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Open AccessArticle

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

1
School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2
College of Architecture, Nanjing Tech University, Nanjing 211816, China
3
School of Business Administration, Nanjing University of Finance and Economics, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(1), 41; https://doi.org/10.3390/ijgi9010041
Received: 25 November 2019 / Revised: 2 January 2020 / Accepted: 13 January 2020 / Published: 14 January 2020
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.
Keywords: emergency shelter; shelter demand assessment; point of interest; evacuation simulation; Python programming language; Guangzhou emergency shelter; shelter demand assessment; point of interest; evacuation simulation; Python programming language; Guangzhou
MDPI and ACS Style

Chen, W.; Fang, Y.; Zhai, Q.; Wang, W.; Zhang, Y. Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation. ISPRS Int. J. Geo-Inf. 2020, 9, 41.

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