Revealing Multiscale Segregation Effects from Fine-Scale Data: A Case Study of Two Communities in Paris

Olteanu, Mădălina; Bezenac, Cecile de; Clark, William A. V.; Randon‐Furling, Julien

doi:10.1007/s40980-020-00065-4

Cited by 6 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent novel development is the creation of a dataset on the population with migrant backgrounds in EU member states, for 100 m grid cells (Alessandrini et al 2017). These data have been used to compare the residential segregation of migrant populations in multiple urban areas across Europe (Benassi et al 2020), and to explore multi-scale segregation in Paris (Olteanu et al 2020). Other studies using gridded data to explore ethnicity include Wong et al (1999), who allocate standard zones to grids based on the zone centroids in an analysis of segregation in US cities.…”

Section: Population Gridsmentioning

confidence: 99%

Population Grids for Analysing Long-Term Change in Ethnic Diversity and Segregation

Catney

Lloyd

2020

Spat Demogr

View full text Add to dashboard Cite

Changes in the spatial patterns of ethnic diversity and residential segregation are often highly localized, but inconsistencies in geographical data units across different time points limit their exploration. In this paper, we argue that, while they are often over-looked, population grids provide an effective means for the study of long-term fine-scale changes. Gridded data represent population structures: there are gaps where there are no people, and they are not (unlike standard zones) based on population distributions at any one time point. This paper uses an innovative resource, PopChange, which provides spatially fine-grained (1 km by 1 km) gridded data on country of birth (1971–2011) and ethnic group (1991–2011). These data enable insight into micro-level change across a long time period. Exploring forty years of change over five time points, measures of residential ethnic diversity and segregation are employed here to create a comprehensive ‘atlas’ of ethnic neighbourhood change across the whole of Britain. Four key messages are offered: (1) as Britain’s ethnic diversity has grown, the spatial complexity of this diversity has also increased, with greater diversity in previously less diverse spaces; (2) ethnic residential segregation has steadily declined at this micro-scale; (3) as neighbourhoods have become more diverse, they have become more spatially integrated; (4) across the whole study period, the most dynamic period of change was between 2001 and 2011. While concentrating on Britain as a case study, the paper explores the potential offered by gridded data, and the methods proposed to analyse them, for future allied studies within and outside this study area.

show abstract

Section: Population Gridsmentioning

confidence: 99%

Population Grids for Analysing Long-Term Change in Ethnic Diversity and Segregation

Catney

Lloyd

2020

Spat Demogr

View full text Add to dashboard Cite

show abstract

“…The D4I dataset provides researchers worldwide with population distribution grids consisting of 100 m grid cells for cities in eight EU Member States 3 . Many studies have utilised the D4I dataset to gain comparable insights into migrant settlement patterns across various urban contexts (Benassi et al 2023a(Benassi et al , 2020a(Benassi et al , 2020bMarci ńczak et al 2021;Olteanu et al 2020).…”

Section: Homogenising Geographiesmentioning

confidence: 99%

Framing the Residential Patterns of Asian Communities in Three Italian Cities: Evidence from Milan, Rome, and Naples

Bitonti,

Benassi,

Mazza

et al. 2023

Social Sciences

View full text Add to dashboard Cite

Today, the interplay between economic inequality, international migration, and urban transformation has raised awareness about segregation and its social implications on a global and European scale. As cities become home to diverse populations with various backgrounds including social, racial, ethnic, and cultural, the proximity of these groups becomes more pronounced. This article explores the residential segregation of four Asian immigrant groups in three major Italian cities: Milan, Rome, and Naples. Using data from the 2011 Italian General Population Census and employing an areal weighted interpolation procedure, the study measures segregation using both traditional two-group indices and multi-group indices that account for the complexities of contemporary societies. The results indicate a north–south disparity, with Naples exhibiting the highest levels of residential segregation. Among the analysed immigrant groups, Bangladeshis and Chinese tended to be more self-segregated, while Filipinos and Sri Lankans were relatively more dispersed. This research underscores the necessity for a nuanced understanding of segregation dynamics and the adoption of appropriate approaches to address the challenges and opportunities presented by the coexistence of diverse groups in urban areas. By contributing to the existing literature on residential segregation in Southern Europe, this study sheds light on the spatial patterns and social dynamics of different ethnic groups in Italian cities.

show abstract

“…Other data-driven modeling approaches of urban populations and territories are clearly possible, even if they do not directly rely on a network-based paradigm. Through suitable mathematical frameworks, a comparative analysis of local and global properties of the population may reveal multi-scale patterns of cohesion/segregation, highlighting the role that different socio-demographic covariates, such as income or ethnicity, play in the definition of urban communities [70,71]. Further, stochastic differential equations may be used to formally account for uncertainty in the description of urban flows, thus making it possible to estimate the parameters of a spatial interaction model from the urban structure alone, characterizing the impact of geographical distance upon individual cost/utility-based choices [72].…”

Section: Social and Contact Network Modelsmentioning

confidence: 99%

Inferring Urban Social Networks from Publicly Available Data

et al. 2021

View full text Add to dashboard Cite

The definition of suitable generative models for synthetic yet realistic social networks is a widely studied problem in the literature. By not being tied to any real data, random graph models cannot capture all the subtleties of real networks and are inadequate for many practical contexts—including areas of research, such as computational epidemiology, which are recently high on the agenda. At the same time, the so-called contact networks describe interactions, rather than relationships, and are strongly dependent on the application and on the size and quality of the sample data used to infer them. To fill the gap between these two approaches, we present a data-driven model for urban social networks, implemented and released as open source software. By using just widely available aggregated demographic and social-mixing data, we are able to create, for a territory of interest, an age-stratified and geo-referenced synthetic population whose individuals are connected by “strong ties” of two types: intra-household (e.g., kinship) or friendship. While household links are entirely data-driven, we propose a parametric probabilistic model for friendship, based on the assumption that distances and age differences play a role, and that not all individuals are equally sociable. The demographic and geographic factors governing the structure of the obtained network, under different configurations, are thoroughly studied through extensive simulations focused on three Italian cities of different size.

show abstract

Revealing Multiscale Segregation Effects from Fine-Scale Data: A Case Study of Two Communities in Paris

Cited by 6 publications

References 24 publications

Population Grids for Analysing Long-Term Change in Ethnic Diversity and Segregation

Population Grids for Analysing Long-Term Change in Ethnic Diversity and Segregation

Framing the Residential Patterns of Asian Communities in Three Italian Cities: Evidence from Milan, Rome, and Naples

Inferring Urban Social Networks from Publicly Available Data

Contact Info

Product

Resources

About