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Mapping the invisibles: Using non-conventional point-level data to analyse residential patterns of deprived people in a mid-sized city

In urban geography it is a common practice to refer to censuses and other official sources of data to analyse residential segregation. However, there are limitations to those data, especially where particular groups of people are concerned. Often, official sources of data do not allow micro-level analysis of the characteristics, needs and residential patterns of socially disadvantaged residents. At the same time, measures based on income thresholds fail to fully take into account the complexity of multidimensional deprivation. This work uses the unconventional information coming from a voluntary organisation to investigate and understand the residential patterns of disadvantaged residents living in a mid-sized Italian city. The factors associated with the relative presence of deprived residents in city neighbourhoods are tested with a GLM/Poisson regression model. The results are differentiated among the sub-groups: the disadvantaged people pertaining to the Asian community are more residentially clustered than others. Their distribution, unlike that of other ethnic groups, is not significantly related to the economic characteristics at area level but to the presence of other Asian residents in an area.