  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.
