Global and local spatial indices of urban segregation

Feitosa, Flávia da Fonseca; Câmara, Gilberto; Monteiro, Antônio Miguel Vieira; Koschitzki, Thomas; Silva, M. P. S.

doi:10.1080/13658810600911903

Cited by 116 publications

(135 citation statements)

References 30 publications

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“…Global and local versions of the generalized spatial dissimilarity index (D and d j ) and the spatial isolation index (Q m and q m;j ) were adopted. While global indices summarize the segregation degree of the whole city, the local indices show segregation as a spatially variant phenomenon that can be displayed in maps (Feitosa et al, 2007).…”

Section: Urban-population Modulementioning

confidence: 99%

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Multi-agent simulator for urban segregation (MASUS): A tool to explore alternatives for promoting inclusive cities

Feitosa

Vlek

2011

Computers, Environment and Urban Systems

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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.

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Section: Urban-population Modulementioning

confidence: 99%

“…The segregation state of the population (P seg ) is depicted by spatial indices of segregation as described by Feitosa, Câmara, Monteiro, Koschitzki, and Silva (2007). Global and local versions of the generalized spatial dissimilarity index (D and d j ) and the spatial isolation index (Q m and q m;j ) were adopted.…”

Section: Urban-population Modulementioning

confidence: 99%

Multi-agent simulator for urban segregation (MASUS): A tool to explore alternatives for promoting inclusive cities

Feitosa

Vlek

2011

Computers, Environment and Urban Systems

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“…Researchers in Reference [3] study segregation as spaces of activities, analyzing the indexes of evenness/clustering and exposure/isolation. This context reinforces the idea of urban segregation as the absence of interactions in space or as the exclusion to opportunities of access to activities and services in the cities [1, [17][18][19].…”

Section: Urban Segregationmentioning

confidence: 99%

“…The work in [1] defines segregation as the restriction of interaction involving or not involving the physical space. In architecture and urban planning studies, urban segregation is commonly approached as separation [2,3]. Segregation is the separation of people, activities and functions [4] and it can hardly be approached without considering its spatial aspect.…”

Section: Introductionmentioning

confidence: 99%

Urban Segregation and Socio-Spatial Interactions: A Configurational Approach

Machado

Maraschin

2018

Urban Science

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Abstract:Urban segregation is an inherent feature of cities and becomes a problem when excluding or hindering certain groups from accessing services, activities and spaces. In Brazil, segregation by social class is dominant in the structure of cities and public policies rarely address urban configuration as part of the segregation problem. This work addresses segregation with a shift in emphasis from traditional housing segregation to segregation as the restraint of socio-spatial interactions, thus including other facets of the phenomenon that have not yet been properly explored and seeking new spatially relevant metrics. This paper aims to present a methodology of segregation analysis based on configurational models and develop an empirical application in a Brazilian city. Representing the probabilities of interaction between different socio-economic groups in public spaces, a configurational model was used, addressing retail-residence spatial relationship. The attributes of population size, household income and number of retail establishments were considered. The results allowed identifying the probabilistic residence-retail trajectories for high and low income groups, providing a first measure of spatial segregation. The conclusions seek to highlight the importance of configurational approaches for segregation studies, as well as to show potentialities and limits of this methodology.

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“…Thus, instead of treating each location and its population density as isolated units, these researchers treated population density as a continuous surface. Moreover, Feitosa et al (2007) developed a geographically weighted population index in which the population count of a geographic unit is geographically weighted (instead of the population density), and they used this index to measure urban segregation at different scales and better capture the interaction between groups across geographic boundaries. Certain studies have also used indices for overcrowding.…”

Section: Impact Of Geographic Context In Demographic Analysesmentioning

confidence: 99%

Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914

et al. 2017

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Geocoding longitudinal and individual-level historical demographic databases enables novel analyses of how micro-level geographic factors affected demographic outcomes over long periods. However, such detailed geocoding involves high costs. Additionally, the high spatial resolution cannot be properly utilized if inappropriate methods are used to quantify the geographic factors. We assess how different geocoding levels and methods used to define geographic variables affects the outcome of detailed spatial and historical demographic analyses. Using a longitudinal and individual-level demographic database geocoded at the property unit level, we analyse the effects of population density and proximity to wetlands on all-cause mortality for individuals who lived in five Swedish parishes, 1850-1914. We compare the results from analyses on three detailed geocoding levels using two common quantification methods for each geographic variable. Together with the method selected for quantifying the geographic factors, even small differences in positional accuracy (20-50 m) between the property units and slightly coarser geographic levels heavily affected the results of the demographic analyses. The results also show the importance of accounting for geographic changes over time. Finally, proximity to wetlands and population density affected the mortality of women and children, respectively. However, all possible determinants of mortality were not evaluated in the analyses. In conclusion, for rural historical areas, geocoding to property units is likely necessary for fine-scale analyses at distances within a few hundred metres. We must also carefully consider

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Global and local spatial indices of urban segregation

Cited by 116 publications

References 30 publications

Multi-agent simulator for urban segregation (MASUS): A tool to explore alternatives for promoting inclusive cities

Multi-agent simulator for urban segregation (MASUS): A tool to explore alternatives for promoting inclusive cities

Urban Segregation and Socio-Spatial Interactions: A Configurational Approach

Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914

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