Cluster analysis is highly advantageous as it provides “relatively distinct” (or heterogeneous) clusters, each consisting of units (families) with a high degree of “natural association”. Different approaches to cluster analysis are characterized by the need to define a matrix of dissimilarity or distance between the n pairs of observations. The cluster analysis allows to identify the profiles families who meet certain descriptive characteristics, not defined a priori. Fuzzy clustering is useful to mine complex and multi-dimensional data sets, where the members have partial or fuzzy relations. Among the various developed techniques, fuzzy-C-means (FCM) algorithm is the most popular one, where a piece of data has partial membership with each of the pre-defined cluster centers. Fu and Medico [1] developed a clustering algorithm to capture dataset-specific structures at the beginning of DNA microarray analysis process, which is known as Fuzzy clustering by Local Approximation of Membership (FLAME). It worked by defining the neighborhood of each object and Identifying cluster supporting objects that have great importance in the field of market research in order to identify not only the average profiles (centroid) but the real prototypes and assigned to other units observed a degree of similarity.

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.

The aim of this paper is to compare two different clustering methods We consider DBSCAN before in terms of reachability and after in terms of ordering (OPTICS algorithm); the other based on the use of Seg-DBSCAN our new version of DBSCAN that limits the arbitrariness of the choice of input parameters. As an application we use a meaningful data set and we compare the described methods.

Over recent years, and related in particular to the significant recent international economic crisis, an increasingly worrying rise in poverty levels has been observed both in Italy, as well as in other countries. Such a phenomenon may be analysed from an objective perspective (i.e. in relation to the macro and micro-economic causes by which it is determined) or, rather, from a subjective perspective (i.e. taking into consideration the point of view of individuals or families who locate themselves as being in a condition of hardship). Indeed, the individual “perception” of a state of being allows for the identification of measures of poverty levels to a much greater degree than would the assessment of an external observer. For this reason, experts in the field have, in recent years, attempted to overcome the limitations of traditional approaches, focusing instead on a multidimensional approach towards social and economic hardship, equipping themselves with a wide range of indicators on living conditions, whilst simultaneously adopting mathematical tools which allow for a satisfactory investigation of the complexity of the phenomenon under examination. The present work elaborates on data revealed by the EUSILC survey of 2006 regarding the perception of poverty by Italian families, through a fuzzy regression model, with the aim of identifying the most relevant factors over others in influencing such perceptions.

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 (EuSilc-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 analyze 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 present work will describe a model of data integration through a methodology of Statistical Matching (hot deck distance) for the integration of two surveys (EuSilc-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 analyze 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 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.

The presence of a varied range of definitions on the theme of poverty underlines the necessity of no longer relying on a single indicator yet rather on a group of indicators, useful in the definition of living conditions of various subjects. The starting point for this approach derives from the necessity of identifying, on the basis of statistical data, geographical zones of urban poverty, in the specific case of the area of the city of Bari. Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analysis techniques in order to understand the various phenomena. In particular, spatial clustering algorithms, which groups similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this paper is to present a density based algorithm for the discovery of clusters of units in large spatial data sets. These approaches have been improved using the SaTScan and DBSCAN or Seg-DBSCAN methodology. Further developments concern the application of a DENCLUE "weighed", obtained using the intensity of a phenomenon instead of the density kernel.

The aim of this paper is to group territorial units in areas of high intensity, using SaTScan and Seg-DBSCAN clustering methods to aggregate adjacent spatial units that are homogeneous with respect to the phenomenon being studied. SaTScan scans the region of interest with a moving window and compares a smoothing of the intensity inside and outside it so that units belonging to contiguous windows with similar intensity are aggregated into a cluster. On the other hand, Seg-DBSCAN, a new version of DBSCAN, limits the arbitrariness of the choice of input parameters and identifies clusters as dense regions in space. As an application we analyze geo-referenced data concerning housing problems in Bari and we propose a comparison between the two methods presented.

The objective of the present study is to investigate the possibility of developing an integrated database with information pertaining to the income of Italian families arising from two major surveys conducted by ISTAT (EU-SILC) and the Bank of Italy (household income survey). Since neither of the surveys has the scope to allow for the construction of a database of information pertaining to income, an integration has been sought between the data from the two archives, assuming that the surveys are reliable in terms of the accuracy of the sample design and control of the representativeness of the sample. The development of our analysis is primarily aimed at carrying out an in-depth comparison between the two surveys in terms of structure, definition of variables and sample homogeneity and secondly, through the use of an integrated dataset, at a verification measurement of the validity of the information, in particular, of the income component.

The construction of a structural equation modeling can be used to identify the latent variables underlying a determined phenomenon. Developed thanks to the impulses of the statistician psychometrician Karl Joreskog, such models are increasingly applied in the social field. The current study aims at identifying the latent variables underlying the change in family lifestyles caused by the economic recession which has spread in all countries of the European Union and continues to make its effects felt. More specifically, a research carried out by the University of Bari has been taken into account in order to monitor how the lifestyles of the families living in Bari have evolved in time of crisis.

The profound economic, social and cultural rights have taken place in recent years raise the issue of migration the subject of extensive scientific debate. In Italy, it is the presence of a diffusion model, more or less evenly distributed throughout the national territory, with some differences in the different Italian provinces (Dossier Caritas Migrantes 2009). In the work we studied aims primarily to check for actual regular foreigners in our country, analyzing the economic system at the provincial level and comparing the level of employment of non-resident foreign population than foreign. This objective is reached also through the use of methods of spatial cluster aimed at the aggregation of spatial units territorially contiguous, by forcing the various units making up each cluster.

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.

The numerous concepts of socio-economic hardship are, furthermore, attributable to a traditional distinction between absolute and relative conditions of hardship. The options of scientific research were therefore oriented towards the establishment of a multi-dimensional approach, sometimes abandoning dichotomous logic in order to arrive at fuzzy classifications in which each unit belongs and, at the same time, does not belong, to a category. The cluster analysis allows to identify the profiles families who meet certain descriptive characteristics, not defined a priori. The approach used in this work to synthesize and measure the conditions of the hardship of a population is based on a clustering procedure which is known as Fuzzy clustering by Local Approximation of Membership (FLAME) worked by defining the neighborhood of each object and identifying cluster supporting objects. This clustering method allows a set of data to belong not only to a main cluster but also to two or more clusters with “fuzzy profiles”.

La metodologia di analisi cosiddetta "Total Fuzzy and Relative" utilizza, per l’appunto, la tecnica degli insiemi sfocati per ottenere una misura di incidenza di povertà relativa nell’ambito di una popolazione, a partire dall’informazione statistica fornita da una pluralità di indicatori (Lemmi e Pannuzi, 1995). Sulla scorta di tale metodologia, con il presente lavoro si cerca di identificare zone a rischio (hot spots) tra le diverse province italiane, utilizzando indicatori di disagio socio-abitativo che consentano di determinarne il relativo grado di povertà (Montrone, Perchinunno, Torre, 2007).

La questione preliminare della identificazione delle soluzioni urbanistiche al problema dei quartieri degradati da rigenerare, in un momento storico caratterizzato da scarsità di risorse pubbliche da investire, attiene l’individuazione delle aree caratterizzate da un maggior livello di povertà urbana, in modo da orientare la scelta del decisore pubblico in modo trasparente, argomentato ed oggettivo. I metodi utilizzati (Sat Scan e DB Scan) costituiscono un utile supporto alle politiche abitative, evidenziando differenze territoriali altrimenti non emergenti con la stessa chiarezza.

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.

L’applicazione delle tecniche multivariate trova ampio utilizzo qualora si voglia studiare fenomeni/processi caratterizzati da numerose variabili che operano contemporaneamente nel tempo e nello spazio. In questo scenario, partendo da taluni indicatori, si vuole verificare l’assetto territoriale della Puglia che meglio potrebbe rispondere alle misure di pianificazione e programmazione territoriale poste in essere da soggetti di governo, enti ed autorità competenti. Come ampiamente noto, ed ormai dibattuto a tutti i livelli della società civile, la contemporanea crisi finanziaria ed economica rende di grandissima attualità l’esigenza di investigare più da vicino quelle tematiche che più direttamente coinvolgono e riguardano politiche di coesione e politiche regionali di sviluppo (si pensi, esempio, ad uno dei principali “fattori di contesto” come la pubblica amministrazione locale). Il presente contributo – in un’ottica di utilizzo di strumenti statistici spaziali – intende porre l’accento su quelle che sono le identità e le vocazioni economiche e produttive della Puglia, osservata ed analizzata in quelle che sono le proprie “aree sistema”, tentando di evidenziare – ove possibile – eventuali o meno validi rapporti/relazioni inter-istituzionali. Cosicché, sulla base dell’analisi di indicatori territoriali ed economici ci si è proposti di ipotizzare cluster quali potenziali spunti di riferimento ed adeguati strumenti funzionali alla programmazione ed adozione di efficaci ed adeguate politiche regionali di intervento???

Tra i temi di maggior interesse sia in campo economico che in campo sociale, si pone l’analisi della povertà, come fattore di evoluzione e di misura del livello di benessere della società attuale. Le profonde trasformazioni economiche e sociali avvenute negli ultimi decenni pongono il problema della povertà sotto una miriade di sfaccettature. Presupposto fondamentale per una corretta analisi statistica di tale fenomeno è la necessità di condividere una definizione univoca di povertà.

Il presente lavoro si pone come obiettivo la costruzione di un archivio integrato tra consumi e redditi delle famiglie italiane, con particolare attenzione per le famiglie considerate povere. Al fine di creare un data base integrato si ricorre alle tecniche di integrazione tra due archivi gestiti dall’Istat, l’Indagine sui Consumi delle famiglie italiane e l’Indagine su Reddito e Condizioni di vita (Eu-Silc). Le metodologie, con fondamento statistico, utilizzate per l’integrazione di dati provenienti da più fonti, possono essere raggruppate in due tipologie: record linkage (abbinamento esatto) e statistical matching (abbinamento statistico). Nel presente lavoro si fa ricorso a tecniche di abbinamento statistico con l’obiettivo di identificare record relativi ad unità simili, di stimare la distribuzione congiunta di alcune variabili osservate sui due archivi di dati e di fondere record informativi

Le tecniche di integrazione di dati provenienti da più fonti hanno l’obiettivo di identificare record relativi ad unità simili o uguali, di stimare la distribuzione congiunta di più variabili osservate su diversi archivi di dati e di fondere record informativi. Nel presente lavoro si illustra un modello di integrazione di dati attraverso una metodologia di Statistical Matching (hot deck distance) per l’integrazione tra due indagini (Eu-Silc Istat e Indagine stili di vita).

The issue of housing in Italy remains of great interest to scholars, policy makers and social partners. Although 80% of Italian families own their homes, both the quality of these houses and difficulty in meeting the needs of those weaker sections of society below the so-called “poverty line” present significant contemporary issues. Such hardship is generally concentrated in densely populated urban areas. In light of this situation, there is an urgent need to review the strategies adopted in dealing with the housing crisis in large metropolitan areas: access to owned or rented housing, the absence of services functional to residence and the overcrowding of homes of vulnerable groups are just some of the issues that create situations of housing hardship, and thus urban poverty. The present case study arises from the need to identify risk areas (hot spots) of poverty, characterized by situations of housing hardship, towards which urban regeneration policies should ideally be directed. From these considerations it becomes necessary to define and develop characteristic indicators of hardship, able to estimate poverty in defined areas (Montrone, Perchinunno, Torre, 2007). The presence of a wide range of definitions on the issue of poverty creates the necessity to develop more than a single indicator but, rather, a group of indicators in order to define the living conditions of the different subjects, departing from a dichotomous logic and instead moving towards “fuzzy” classifications in which each unit can simultaneously belong and not belong to the category of poor under certain conditions. Finally, through the use of spatial clustering methods, the aggregation of territorially contiguous spatial units may be identified through the imposition of constraints on the various component units of each cluster (Patil and Taillie 2004; Kulldorff and Nagarwalla 1995). The SaTScan (Kulldorff 1997) and the DBSCAN (Density Based Spatial Clustering of Application with Noise) methods are of particular methodological interest to the present work and are applied to different case studies in order to monitor behaviour and compliance to the context in question.

The aim of this paper is to identify territorial areas and/or population subgroups characterized by situations of deprivation or strong social exclusion through a fuzzy approach that allows the definition of a measure of the degree of belonging to the disadvantaged group. Grouping methods for territorial units are employed for areas with high (or low) intensity of the phenomenon by using clustering methods that permit the aggregation of spatial units that are both contiguous and homogeneous with respect to the phenomenon under study. This work aims to compare two different clustering methods: the first based on the technique of SaTScan [Kuldorff: A spatial scan statistics. Commun. Stat.: Theory Methods 26, 1481–1496 (1997)] and the other based on the use of Seg-DBSCAN, a modified version of DBSCAN [Ester et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceeding of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 94–99 (1996)].

The profound economic, social and cultural rights have taken place in recent years raise the issue of migration the subject of extensive scientific debate. In Italy, it is the presence of a diffusion model, more or less evenly distributed throughout the national territory, with some differences in the different Italian provinces [1, 2]. In the work we studied aims primarily to check for actual regular foreigners in our country, analyzing the economic system at the provincial level and comparing the level of employment of non-resident for-eign population than foreign. This objective is reached also through the use of methods of spatial cluster aimed at the aggregation of spatial units territorially contiguous, by forcing the various units making up each cluster.

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.

The fuzzy set approach to multidimensional poverty measurement is enjoying increasing popularity. A different, yet strongly related issue concerns geo-informatics surveillance for poverty hot-spot detection: hot-spot refers to a local outbreak of persistent poverty typologies. Circle-based spatial-scan statistics is a popular approach, now widely used by many governments and academic research teams. In this paper we define a [0;1] valued fuzzy poverty measure for the census sections in the urban area of Bari, Apulia, Italy. The scan statistics (SaTScan) and other methods (DBSCAN) were used to successfully identifying poverty clusters. The implications for digital governance are also discussed.

The numerous concepts of socio-economic hardship are, furthermore, attributable to a traditional distinction between absolute and relative conditions of hardship. The options of scientific research were therefore oriented towards the establishment of a multi-dimensional approach, sometimes abandoning dichotomous logic in order to arrive at fuzzy classifications in which each unit belongs and, at the same time, does not belong, to a category. A multidimensional index that considers hardship as the overall condition of being disadvantaged and deprived seems the most appropriate in view of the socio-economic differential analysis of demographic phenomena. The approach used in this work to synthesise and measure the conditions of the hardship of a population is based on a clustering procedure (fuzzy c-means) aimed at outlining various not defined a priori profiles, which should be assigned to each family with different socio-economic behaviours. In comparison with conventional methods, this clustering method allows a set of data to belong not only to a main cluster but also to two or more clusters with ‘fuzzy profiles’.

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 socioeconomic behaviours [2]. This clustering method allows, compared to conventional methods, a set of data to belong not

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.