Street centrality vs. commerce and service locations in cities: a Kernel Density Correlation case study in Bologna, Italy

TL;DR

This study develops a GIS-based kernel density correlation method to analyze how street centrality, especially betweenness, influences the distribution of commercial and service activities in Bologna, Italy.

Contribution

It introduces a novel Kernel Density Correlation approach to quantify the relationship between street centrality and urban activities, enhancing urban planning tools.

Findings

Strong correlation between street betweenness centrality and activity locations.

Method supports predictive urban planning for sustainable city development.

Enhances GIS tools for spatial analysis of urban centrality and activities.

Abstract

In previous research we defined a methodology for mapping centrality in urban networks. Such methodology, named Multiple Centrality Assessment (MCA), makes it possible to ascertain how each street is structurally central in a city according to several different notions of centrality, as well as different scales of "being central". In this study we investigate the case of Bologna, northern Italy, about how much higher street centrality statistically "determines" a higher presence of activities (shops and services). Our work develops a methodology, based on a kernel density evaluation, that enhances standard tools available in Geographic Information System (GIS) environment in order to support: 1) the study of how centrality and activities are distributed; 2) linear and non-linear statistical correlation analysis between centrality and activities, hereby named Kernel Density Correlation (KDC). Results offer evidence-based foundations that a strong correlation exists between centrality of streets, especially betweenness centrality, and the location of shops and services at the neighbourhood scale. This issue is at the heart of the current debate in urban planning and design towards the making of more sustainable urban communities for the future. Our results also support the "predictive" capability of the MCA model as a tool for sustainable urban design.