Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

1. A Multidimensional Fuzzy Analysis for Urban Poverty Areas Regeneration Paola Perchinunno Università degli Studi di Bari - Dipartimento di Scienze Statistiche Carmelo Maria Torre and Francesco Rotondo Politecnico di Bari - Dipartimento di Architettura e Urbanistica Geographical Analysis, Urban Modelling, Spatial Statistics Perugia International Conference on Computational Science and Its Applications (ICCSA 2008)

2. Urban poverty and management of the metropolitan area generally represent major problems for developed and developing countries A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

3. Over the last two decades in Europe, p olicies such as the URBAN programmes, beginning in 1994 and financed until 2006, bear witness to such developments with the City of Bari itself being included in the first phases of financing between 1994 and 1999 (for the ancient quarter of S. Nicola) leading to a large-scale “gentrification”. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

4. In each Policy, the choice of the “target” areas to be interested by regeneration is based on a comparative evaluation of the various areas of the municipal territory which quite generic statistical indicators have demonstrated to be affected by urban poverty. Such indicators coincide to a large extent with those used in the present study. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

6. The starting point To identify, geographical zones of urban poverty on the basis of statistical data referring to Social- demographic aspects Structural characters of housing Case study: the City of Bari. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

8. The Total Fuzzy Relative Approach is a measurement of the FUZZY membership to the TOTALITY of the poors, in the RELATIVE interval 0-1. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

9. Supposing the observation of k indicators of poverty for every family, the function of membership of i-th family to the fuzzy subset of the poor may be defined thus: The values w j in the function of membership are only a weighting system, whose specification is: A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

11. The different indices were classified into two sets: - Social difficulty , related to the conditions of the resident population within the various census sections (educational qualifications, working conditions, overcrowding); - Housing difficulty , related to the housing conditions of dwellings occupied by residents in the various census sections (housing status, lack of functional services such as landline telephone, heating systems and designated parking space). A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

12. The context The Bari territory is 120 km 2 wide (320.000 inhabitants) is subdivided (2001 Census) in 1,312 census sections relevant to housing on 1,421 , (the remaining sections are uninhabitable or destined for other uses) The different indices were calculated at two level: individual sections individual neighbourhoods which make up the City of Bari. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

13. The application of the TFR (Total Fuzzy and Relative) method begins from the presupposition of synthesizing the seven indices elaborated in “fuzzy” values. The data arising from various census sections are classified into 4 different typologies of poverty in accordance with the resulting fuzzy value: - non-poor (fuzzy value between zero and 0.25) - slightly poor (between 0.25 and 0.50) - almost poor (between 0.50 and 0.75) - unquestionably poor (between 0.75 and 1). A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

14. Composition of absolute values and percentage values of the census sections for conditions of poverty in 2001. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas 100 100 100 1,312 1,312 1,312 Total 7.9 9.7 23.3 103 127 306 Unquestionably poor (0,75-1,00) 14.3 7.4 12.0 188 97 157 Almost poor (0,50-0,75) 27.2 29.3 19.3 357 384 253 Slightly poor (0,25-0,50) 50.6 53.7 45.4 664 704 596 Non-poor (0,00-0,25) Social and housing difficulty Housing difficulty Social difficulty Social and housing difficulty Housing difficulty Social difficulty Percentage values Absolute values Conditions of poverty

15. In addition, it is worthwhile carrying out an analysis in greater detail of how those classified as unquestionably poor are distributed across the various localities. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

16. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

17. NAIADE (Novel Approach for Imprecise Assessment in Decision Environment) (Munda 1995) The preference of an alternative with respect to another is formulated through a fuzzy measure of the comparison A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

18. The credibility of the ranking relations between two alternatives, X and Y, are as follows: φ>>(X,Y)j credibility of absolute preference for X with respect to Y φ >(X,Y)j credibility of moderate preference for X with respect to Y φ ≈(X,Y)j credibility of moderate indifference for X with respect to Y φ =(X,Y)j credibility of absolute indifference for X with respect to Y φ <(X,Y)j credibility of moderate preference for Y with respect to X φ <<(X,Y)j credibility of absolute preference for Y with respect to X A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

19. The comparison of pairs composed of alternatives is carried out with respect to a defined criteria j. indifference of X and Y. φ >>(X,Y)j, φ >(X,Y)j are near to 0 φ ≈(X,Y)j =φ =(X,Y)j are near to 1 φ >>(X,Y)j, φ >(X,Y)j are near to 1 φ ≈(X,Y)j =φ =(X,Y)j are near to 0 prevalence of X on Y. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

20. In the final evaluation of the alternatives with respect to all criteria, the comparison of pairs , obtained criteria by criteria, is aggregated . The aggregation is performed by the threshold of credibility, according to a modality of fuzzy clustering which identifies groups of relations of similar rankings relative to the differing criteria j, on the base of a credibility test α A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

21. When the credibility of the preference relationship of one alternative compared to another exceeds the threshold value, it can be deduced that the judgment has a credibility equal to 1; in the opposite case such judgment is considered to have no credibility: 0≤ φ (X,Y)≤1 if φ (X,Y)j > α for the majority of criteria j φ (X,Y) = 0 if φ (X,Y)j ≤ α for all the criteria j φ (X,Y) =1 if φ (X,Y)j ≥ α for all the criteria j and φ (X,Y)j > α for at least one of criteria j. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

22. For every X compared to every Y k alternative, two rankings are defined. Ranking Φ + (X) = credibility of the prevalence of X on Y k between [0,1], passing the Ranking Φ ˉ (X) = credibility of non-prevalence of X on Y k between [0,1], passing the A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

23. C represents the generic criterion to compare X and Y k Φ represents the criterion to compare X and Yk, passing the test A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

24. The partial symmetry of the relational pairs Φ+ and Φ- is explicit in the multi-criteria relations generated by starting from eight (seven plus one) criteria more than in that generated by starting from six criteria. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas 0.64 0.13 0.51 0.15 ~2.3 S.Pasquale 0.28 0.58 0.32 0.40 ~1.5 S.Paolo 0.01 0.97 0 0.97 ~1.1 S.Nicola 0.70 0.07 0.65 0.06 ~2.7 Picone 0.58 0.2 0.56 0.14 ~2.8 Murat 0.26 0.56 0.19 0.64 ~2.2 Madonnella 0.27 0.59 0.26 0.60 ~2.4 Libertà 0.55 0,24 0.32 0,19 ~1.6 Japigia 0.27 0.58 0.32 0.47 ~1.3 Ceglie 0.61 0.19 0.51 0.21 ~2.2 Carrassi 0.32 0.44 0.29 0.41 ~1.3 Carbonara Φˉ(X) 7+1 criteria Φ + (X) 7+1 criteria Φˉ(X) 5+1 criteria Φ + (X) 5+1 criteria Property value (thousands of euros) Neighbourhood

25. The distribution of poverty referring to indicators of social difficulty is represented by colour shades, from the highest degree of poverty (darker shades) to the lowest (lighter shades). A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

26. The distribution of poverty referring to the availability of services (presence of heating systems, of a landline telephone and of a designated parking space) are illustrated in the following figure A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

27. Belonging to the totality of poor (in terms of social difficulty) the so-called central peripheries the ancient medieval quarter (San Nicola) neighbourhoods of end of the 19th century “Libertà” and “Madonnella” (particularly the quarter of the “Duca degli Abruzzi”). A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

28. The areas characterised by darker shades, “Japigia”, “San Girolamo-Fesca” and “Stanic”, present the same characteristics as the expanded peripheral residential satellite neighbourhoods of “zone 167 ”, in as much as they have never been the direct focus of urban regeneration policy. A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

29. The distribution of poverty with regards to the availability of services, support the indications of the level of poverty relative to housing conditions in the previous figure, in respect to the central periphery including “ San Nicola ”, “ Madonnella ” and “ Libertà ”. It should be highlighted that poverty within the ancient medieval quarter (San Nicola) may be attributed to the date of the census (2001), which follows the end of the regeneration programme which effects was to take place over the next few years, supported by a funding programme from the European Community, and awarded at the time of the so-called URBAN programme A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas

30. possibility of describing the range of indicators in a single synthetic index A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas using this fuzzy model as a form of evaluation “ex post” of the effectiveness of urban policy Importance of in-depth research based on methods which privilege groups of key-indicators of a limited number, as demonstrated above. The present study provides certain considerations for the future. The effectiveness of such a method is to some degree demonstrated by the specific case which can only lead to the temptation to widen the investigation.