Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data

Risk management in urban planning is of increasing importance to mitigate the growing amount of damage and the increasing number of casualties caused by natural disasters. Risk assessment to support management requires knowledge about present and future hazards, elements at risk and different types of vulnerability. This article deals with the assessment of social vulnerability (SV). In the past this has frequently been neglected due to lack of data and assessment difficulties. Existing approaches for SV assessment, primarily based on community-based methods or on census data, have limited efficiency and transferability. In this article a new method based on contextual analysis of image and GIS data is presented. An approach based on proxy variables that were derived from high-resolution optical and laser scanning data was applied, in combination with elevation information and existing hazard data. Object-oriented image analysis was applied for the definition and estimation of those variables, focusing on SV indicators with physical characteristics. A reference Social Vulnerability Index (SVI) was created from census data available for the study area on a neighbourhood level and tested for parts of Tegucigalpa, Honduras. For the evaluation of the proxy-variables, a stepwise regression model to select the best explanatory variables for changes in the SVI was applied. Eight out of 47 variables explained almost 60% of the variance, whereby the slope position and the proportion of built-up area in a neighbourhood were found to be the most valuable proxies. This work shows that contextual segmentation-based analysis of geospatial data can substantially aid in SV assessment and, when combined with field-based information, leads to optimization in terms of assessment frequency and cost.