From Urban Segregation to Spatial Pattern Detection

TL;DR

This paper introduces a multifocal approach to analyze spatial dissimilarities in cities by examining variable sequences at multiple scales, providing a mathematical framework to detect underlying spatial structures.

Contribution

It presents a novel multifocal method and mathematical framework for revealing and analyzing spatial dissimilarities across different city scales.

Findings

The approach captures spatial dissimilarities effectively.

Sequences encode signatures of spatial structures.

Framework applicable to various urban variables.

Abstract

We develop a "multifocal" approach to reveal spatial dissimilarities in cities, from the most local scale to the metropolitan one. Think for instance of a statistical variable that may be measured at different scales, eg ethnic group proportions, social housing rate, income distribution, or public transportation network density. Then, to any point in the city there corresponds a sequence of values for the variable, as one zooms out around the starting point, all the way up to the whole city -- as if with a varifocal camera lens. The sequences thus produced encode in a precise manner spatial dissimilarities: how much they differ from perfectly random sequences is indeed a signature of the underlying spatial structure. We introduce here a mathematical framework that allows to analyze this signature and we provide a number of illustrative examples.