This paper reports about an application of autocorrelation methods in order to produce more detailed analyses for urban regeneration policies and programs. Generally, a municipality proposes an area as suitable for a urban regeneration program considering the edge of neighbourhoods, but it is possible that only a part of a neighbourbood is interested by social degradation phenomena. Furthermore, it is possible that the more deteriorated area belongs to two different adjacent neighbourboods. Compared to classical statistical analyses, autocorrelation techniques allow to discover where the concentration of several negative social indicators is located. These methods can determine areas with a high priority of intervention in a more detailed way, thus increasing efficiency and effectiveness of investments in urban regeneration programs. In order to verify the possibility to apply these techniques Bari municipality has been chosen for this research since it shows very different social contexts.

Where are the slum? New approaches to urban regeneration

Danese Maria
2008

Abstract

This paper reports about an application of autocorrelation methods in order to produce more detailed analyses for urban regeneration policies and programs. Generally, a municipality proposes an area as suitable for a urban regeneration program considering the edge of neighbourhoods, but it is possible that only a part of a neighbourbood is interested by social degradation phenomena. Furthermore, it is possible that the more deteriorated area belongs to two different adjacent neighbourboods. Compared to classical statistical analyses, autocorrelation techniques allow to discover where the concentration of several negative social indicators is located. These methods can determine areas with a high priority of intervention in a more detailed way, thus increasing efficiency and effectiveness of investments in urban regeneration programs. In order to verify the possibility to apply these techniques Bari municipality has been chosen for this research since it shows very different social contexts.
2008
SOCIAL COMPUTING, BEHAVIORAL MODELING AND PREDICTION
176
186
11
978-0-387-77671-2
1
none
Murgante, Beniamino; Casas, Giuseppe Las; Danese, Maria
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/269642
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