Local Spatial and Temporal Factors Influencing Population and Societal Vulnerability to Natural Disasters

Abstract

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county-level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county-level social vulnerability. The results demonstrate that the dynamic characteristics of China's county-level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering-scattering-gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.

Integrated risk assessment of multi-hazards in China

“…The concept of vulnerability is used by various disciplines, and current approaches in vulnerability research are driven by a divide between social scientists and natural scientist (Fuchs et al 2012). Social scientists view vulnerability as representing the set of socioeconomic factors that determine people's ability to cope with or changes (Wisner et al 2004;Birkmann 2006;IPCC 2012) or call it social vulnerability (Cutter and Finch 2008;Zhou et al 2014a), whereas natural scientists focus on vulnerability in terms of the likelihood of occurrence of a specific event and associated impacts on the built environment, also known as physical vulnerability (Papathoma-Kohle et al 2011). Birkmann et al (2013) concluded the dimensions of vulnerability from social, economic, physical, cultural, environmental and institutional aspects.…”

“…The natural hazards in China are characterized with a wide distribution, occurring at a high frequency and often causing huge losses. The natural hazards in China have had a farreaching impact on the sustainable development of China's economy and society, thus being a particular domestic and international concern (Zhou et al 2014a). On average, China's natural hazards from 1990 to 2011 affected about 400 million people per year over, claimed the lives of more than 8,000 victims and caused direct economic losses exceeding 100 billion RMB .…”

A New Framework for Understanding Urban Social Vulnerability from a Network Perspective

“…In essence, social vulnerability is a by-product of social stratification and social inequalities among different communities and different places from the built environment [21][22][23]. It has roots in the various characteristics of people including socio-economic status, demographics, and risk perception [24].…”

Spatial statistical modelling of urban particulate matter

“…"Social vulnerability is the exposure of groups or individuals to stress as a result of social and environmental change" (Zhou et al, 2014). Our interest is differential risk related to ambient levels of particulate matter and heterogeneity in social vulnerability of the exposed population.…”

“…One of the reasons for declaring priority air-sheds is for implementing remedial actions. Comparing kriged Differences in susceptibility to air pollution hazards were captured by using official data on population characteristics Zhou et al, 2014). Previously, population counts were identified as a good proxy for pollutant emissions and the type of area, whether rural or urban for mapping of ambient pollutant concentration (Paciorek and Liu, 2009;Kloog et al, 2011;Finazzi et al, 2013).…”