Rotondo Perchinunnno Torre

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


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”.



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.



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



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.



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



The Total Fuzzy Relative Approach

is

a measurement of

the FUZZY membership

to the TOTALITY of the poors,

in the RELATIVE interval 0-1.



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



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



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).



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.



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).



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.



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



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



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



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



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.



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



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.



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



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



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



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).



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



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”).



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.



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



possibility of describing the range of indicators in a single synthetic

index



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.

Rotondo Perchinunnno Torre

Technology

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