TY - JOUR
T1 - Measurement of disaster damage utilizing disaster statistics
T2 - A case study analyzing the data of Indonesia
AU - Sasaki, Daisuke
AU - Okumura, Makoto
AU - Ono, Yuichi
N1 - Funding Information:
This study was partly supported by JSPS KAKENHI Grant Number JP16K13344, JP19K20540, and the Ensemble Grant for Early Career Researchers at Tohoku University.
Publisher Copyright:
© 2020, Fuji Technology Press. All rights reserved.
PY - 2020
Y1 - 2020
N2 - The Global Centre for Disaster Statistics (GCDS) in Tohoku University was established in April 2015. One of its main missions is to support the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) in the monitoring and evaluation of progress by providing support at a national level for building the capacity to develop nationwide statistics on disaster damage and by establishing an improved global database for such statistics. The objective of this study was to find clues for the effective measurement of disaster damage utilizing disaster statistics. In disaster loss databases, we often encounter so many observed variables that it is difficult to establish how severe each disaster was in total. Thus, it was considered that introducing a whole new compound indicator to estimate the scale of each disaster properly would be beneficial. In this context, the authors conducted a principal component analysis (PCA) to introduce new compound indicators. The material data for the analysis were retrieved via the global disaster-related database (GDB) provided by the GCDS. Consequently, it was posited that the score of the first principal component, calculated by a PCA, could be an effective indicator to estimate the specific impact of a disaster. We believe that the findings and proposal of a new indicator in this study will contribute to the literature in that new clues to establish an evidence-based criteria and threshold of disaster data collection are provided.
AB - The Global Centre for Disaster Statistics (GCDS) in Tohoku University was established in April 2015. One of its main missions is to support the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) in the monitoring and evaluation of progress by providing support at a national level for building the capacity to develop nationwide statistics on disaster damage and by establishing an improved global database for such statistics. The objective of this study was to find clues for the effective measurement of disaster damage utilizing disaster statistics. In disaster loss databases, we often encounter so many observed variables that it is difficult to establish how severe each disaster was in total. Thus, it was considered that introducing a whole new compound indicator to estimate the scale of each disaster properly would be beneficial. In this context, the authors conducted a principal component analysis (PCA) to introduce new compound indicators. The material data for the analysis were retrieved via the global disaster-related database (GDB) provided by the GCDS. Consequently, it was posited that the score of the first principal component, calculated by a PCA, could be an effective indicator to estimate the specific impact of a disaster. We believe that the findings and proposal of a new indicator in this study will contribute to the literature in that new clues to establish an evidence-based criteria and threshold of disaster data collection are provided.
KW - Disaster loss database
KW - Disaster statistics
KW - Global Centre for Disaster Statistics (GCDS)
KW - Principal component analysis
KW - Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR)
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U2 - 10.20965/jdr.2020.p0970
DO - 10.20965/jdr.2020.p0970
M3 - Article
AN - SCOPUS:85097495852
SN - 1881-2473
VL - 15
SP - 970
EP - 974
JO - Journal of Disaster Research
JF - Journal of Disaster Research
IS - 7
ER -