Silvestro Montrone

Identification of “Hot Spots” of Social and Housing Difficulty in Urban Areas: Scan Statistics for Housing Market and Urban Planning Policies

The objective of the present work is to use statistical data to identify territorial zones characterized by the presence of urban poverty related to property ownership and the availability of residential services. Poverty clusters have a high concentration of poor people, but that does not mean that everyone living in them is poor. While poverty is widely accepted to be an inherently multi-dimensional concept, it has proved very difficult to develop measures that both capture this multidimensionality and make comparisons over time and space easy. With this in mind, we attempt to apply a Total Fuzzy and Relative (TFR) approach, based on a fuzzy measure of the degree of association of an individual to the totality of the poor and an approach of semantic distance (Munda 1995), based on the definition of a “fuzzy distance” as a discriminating multidimensional reference to rank the availability to property in real estate market, as complement of urban poverty, in the specific case of the City of Bari. These approaches have been improved using the SaTScan methodology, a circle-based spatial-scan statistical method (Kulldorff 1997; Patil and Taille 2004; Aldstat and Getis 2006). It concerns geoinformatic surveillance for poverty hot-spot detection, used as a scientific base to lead urban regeneration policies.

Analysis of positional aspects in the variation of real estate values in an italian southern metropolitan area

The paper show the use of a fuzzy weighting system to identify the correspondence of real estate value with main socio-physical characters of the urban tissue. The descriptor of the relationship with the real estate value is represented by a set of indicators of the urban decay of housing property and the analysis is tested on a real application of a case study. The study gives support to the development of new approach for localizing cadastral values at a more detailed scale, compared to the current scale used in the Italian Cadastre. The utilized statistical approach has been based on the SaTScan application, as a techniques of fuzzy clustering, and on a test of stability based on the comparison of a “fuzzy semantic distance” among the average real estate values of urban quarters, with the expected crisp distance among the same quarters.

A Fuzzy Approach to the Small Area Estimation of Poverty in Italy

Urban poverty, especially in metropolitan areas, represents one of the most significant problems to both developed and developing countries. The aim of the present work is to identify territorial zones characterized by the presence of such a phenomenon. In particular, data gathered from the EU-SILC study for 2006 has been examined and elaborated in order to obtain estimates of poverty at a provincial level through the use of statistical methods such as Small Area Estimation and Total Fuzzy and Relative. The results obtained from this approach have been improved using SaTScan methodology for the graphical identification of homogeneous areas of poverty.

Statistical Methods for Spatial Planning and Monitoring

The book aims to investigate methods and techniques for spatial statistical analysis suitable to model spatial information in support of decision systems. Over the last few years there has been a considerable interest in these tools and in the role they can play in spatial planning and environmental modelling. One of the earliest and most famous definition of spatial planning was a geographical expression to the economic, social, cultural and ecological policies of society: borrowing from this point of view, this text shows how an interdisciplinary approach is an effective way to an harmonious integration of national policies with regional and local analysis. A wide range of spatial models and techniques is, also, covered: spatial data mining, point processes analysis, nearest neighbor statistics and cluster detection, Fuzzy Regression model and local indicators of spatial association; all of these tools provide the policy-maker with a valuable support to policy development.

A Model-Based Scan Statistics for Detecting Geographical Clustering of Disease

The classical likelihood ratio spatial scan statistics has been widely used in spatial epidemiology for disease cluster detection. The question is whether the geographic incidence pattern is due to random fluctuations or the map reflects true underlying geographical variation due to etiologic risk factors. The hypothesis underlying the classic scan statistics assume that disease counts in different locations have independent Poisson distribution; unfortunately, outcomes in spatial units are often not independent of each other. Risk estimates of areas that are close to each other will tend to be positively correlated as they share a number of spatially varying characteristics. Ignoring the overdispersion caused by spatial autocorrelation leads to incorrect results. To overcome this difficulty, we propose a model-based approach adjusting for area-specific fixed-effects measuring potential effect modifiers, and for large-scale geographical variation of etiologic factors that vary continuously in space and are not expressly present within the model. We apply our methodology to the spatial distribution of lung cancer male mortality occurred in the province of Lecce, Italy, during the period 1992-2001.

The Lifestyles of Families through Fuzzy C-Means Clustering

The objective of this report is the analysis of the data arising from the Family Lifestyles survey conducted by the University of Bari “A. Moro” (2012-2013) through the construction of indicators of socio-economic hardship and the identification of family profiles during the current period of crisis. The approach used in this work in order to synthesize and measure the conditions of hardship of a population is based on the so-called “Totally Fuzzy and Relative” method employing a Fuzzy Sets technique in order to obtain a measure of relative incidence in a population from the statistical information provided by a plurality of indicators [1]. The subsequent step involved considering a clustering procedure (Fuzzy c-means) with the objective of outlining various profiles, not defined a priori, to be assigned to each family with different socio-economic behaviours [2]. This clustering method allows, compared to conventional methods, a set of data to belong not only to a main cluster but also to two or more clusters with “fuzzy” profiles.

Internal Areas Strategies: From Statistical Methods to Planning Policies

The “National Strategy for Internal Areas”, made by the Italian Government for the European Union Partnership Agreement 2014-2020, defines the territory of the Italian internal areas as a set of project-areas, local inter-municipal systems each with its own territorial identity defined by social, economic, geographic, demographic and environmental characteristics. In this sense, we can define “internal” those areas significantly distant from the centers of supply of essential services (education, health, and mobility), rich in environmental and cultural resources with highly diversified natural aspects. The objective of the work is to re-elaborate the existing mapping for the identification of the internal areas, made by the Italian Government, especially taking into account the demographic, economic, morphological profiles and essential services supply, through the use of fuzzy logic. Then, trying to deep explain possible planning strategies and policies for these relevant, sometimes abandoned and extremely diffuse territories.

The Statistical Matching: An Integrated Archive of the Lifestyles of Italian Families

The objective of the techniques of integration for data from a number of different sources is to identify records relating to similar or identical units, to estimate the unified distribution of a number of variables observed in different data archives and to merge informative records. The present work will describe a model of data integration through a methodology of Statistical Matching (hot deck distance) for the integration of two surveys (Eu-Silc Istat and Lifestyle Survey University of Bari). The construction of an integrated database on the basis of these two surveys may be useful for the study of consumer behavior in relation to specific groups of commodities, in order to analyse the decisions taken by families with regard to saving, to examine economic and social inequality, and to study the impact of public policies by means of simulations. The coexistence of multiple and differentiated objectives triggers the need to obtain a very general and versatile integrated file, which provides ongoing detailed information on the different types of spending, on levels of saving, on the distribution of incomes, on the occupational conditions of the members of the family unit, etc.

Economic Evaluation and Statistical Methods for Detecting Hot Spots of Social and Housing Difficulties in Urban Policies

The paper shows a reasoning about how the new national policies on social housing can be implemented in connection with regional and local economic analyses. In this light the role of evaluation is examined, to show which kind of approach can be referred to each dimension, context and level. After a general introduction explaining the main aspects of the National Housing Plan, an example of integrate assessment of rent market and difficulty in housing access is shown, obtained by a scaling that profile some Italian metropolitan reality. All our examples are aiming to demonstrate that only an integrate, multilevel approach, supported by appropriate analysis can succeed in improving supply and quality of housing stock. After a general economic analysis the paper speaks about use statistical data to identify territorial zones (by the use of hot spots) characterized by the presence of urban poverty related to property ownership and the availability of residential services.

From the District Model to an Analysis by Specializations, Concentrations and Productive Clusters: The Localization of Economic Activities in Puglia

The application of multivariate techniques is widely used if you want to study phenomena or processes characterized by numerous variables that work simultaneously in time and space. The present contribution - with a view to using spatial statistical tools - intends to focus on the economic and production identities and vocations of Puglia, attempting to highlight inter-institutional relations and relationships. Thus, on the basis of the analysis of territorial and economic indicators, it has been proposed to hypothesize clusters as potential reference points and adequate functional tools for the planning and adoption of effective and appropriate regional policies of intervention.