Urban segregation has received increasing attention in the literature due to the negative impacts that it has on urban populations. Indices of urban segregation are useful instruments for understanding the problem as well as for setting up public policies. The usefulness of spatial segregation indices depends on their ability to account for the spatial arrangement of population and to show how segregation varies across the city. This paper proposes global spatial indices of segregation that capture interaction among population groups at different scales. We also decompose the global indices to obtain local spatial indices of segregation, which enable visualization and exploration of segregation patterns. We propose the use of statistical tests to determine the significance of the indices. The proposed indices are illustrated using an artificial dataset and a case study of socio-economic segregation in Sã o José dos Campos (SP, Brazil).

a b s t r a c tUrban segregation represents a significant barrier to achieving social inclusion in cities. To mitigate this problem, it is necessary to implement policies founded upon a better understanding of segregation dynamics. This paper proposes MASUS, a multi-agent simulator for urban segregation, which provides a virtual laboratory for exploring the impacts of different contextual mechanisms on the emergence of segregation patterns. We illustrate the potential of MASUS through three experiments on segregation in São José dos Campos, a medium-sized city in southeast Brazil. The first experiment compares simulated outputs with empirical data, the second exemplifies the ability of MASUS to test theories, and the third tests an anti-segregation policy. We also discuss limitations of the current version of the model, and we recommend directions for further research.

Residential segregation is known as one of the most prevalent problems of Latin American and Brazilian cities. This chapter looks into the changes in segregation levels in the Metropolitan Region of São Paulo between 2000 and 2010. This period was marked by economic growth and decreasing social inequalities in Brazil with consequent improvement to the quality of life of lower income classes. Despite those improvements, general patterns of urban segregation in Brazilian cities showed remarkable stability, albeit with important changes in the details of segregation patterns. This chapter explores the spatial relationship between socio-occupational groups using global and local segregation indices. The analysis confirmed a highly segregated distribution of social groups in the Metropolitan Region and revealed increased levels of segregation, with global indices figures for 2010 higher than for 2000. Analysis demonstrated that peripheral areas of the Metropolitan Region became more fragmented and heterogeneous in that period, and revealed that their increased heterogeneity is mainly composed of classes with close social proximity, rather than polarised ones. Results showed that while middle classes became more integrated amongst themselves and with lower classes, the separation between lower and upper classes was not only maintained but also increased during the period. All these findings suggest a reconfiguration of the concentric pattern of segregation that maintained a spatial structure of strong social isolation during the period, although with greater complexity.

There has been extensive use of segregation indices for measuring residential segregation since 1950s, with continuous progress made in the field. Recent developments include the propositions of spatial global and local versions of traditionally used segregation indices, which have opened avenues for representing and analysing segregation as a multiscale and spatially varying phenomenon. Much less explored has been the issue of how important research design choices, such as the extent of geographical boundaries, grouping systems and scales of analysis, can influence the measurement of segregation. This paper contributes in this direction by investigating the impact of such decisions in the outcomes of the indices of generalized dissimilarity (D) and information theory (H) using a set of sensitivity analysis. Using a comparative study between London and São Paulo as basis, results obtained with different geographical boundaries, grouping systems and scales for the two indices are analysed visually and quantitatively. Results suggest that although D and H depict the same spatial dimension of segregation (unevenness/clustering), they present different sensitivity to geographical boundaries and grouping systems. The study also revealed how the two indices unfold different aspects of the segregation, which impact on their interpretation and applicability. The study concludes with a discussion of the considerations on research design choices concerning the interpretation of the results, which indicate the two indices should not be used interchangeably.