Academic journal article Demographic Research

Spatial Attraction in Migrants' Settlement Patterns in the City of Catania

Academic journal article Demographic Research

Spatial Attraction in Migrants' Settlement Patterns in the City of Catania

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1. Introduction

Residential location influences individuals' proximity to important resources (such as schools, hospitals, child care facilities, labor markets, and employment opportunities) and to potential risks, including environmental threats and social hazards (such as exposure to crime and violence) (Reardon 2006). Furthermore, it impacts access to social networks and other forms of social capital; overall, it shapes human interaction and the demographic processes that originate from it, such as mortality, fertility and mobility (Almquist and Butts 2012).

A minority ethnic group is spatially clustered when the spatial arrangement of minority households departs from expectations based upon a random spatial allocation (Freeman, Pilger, and Alexander 1971).

In broad terms, and apart from ethnic discriminatory rules enforced by law or traditions in some places and at some times, we may distinguish between two sources of spatial clustering. One source is spatial inhomogeneity or apparent contagion. Typically, the different parts of a city exhibit large variations in the price of residential property, in the accessibility of low cost public infrastructures, and in the availability of certain types of jobs; these inhomogeneities may lead to a mostly economically induced segregation. As Schelling (1971) observes, ethnicity is often correlated with income, and income with residence; so even if residential choices were unconstrained by ethnic discrimination, the different ethnic groups would not be randomly distributed among residences.

The second source is spatial attraction or true contagion. Survey data on the ideal neighborhood composition for different ethnic groups in the USA, reported in Clark and Fossett (2008), show that all groups prefer living in areas where their group is a majority or near-majority. These preferences have complex origins and may reflect attachment to group identity and culture (e.g., language, religion, customs, etc.). Newly arrived minority migrants may benefit from positive spillovers in settling close to their compatriots, in terms of reciprocal acceptance, common language, and support. Transnational social networks play an important role in channeling arriving migrants into specific neighborhoods and also into particular occupations (Gelderblom and Adams 2006).

However, regardless of what the basis of the individual preferences for coethnic contact is, they produce identical patterns of residential segregation (Clark and Fossett 2008). The Schelling (1971) model provides an analysis of the implications of individual preferences and shows that when a household enters a neighborhood, that neighborhood becomes more attractive to members of the household's own group and less attractive to members of other groups. In other words, the presence of a household in a given area increases the probability of others of the same group to locating nearby.

It is relevant in social research to be able to distinguish between these two sources of clustering. Whereas economic induced segregation might explain some initial degree of segregation and raises questions of social equity, the Schelling model highlights the importance of individually motivated segregation and posits that even mild preferences for living with similar neighbors carry the potential of being strong determinants for residential segregation (Clark and Fossett 2008). The spatial distribution of households may be represented by a point pattern, i.e.,

a set of points in a map. Ripley's K-function (Ripley 1981) is widely used to detect clustering in point processes. The inhomogeneous K-function is a version of Ripley's K-function conceived for assessing the effects of spatial attraction (or inhibition), while adjusting for the effects of spatial inhomogeneity. In other words, this approach allows us to distinguish between the two sources of clustering, by assessing clustering above and beyond that due to apparent contagion. …

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