TY - JOUR
T1 - Disaster resilience in Pakistan
T2 - A comprehensive multi-dimensional spatial profiling
AU - Sajjad, Muhammad
N1 - Funding Information:
There were no dedicated funds available for this study. The author is partially funded by the City University of Hong Kong , Hong Kong ( SAR ). The research is conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The data used here are freely available and the resources are mentioned within the paper.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2021/1
Y1 - 2021/1
N2 - Building disaster-resilient communities require operative resilience frameworks enabling factual decision-making and resource allocation at national and sub-national scales. While Pakistan is frequently hit by several natural hazards (i.e., floods, droughts, earthquakes, and extreme heatwaves) resulting in devastating impacts, no national-level higher-resolution disaster resilience information is available to provide references for informed planning. Hence, this study provides a, first of its kind, multi-level comprehensive disaster resilience evaluation in Pakistan. To do so, data on a customized list of indicators within three key resilience sub-components (i.e., economic, institutional, and social) are acquired to compute a resilience index. Frequency distribution and the Analysis of Variance (ANOVA) methods are employed to analyse the differences between different resilience indices and a cross-regional assessment is carried out at the sub-national level. Subsequently, an extensive spatial assessment is performed using geo-information models (i.e., Global Moran's I, Local Indicators of Spatial Association, and machine learning-based multivariate clustering) to explore the global and local geographies of the resilience. Based on ANOVA, significant differences between the resilience sub-components are found (95% confidence). The geographical distribution of resilience scores ascertains a large spatial heterogeneity across the study area with the least resilient regions belonging to Sindh and Balochistan provinces (95% confidence). As shown by the machine learning-based multivariate clustering, the least resilient regions particularly lack in economic and institutional aspects of disaster resilience. The findings provide important references to ensure resilience management-related cross-regional equity and justice. The rigorous analyses regarding the geographies of disaster resilience in Pakistan are important to support the country's disaster risk reduction efforts. While the results are useful for practitioners, decision-makers, and professionals in the risk management field, the study has important policy-relevant implications in the context of disaster risk mitigation strategies.
AB - Building disaster-resilient communities require operative resilience frameworks enabling factual decision-making and resource allocation at national and sub-national scales. While Pakistan is frequently hit by several natural hazards (i.e., floods, droughts, earthquakes, and extreme heatwaves) resulting in devastating impacts, no national-level higher-resolution disaster resilience information is available to provide references for informed planning. Hence, this study provides a, first of its kind, multi-level comprehensive disaster resilience evaluation in Pakistan. To do so, data on a customized list of indicators within three key resilience sub-components (i.e., economic, institutional, and social) are acquired to compute a resilience index. Frequency distribution and the Analysis of Variance (ANOVA) methods are employed to analyse the differences between different resilience indices and a cross-regional assessment is carried out at the sub-national level. Subsequently, an extensive spatial assessment is performed using geo-information models (i.e., Global Moran's I, Local Indicators of Spatial Association, and machine learning-based multivariate clustering) to explore the global and local geographies of the resilience. Based on ANOVA, significant differences between the resilience sub-components are found (95% confidence). The geographical distribution of resilience scores ascertains a large spatial heterogeneity across the study area with the least resilient regions belonging to Sindh and Balochistan provinces (95% confidence). As shown by the machine learning-based multivariate clustering, the least resilient regions particularly lack in economic and institutional aspects of disaster resilience. The findings provide important references to ensure resilience management-related cross-regional equity and justice. The rigorous analyses regarding the geographies of disaster resilience in Pakistan are important to support the country's disaster risk reduction efforts. While the results are useful for practitioners, decision-makers, and professionals in the risk management field, the study has important policy-relevant implications in the context of disaster risk mitigation strategies.
KW - Disaster risk reduction
KW - Geospatial analysis
KW - Machine learning
KW - Natural hazards
KW - Resilience mapping
KW - Spatial statistical tools
UR - http://www.scopus.com/inward/record.url?scp=85097085407&partnerID=8YFLogxK
U2 - 10.1016/j.apgeog.2020.102367
DO - 10.1016/j.apgeog.2020.102367
M3 - Journal article
SN - 0143-6228
VL - 126
JO - Applied Geography
JF - Applied Geography
M1 - 102367
ER -
