Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation
Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and nigh...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-01-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/9/1/41 |
| _version_ | 1819012906376757248 |
|---|---|
| author | Wei Chen Yao Fang Qing Zhai Wei Wang Yijie Zhang |
| author_facet | Wei Chen Yao Fang Qing Zhai Wei Wang Yijie Zhang |
| author_sort | Wei Chen |
| collection | DOAJ |
| description | Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning. |
| first_indexed | 2024-12-21T01:51:30Z |
| format | Article |
| id | doaj.art-dca8673616ac4ed5b1e15542ff8f28a1 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-21T01:51:30Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-dca8673616ac4ed5b1e15542ff8f28a12022-12-21T19:19:53ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-01-01914110.3390/ijgi9010041ijgi9010041Assessing Emergency Shelter Demand Using POI Data and Evacuation SimulationWei Chen0Yao Fang1Qing Zhai2Wei Wang3Yijie Zhang4School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaCollege of Architecture, Nanjing Tech University, Nanjing 211816, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, ChinaSchool of Business Administration, Nanjing University of Finance and Economics, Nanjing 210023, ChinaMapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning.https://www.mdpi.com/2220-9964/9/1/41emergency sheltershelter demand assessmentpoint of interestevacuation simulationpython programming languageguangzhou |
| spellingShingle | Wei Chen Yao Fang Qing Zhai Wei Wang Yijie Zhang Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation ISPRS International Journal of Geo-Information emergency shelter shelter demand assessment point of interest evacuation simulation python programming language guangzhou |
| title | Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation |
| title_full | Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation |
| title_fullStr | Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation |
| title_full_unstemmed | Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation |
| title_short | Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation |
| title_sort | assessing emergency shelter demand using poi data and evacuation simulation |
| topic | emergency shelter shelter demand assessment point of interest evacuation simulation python programming language guangzhou |
| url | https://www.mdpi.com/2220-9964/9/1/41 |
| work_keys_str_mv | AT weichen assessingemergencyshelterdemandusingpoidataandevacuationsimulation AT yaofang assessingemergencyshelterdemandusingpoidataandevacuationsimulation AT qingzhai assessingemergencyshelterdemandusingpoidataandevacuationsimulation AT weiwang assessingemergencyshelterdemandusingpoidataandevacuationsimulation AT yijiezhang assessingemergencyshelterdemandusingpoidataandevacuationsimulation |