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A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Dados Bibliográficos

ID15469490
AutoresXiaoxin Zhu (0000-0003-4838-4907, Qingdao University of Science and Technology), Yanyan Wang (0000-0002-6312-206X, Tsinghua University, autor correspondente), David Regan (School of Foreign Studies, China University of Petroleum, Qingdao 266580, China), David G Regan (0000-0002-2893-0969, China University of Petroleum, East China), Baiqing Sun (0000-0003-3384-5029, Harbin Institute of Technology)
Ano2020
Volume17
Fascículo19
Páginas7162-7162
Data de publicação2020-09-30
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoInternational Journal of Environmental Research and Public Health (JOURNAL)
Identificadores do periódicoISSN: 1661-7827 • E-ISSN: 1660-4601
EditoraMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17197162
PMID33007885
OpenAlexW3090681537
IdiomaEN
Referências citadas24

Awareness of the requested quantity and characteristics of emergency supplies is crucial for facilitating an efficient relief operation. With the aim of focusing on the quantitative study of immediate food supplies, this article estimates the numerical autoregressive integrative moving average (ARIMA) model based on the actual data of 14 key commodities in the Sendai City of Japan during the 2011 Tohoku earthquake. Although the temporal patterns of key food commodity groups are qualitatively similar, the results show that they follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. A key finding is that 3 of the 14 items are significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements so as to promptly obtain them from distributors

Autoregressive integrated moving average · Autoregressive model · Business · Commodity · Computer security · Econometrics · Economics · Key (lock · Machine learning · Operations research · Statistics · Time series · Computer Science · Disaster Management and Resilience · Evacuation and Crowd Dynamics · Facility Location and Emergency Management · Mathematics · Finance

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