Spatial dependence and heterogeneity in patterns of hardship:: An intra-urban analysis

Developments in the provision and quality of digital data are creating possibilities for spatial and temporal measurement of the properties of socioeconomic systems at finer levels of granularity. In this article, we suggest that the "lifestyles" datasets collected by private sector organizations in the U.K. and the U.S. provide one such prospect for better inferring the structure, composition, and heterogeneity of urban areas. Using a case study of Bristol, U.K., we compare the patterns of spatial dependence and spatial heterogeneity observed for a small-area ("lifestyles") income measure with those of the census indicators that are commonly used as surrogates for it. This leads first to an exploration of spatial effects using geographically weighted regression (GWR) and then to a specification of spatial dependence using a spatially autoregressive model. This analysis extends our understanding of the determinants of hardship and poverty in urban areas; urban policy has hitherto used aggregate, outdated, or proxy measures of income in an insufficiently critical manner, and techniques for measuring spatial dependence and heterogeneity have usually been applied at the regional, rather than intra-urban, scales. The consequence is a limited understanding of the geography and dynamics of income variations within urban areas. The advantages and limitations of the data used here are explored in the light of the results of our statistical analysis, and we discuss our results as part of a research agenda for exploring dependence and heterogeneity in research focusing on the intra-urban geography of deprivation.