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Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"TRANSCRIPT
A Multidimensional Fuzzy Analysis for Urban
Poverty Areas Regeneration
Paola PerchinunnoUniversità degli Studi di Bari - Dipartimento di Scienze Statistiche
Carmelo Maria Torre and Francesco RotondoPolitecnico di Bari - Dipartimento di Architettura e Urbanistica
Geographical Analysis, Urban Modelling, Spatial Statistics
Perugia
International Conference on Computational Science and Its Applications (ICCSA 2008)
Geographical Analysis, Urban Modelling, Spatial Statistics
Perugia
International Conference on Computational Science and Its Applications (ICCSA 2008)
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
Over the last two decades in Europe, policies 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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
The Aim is to address policies for
1. urban regeneration of neighborhoods
2. housing policies in condition of urban poverty and inaccessibility
to real estate market
using appropriate and clearly demonstrated priorities
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
The theme of poverty is characterised by:
varied range of definitions
Varied range of indicators useful to define quality of housing and
living status
The chosen approaches
1. Total Fuzzy and Relative, to define urban poverty
2. As a test: Comparing urban poverty and real estate value by
multicriteria evaluations
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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:
niw
wxg
xfk
jj
k
jjji
i ,.....,1
).(
)(
1
1.
The values wj in the function of membership are only a weighting
system, whose specification is:
)(/1log jj xgw
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
The choice of poverty indices:
1. educational levels (lack of scholar progress)
2. working conditions (unemployment rates)
3. housing conditions (overcrowding and
homeownership)
Along with the quality of housing, the presence of:
1. landline telephone
2. heating system
3. parking spaces
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
The context
The Bari territory is 120 km2 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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
Conditions of poverty
Absolute values Percentage values
Social difficulty
Housing difficulty
Social and housing difficulty
Social difficulty
Housing difficulty
Social and housing difficulty
Non-poor (0,00-0,25) 596 704 664 45.4 53.7 50.6
Slightly poor (0,25-0,50) 253 384 357 19.3 29.3 27.2
Almost poor (0,50-0,75) 157 97 188 12.0 7.4 14.3
Unquestionably poor (0,75-1,00) 306 127 103 23.3 9.7 7.9
Total 1,312 1,312 1,312 100 100 100
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Loseto
Murat
Torre a Mare
Japigia
Palese
Picone
Carrassi
Carbonara
S.Girolamo Fesca
S.Paolo
Stanic
S.Pasquale
S.Spirito
Ceglie
Libertà
Madonnella
S.Nicola
Unquestionably poor Almost poor Slightly poor Non poor
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Loseto
Murat
Torre a Mare
Japigia
Palese
Picone
Carrassi
Carbonara
S.Girolamo Fesca
S.Paolo
Stanic
S.Pasquale
S.Spirito
Ceglie
Libertà
Madonnella
S.Nicola
Unquestionably poor Almost poor Slightly poor Non poor
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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 0prevalence of X on Y.
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
For every X compared to every Yk alternative, two rankings are
defined.
Ranking Φ+(X) = credibility of the prevalence of X on Yk between
[0,1], passing the
Ranking Φˉ(X) = credibility of non-prevalence of X on Yk between
[0,1], passing the
testα
testα
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
1
11 1
1 1
[ ( , ) ^ ( , ) ( , ) ^ ( , )]
( )
( , ) ( , )
n
k k k kk
n n
k kk k
X Y C X Y X Y C X Y
X
C X Y C X Y
1
11 1
1 1
[ ( , ) ^ ( , ) ( , ) ^ ( , )]
( )
( , ) ( , )
n
k k k kk
n n
k kk k
X Y C X Y X Y C X Y
X
C X Y C X Y
C represents the generic criterion to compare X and Yk
Φ represents the criterion to compare X and Yk, passing the test
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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. Neighbourhood
Property value
(thousands of euros)
Φ+(X)5+1
criteria
Φˉ(X)5+1
criteria
Φ+(X)7+1
criteria
Φˉ(X)7+1
criteria
Carbonara ~1.3 0.41 0.29 0.44 0.32
Carrassi ~2.2 0.21 0.51 0.19 0.61
Ceglie ~1.3 0.47 0.32 0.58 0.27
Japigia ~1.6 0,19 0.32 0,24 0.55
Libertà ~2.4 0.60 0.26 0.59 0.27
Madonnella ~2.2 0.64 0.19 0.56 0.26
Murat ~2.8 0.14 0.56 0.2 0.58
Picone ~2.7 0.06 0.65 0.07 0.70
S.Nicola ~1.1 0.97 0 0.97 0.01
S.Paolo ~1.5 0.40 0.32 0.58 0.28
S.Pasquale ~2.3 0.15 0.51 0.13 0.64
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
possibility of describing the range of indicators in a single synthetic
index
A Multivariate Fuzzy Analysis for the Regeneration of Urban Poverty Areas
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.