A neural network investigation of the crucial facets of urban sustainability.
This paper focuses on the concept of a sustainable city and its theoretical implications for the Italian urban system. Urban sustainability is based on positive interactions among three different urban subsystems: social, economic and physical, where social well-being coexists with economic development and environmental quality. This utopian scenario does not appear in the existing cities. The aesthetic quality of natural and man-made environment is often associated with marginality and poverty, labor market variety and urban efficiency coexisting with pollution, criminality and high settlement costs. Moreover, since each city differs institutionally, historically, culturally and economically, few attempts have been implemented to build a comparative synthetic vision of the urban sustainability in different cities. The interactions among these selected systems are complex and unpredictable and present the opportunity for a new methodology of scientific investigation: the connectionistic approach. The dual aim of this study is to: investigate the underlying relationships among the three subsystems with a set of social, economic and physical attributes of the chief towns of a Province in Italy and ; verify if this underlying structure could reproduce the heterogeneity of urban realities, allowing one to distinguish groups of cities with different assets or drawbacks in their sustainability. The Data Base (DB), composed of 43 attributes for 95 cities, was processed by Self-Reflexive Neural Networks (SRNN) (Buscema, 1995). These Networks are a useful instrument of investigation and analogic questioning of the Data Base. Once the SRNN has learned the structure of the weights from the DB, by querying the network with the maximization or minimization of specific groups of attributes, it is possible to read the related properties and to rank the cities' urban profile.
Buscema
M
M
Semeion Research Center, Viale di Val Fiorita, Rome, Italy.
Diappi
L
L
Ottanà
M
M
D002947:Cities / Q000191:economics; D019992:Databases as Topic; D004467:Economics; D004777:Environment; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012959:Socioeconomic Factors; D014507:Urbanization