risks |
| title_fullStr | Adapting node–place model to predict and monitor COVID-19 footprints and transmission risks |
| title_full_unstemmed | Adapting node–place model to predict and monitor COVID-19 footprints and transmission risks |
| title_short | Adapting node–place model to predict and monitor COVID-19 footprints and transmission risks |
| title_sort | adapting node place model to predict and monitor covid 19 footprints and transmission risks |
| topic | Node–place model Land use Transport Human mobility COVID-19 |
| url | http://www.sciencedirect.com/science/article/pii/S2772424723000215 |
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