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Published

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

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

Published in Technology , Economy & Finance
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  • 1. Homogenous Urban Poverty Clusters within the City of Bari Francesco Campobasso, Annarita Fanizzi, Paola Perchinunno Department of Statistical Sciences “Carlo Cecchi” University of Bari
  • 2. The aims of the work
    • To determine homogenous urban poverty clusters within the City of Bari based on the most recent available data released by ISTAT (Italian National Statistics Institute) in the last General Population and Inhabitation Census (2001).
    • To identify - on the basis of determined indices - different groupings of census sections, which are sufficiently homogenous in themselves and heterogeneous with each other in terms of urban poverty.
  • 3. Introduction
    • Economic and social transformations over the last decades bring the
    • necessity of adopting actions aimed at contrasting the urban poverty.
    • Townsend: “poor families lack the resources for a quality of living conditions which are standard in the society in which they live” (relative privation).
      • 12 areas of daily life in which poverty may be manifested (alimentation, clothing, housing costs, costs within the household, living conditions, working conditions, health, education, the environment, family activities, recreational activities, social relations).
    • The use of several indicators allows a more complete representation of the living conditions of the population sections.
  • 4. Data sources
    • The data derives from the General Population and Residential Census carried out by ISTAT in 2001.
    • The geographical units of the survey are the 1,421 census sections within the City of Bari, of which 109 are uninhabited areas or are designated for other uses (parks, universities, etc.).
    The indices of urban poverty
    • The indices are chosen according to the analysis of the following aspects: education levels , working conditions and living conditions .
    • In correspondence with a high level of poverty, the value of each of the indices used is equally high.
  • 5.
    • Education levels
    • index of lack of progress to high school diploma
    • = 1- index of progress to high school diploma (as defined by ISTAT)
    • = total number of residents aged 19 or over who have not achieved a high school diploma/ total number of residents of the same age group
    • Working conditions
    • rate of unemployment (as defined by ISTAT )
    • = total number of residents aged 15 or over who are in search of employment/ workforce of the same age group.
    The indices of urban poverty
  • 6. The indices of urban poverty
    • Living conditions
    • incidence of the number of dwellings occupied by rent-paying residents
    • (with respect to the total number of dwellings occupied by residents).
    • incidence of the number of dwellings occupied by residents lacking each of the following functional services:
      • landline telephone
      • heating
      • parking space
    • (with respect to the total number of dwellings occupied by residents)
  • 7. The fuzzy grouping technique
    • Allows for the determination of the degree of membership  ig of the i -th section ( i = 1,2,...,n ) to the g -th cluster ( g = 1,2,...,c ), so that :
    • Estimates the values of  ig minimizing the object function:
    where d ij = distance between the i -th and the j -th observation and r> 1 produces a more or less fuzzy classification.
  • 8.
    • The heterogeneity of the sections within each cluster diminishes according to the decrease of the object function.
      • With the increase in r , the estimates of  ig converge at the value 1/ c , in correspondence with the most fuzzy classification.
      • With the decrease in r , each observation may be attributed to only one cluster with a degree of membership ever closer to 1.
      • In this case the classification becomes easily interpreted in correspondence to r = 1.3.
    The fuzzy grouping technique
  • 9.
    • A preliminary classification (with the method of Ward and with the distance of Mahalanobis) leads to 3 as the optimal number of clusters
    • This number is confirmed by the use of the relative index of cohesion and of the mean within-group standard deviation .
    The optimal number of clusters
  • 10.
    • Each of the 1,312 sections is attributed to the cluster to which it presents the greatest  ig (clusters are sufficiently differentiated as R 2 = 0.48)
      • 1° cluster: 516 “poor” sections (not necessarily adjacent)
      • Degraded suburbs, where socio-cultural and housing conditions are clearly lacking.
      • 2° cluster: “376” non poor sections (n.n.a.)
      • Residential zones that show low values of all the indices, with the exception of the incidence of the dwellings with a parking space.
      • 3° cluster: “420” unquestionably non-poor sections (n.n.a.)
      • Sections, characterised by buildings of recent construction, that show the lowest values of all the indices.
    The obtained clusters
  • 11. Percentage composition of clusters to which the sections of each neighbourhood are attributed
    • The poorest zones are peripheral (except for San Nicola and Libertà);
    • The zones surrounding the centre (Carrassi, ..) show less degradation;
    • The centre of Bari (Murat) does not indicate criticality in terms of poverty, except for the lack of parking spaces.
  • 12. Attribution of census sections to the 3 clusters
    • Darker shades are concentrated in the historical neighbourhoods (“central periphery”), in some of the areas of residential expansion and among the satellite neighbourhoods of the “ Zone 167” (council houses)
    Centre of Bari
  • 13. More definite classifications Classification is now limited to the sections for which the maximum degree of membership>0.5: by increasing this threshold, classification is ever more definite and the number of sections to be grouped decrease. The most clearly defined classification regards the sections with a maximum degree of membership not inferior to 0.55.
  • 14.
    • The “poor” sections, which were seen to be less homogeneous in the previous classification, are reduced from 516 to 177;
    • all neighbourhoods present a lower percentage of “poor” sections ( Ceglie passes from 84% to 76% and San Nicola from 76% to 61% ).
    New percentage composition of clusters to which the sections of each neighbourhood are attributed
  • 15.
    • Darker shades are noted only in certain satellite neighbourhoods (Carbonara, Ceglie and Loseto);
    • the areas of residential expansion near the city centre (Picone, Japigia and Carrassi) avoid being classified as areas of particular degradation.
    New attribution of census sections to the 3 clusters Centre of Bari
  • 16. Conclusions
    • We determined three different groupings of census sections, sufficiently homogenous in themselves in terms of urban poverty;
    • Assigning the 801 sections to the cluster in correspondence with which they show a degree of membership>0.55, differences emerge between
      • (1) Consolidated settlements ;
      • (2) Suburbs of the past ;
      • (3) Modern suburbs.
      • Both categories of the suburbs (of the past and modern) belong predominantly to the cluster of the poorer sections, though with different levels of hardship.
  • 17. Conclusions
      • (1) Consolidated settlements:
    • Residential areas, either central (Murat) or suburban (Picone and Carrassi), are distributed predominantly in the two non-poor clusters .
      • (2) Suburbs of the past:
    • Neighbourhoods surrounding the city centre (Libertà and Madonnella) have been overlooked in terms of urban policy in favour of the modern suburbs.
      • (3) Modern suburbs:
    • The so-called “ Zone 167 ” (San Paolo, Ceglie and Carbonara).