Assessing urban vulnerability to natural hazards such as earthquakes can be regarded as an ill-structured problem (i.e. a problem for which there is no unique, identifiable, objectively optimal solution). A review of the literature indicates a number of contrasting definitions of what vulnerability means, as well as numerous conflicting perspectives on what should or should not be included within the broad assessment of vulnerability in cities. This paper reports on the findings from a project in which a GIS methodology has been developed to assess urban vulnerability through a spatial analytical procedure. First, we highlight the deficiencies of current GIS approaches to urban vulnerability analysis and discuss the ill-structured nature of the vulnerability problem. We then propose a working definition for vulnerability assessment in which vulnerability is thought of as a spatial decision problem under the conditions of uncertainty. Next, we present a methodology to incorporate this definition into a GIS framework that combines elements from the techniques of spatial multicriteria analysis and fuzzy logic. The application of this methodology is then illustrated with a case study from Los Angeles County. The results suggest that the proposed methodology may provide a new approach for analyzing vulnerability that can add to our understanding of human/hazards interaction.

This paper examines the feasibility of spectral mixture analysis (SMA) in deriving comparable physical measures of urban land cover that describe the morphological characteristics of cities. SMA offers a way of analyzing satellite imagery of urban areas that may be superior to more standard methods of classification. Mixing models are based on the assumption that the remotely measured spectrum of a given pixel can be modeled as a combination of pure spectra, called endmembers. SMA, using four image endmembers (vegetation, impervious surface, soil, and shade), was applied to an IRS‐1C multispectral image in order to extract measures that describe the anatomy of the Greater Cairo region, Egypt, in terms of endmember fractions. The resulting fractions were then used to classify the urban scene into eight classes of natural and human‐built features through a decision tree (DT) classifier. The accuracy of the DT classification was compared to the accuracies of two per‐pixel supervised classifications of the IRS‐1C image employing maximum likelihood (ML) and minimum distance‐to‐means (MDM) classifiers. Overall KAPPA accuracies were 0.88 for the DT classification based on SMA fractions, and 0.60 and 0.45 for the classifications conducted through ML and MDM respectively.

This paper reports on preliminary results from a study applying the technique of spectral mixture analysis (SMA) to the measurement of temporal changes in the composition of urban morphology in the metropolitan area of Greater Cairo, Egypt, between 1987 and 1998. Although several remote sensing techniques have been used successfully for urban change analysis, most of these focus on change ‘between’ classes measured in a discrete, crisp way through which each pixel is assigned to a label indicating either a change or no change in the class to which the pixel originally belonged. In many major cities, such as Cairo, change also occurs within classes (e.g. vertical growth of buildings, increase in housing density, decrease in open spaces) and is reflected by an aggregation of land cover and urban materials. None of these materials may seem important in isolation. Rather, the significance of these urban land covers arises from the way they interweave with each other to structure the morphology of the urban place. In this paper, a ‘soft’ approach is presented to identify and measure the composition of changing morphology from multi‐temporal, multi‐spectral satellite images. SMA is demonstrated to be capable of deriving spatially continuous variables quantified at the sub‐pixel level. These variables represent measures that can be compared across urban places and at different time periods. They can be integrated readily into a wide range of applications and models concerned with physical, economic and/or socio‐demographic phenomena in the city.