Cities are exploding, occupying rural territory in dispersed and fragmented ways. A consequence of this phenomenon is that the demand for utilities includes more and more extensive territories. Among them, fulfilling the demand for services related to integrated water service presents many difficulties. The economic costs needed to meet service demand and the environmental costs associated with its non-fulfilment are inversely proportional to the population needing service in rural areas, since that population is distributed across a low-density gradient. Infrastructure planning, within the area of competence, generally follows a policy of economic sustainability, fixing a service coverage threshold in terms of a "sufficient" concentration of population and economic activity (91/271/CEE). This threshold, homogenous within the territorial limits of a water infrastructure plan, creates uncertainty in the planning of investments, which are not sized on the actual, appropriately spatialized, demand for service. Careful prediction of the location of infrastructure investments would guarantee not only economic savings but also reduce the environmental costs generated by the lack of utilities. Therefore, is necessary to create a link between water infrastructure planning and urban planning, which is responsible for the future spatial distribution of service demand. In this study, the relationships between the instruments of regulation and planning are compared by a multi-criteria spatial analysis network (analytic network process (ANP)). This method, tested on a sample of a city in southern Italy, allows us to optimize the design and location of the investment needed to meet the service criteria, looking at the actual efficiency of the networks. The result of this application is a suitability map that allows us to validate the criteria for defining urban transformations.
To reduce environmental noise pollution and to safeguard people’s well-being, it is urgently necessary to move towards sustainable urban development and reconcile demographic and economic growth with the protection and restoration of the environment and the improvement of the quality of human lives. This challenge should be a concern to policymakers, who must issue regulations and define the appropriate actions for noise monitoring and management, and citizens, who must be sensitive to the problem and act accordingly. Starting from an analysis of several crowdsourcing noise data collection tools, this paper focuses on the definition of a methodology for data analysis and mapping. The sound sensing system, indeed, enables mobile devices, such as smartphones and tablets, to become a low-cost data collection for monitoring environmental noise. For this study, the “NoiseCapture” application developed in France by CNRS and IFSTTAR has been utilized. The measurements acquired in 2018 and 2019 at the Fisciano Campus at the University of Salerno were integrated with the kernel density estimation. This is a spatial analysis technique that allows for the elaboration of sound level density maps, defined spatially and temporally. These maps, overlaid on a campus facilities map, can become tools to support the appropriate mitigation actions.
The demand for water is constantly increasing, while there are factors related to climate change and pollution that make it less and less available. Addressing this problem means being able to face it with a global approach, which takes into account that human beings need water to survive, as well as all the systems on which they rely, namely sanitation, health, education, business, and industry. While human behavior is influenced by the growing awareness on this topic promoted by organizations specifically targeting this mission, the need to protect water resources in operational terms has led mainly to the need for smart urban infrastructure planning, consistent with the objective of promoting sustainable development. To this aim, the authorities in charge of monitoring the implementation of the investment plans by operators need to perform accurate evaluations of the technical quality of the services provided. The present paper introduces a framework to design a Multi-criteria Spatial Decision Support System, conceived to help decision-makers define and analyze the investment priorities of the individual service operators. By building a knowledge model of the network under investigation, decision-makers are aware of physical components of the whole system and are provided with an intervention priority index related to the network objects that could be affected by the planning action to be implemented.
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