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Social vulnerability index (SoVI) has been widely used to measure the extent to which people or places are socially vulnerable. The SoVI is an aggregate composite index that linearly combines a few principal components resulted from the principal components analysis on a number of selected social vulnerability indicator variables, and it can quantify the relative level of overall social vulnerability but cannot inform the specific local social indicators that contribute to the vulnerability in various degrees. The specific social indicators that either attenuate or amplify local social vulnerability are of much need in policy making to reduce social vulnerability. This study explores the differential contributions of the constituent components of SoVI and investigates how the local indicator variables have evolved over time and across the Greater Houston metropolitan area in the USA using the geographically weighted principal components analysis. It found that the overall social vulnerability as measured by SoVI has exhibited persistent spatial patterns in the Greater Houston area since 1970; however, the spatial patterns of the SoVI are not equally constituted by the components of the SoVI. In particular, the high social vulnerability of suburban areas is mainly the result of one principal component that highly correlates with the percentage of mobile homes. It also found that the indicator variables of social vulnerability have exhibited great spatial heterogeneity and dependence at local scale, and they vary over time but persist on disadvantages in economic condition, mobility, and family structure. [ABSTRACT FROM AUTHOR]

Hurricanes pose a continuing hazard to populations in coastal regions. This study estimates the impact of hurricanes on population change in the years 1970-2005 in the U.S. Gulf Coast region. Geophysical models are used to construct a unique data set that simulates the spatial extent and intensity of wind damage and storm surge from the 32 hurricanes that struck the region in this period. Multivariate spatial time-series models are used to estimate the impacts of hurricanes on population change. Population growth is found to be reduced significantly for up to three successive years after counties experience wind damage, particularly at higher levels of damage. Storm surge is associated with reduced population growth in the year after the hurricane. Model extensions show that change in the white and young adult population is more immediately and strongly affected than is change for blacks and elderly residents. Negative effects on population are stronger in counties with lower poverty rates. The differentiated impact of hurricanes on different population groups is interpreted as segmented withdrawal-a form of segmented resilience in which advantaged population groups are more likely to move out of or avoid moving into harm's way while socially vulnerable groups have fewer choices. [ABSTRACT FROM AUTHOR]

Studies on small-world networks have received intensive interdisciplinary attention during the past several years. It is well-known among researchers that a small-world network is often characterized by high connectivity and clustering, but so far there exist few effective approaches to evaluate small-world properties, especially for spatial networks. This paper proposes a method to examine the small-world properties of spatial networks from the perspective of network autocorrelation. Two network autocorrelation statistics, Moran’s I and Getis–Ord’s G, are used to monitor the structural properties of networks in a process of “rewiring” networks from a regular to a random network. We discovered that Moran’s I and Getis–Ord’s G tend to converge and have relatively low values when properties of small-world networks emerge. Three transportation networks at the national, metropolitan, and intra-city levels are analyzed using this approach. It is found that spatial networks at these three scales possess small-world properties when the correlation lag distances reach certain thresholds, implying that the manifestation of small-world phenomena result from the interplay between the network structure and the dynamics taking place on the network. [ABSTRACT FROM AUTHOR]