A case study in Lisbon, Portugal

Mapping the Quality of Life Experience in Alfama

Pearl May dela CruzPedro CabralJorge Mateu

Introduction

“Socrates, we have strong evidence that the city pleased you; for you would never have stayed if you had not been better pleased

with it.” — Plato

Introduction Lisbon –

45th out of 420 cities worldwide(Mercer 2010 Quality of Living Survey)

Quality of Livingvs.

Quality of Life (QoL)

Objectives Assess the urban QoL in Alfama

Determine the relationship between objective and subjective QoL

Evaluate the current situation in Alfama

Determine which subjective indicators have the highest priorities

Determine if subjective QoL is spatially autocorrelated

Hypotheses There is no linear relationship between

objective and subjective QoL

Subjective QoL is not spatially dependent

Conceptual Framework

Quality of Life (QoL)

Correlation and Spatial autocorrelation analysis

Spatial prediction

Multi-Criteria Decision Analysis (MCDA)

Indicator Domains

Physical Social Economic

Perceptions to indicators

Subjective QoL

Objective QoL

Distance to services

Study Area

Lisbon

Portugal

DNKRW 1CBE 2CBE 3CBE CSEC

Study Area

DRW – Do not know how to read and write UNIVC – Completed university course1CBE – Completed 1º cycle of basic education 1COBE – Studying 1º cycle of basic education2CBE – Completed 2º cycle of basic education 2COBE – Studying 2º cycle of basic education3CBE – Completed 3º cycle of basic education 3COBE – Studying 3º cycle of basic educationCSEC – Completed secondary education COHI – Studying in high schoolMEDC – Completed medium course COUNI – Studying in the University

2001 Educational Attainment in Alfama (INE, 2001)

15186

74 87 91 117144

0

200

400

600

800

1000

1200

1400

1600

MEDC UNIVC 1COBE 2COBE 3COBE COHI COUNIV

Fre

qu

ency

548

1437

390475

338

Study Area

2001 Building Age statistics

228

31 33

0

50

100

150

200

250

300

350

400

1919-1945 1945-1980 1980-2001

Date Built

Nu

mb

er

of

Bu

ild

ing

s

363

Before 1919

Study Area

Social Services in Alfama

Study Area

Market and Food Services in Alfama

Research Methodology

Objective QoL Subjective QoL

Perceptions

Field data collection

Distance to services

GPS points of service locations

Residential Survey

Likert scale

INPUT DATA

Research Methodology

Objective QoL Subjective QoL

Perceptions

Field data collection

Distance to services

GPS points of service locations

Residential Survey

Likert scale

INPUT DATA

Objective Indicators

Distance from a nearest service Recycling bin Parking lot Police station Recreational center Market Urban open space Main street Public transport stop Restaurant Institution High and low order

shop

10.24 m

23.12 m

Subjective IndicatorsPhysical Domain

Street cleanliness Car circulation Parking space sufficiency Green space availability

Social Domain Safety at home Safety at streets Health care center

accessibility Supermarket accessibility Public transport facility

accessibility Recreational center

accessibility Recycling bin accessibility Neighborhood InteractionEconomic Domain

Level of education Affordability of housing

cost Housing quality

CorrelatedNot correlated

Research Methodology

Not correlated

Linear regression model

Spatial autocorrelation of residuals

Variogram analysis

Regression-kriging

Correlation Analysis

Environmental Correlation

Correlated

Spatial autocorrelation of variables

Ordinary krigingIDW

>1 parameterPure nugget effect

Voronoi Polygons

Cross Validation Cross Validation

Correlated

Spatial Prediction

Not correlated

Research Methodology

Weighted Sum

Spatial predictions of all indicators

Multi-Criteria Decision Analysis (MCDA)

Specified weights of respondents

Weighted Sum

Overall QoL map

Results

Respondent Survey Points in Alfama

Results

Service locations in Alfama

Services within Alfama

Services Frequency Services Frequency

Urban open space 9 Phone booths 2

Pharmacy 1 Shower place 1

Bakeries 4 Hostel 1

Markets 15 Drinking fountains 4

Salon 7

Bus stops 2 Recycling bins 2

Tram stops 2 Museums 3

Laundry shop 1

Internet café 1 Art Galleries 2

Government offices 3 Churches 2

Police station 1 Recreational centers

2

Bank 1 Sports centers 2

Restaurants 85

High and low order shops 24

Results – Polyserial Correlation

Objective Subjective

Distance from a nearest service

Street cleanliness Car circulation Parking space Green Space

Rho P-value Rho P-value Rho P-value Rho P-value

Recycling bins 0.06 0.128 0.09 0.126 0.04 0.128 -0.05 0.130

Parking lots -0.04 0.128 0.08 0.129 <-0.0 0.130 <0.0 0.131

Police stations 0.10 0.126 0.02 0.131 0.01 0.129 0.06 0.128

Recreational centers 0.05 0.127 0.26 0.119 0.07 0.127 0.05 0.127

Markets 0.18 0.123 0.16 0.125 -0.13 0.125 <0.0 0.129

Urban open spaces 0.10 0.126 -0.02 0.128 0.02 0.127 0.09 0.128

Main streets -0.02 0.128 0.23 0.119 -0.02 0.128 -0.10 0.130

Public transport facilities

-0.03 0.127 -0.16 0.124 -0.10 0.126 -0.10 0.128

Restaurants 0.13 0.125 0.20 0.12 -0.26 0.118 0.09 0.128

Institutions -0.01 0.128 -0.16 0.124 0.04 0.128 0.33 0.112

High and low order shops

0.07 0.127 -0.07 0.126 -0.12 0.124 -0.02 0.129

Results – Moran’s I Test

Subjective Indicator Moran's Index P-value

Street cleanliness -0.075618 0.453490

Car Circulation 0.084046 0.226065

Parking Space 0.040147 0.500866

Green Space 0.029617 0.584764

Safety at Streets 0.107389 0.134920

Health Care center Accessibility -0.082397 0.405184

Supermarket Accessibility -0.056243 0.608571

Recreational Center Accessibility 0.027370 0.606232

Neighborhood Interaction -0.084477 0.388314

Level of Education 0.020318 0.664140

Affordability of Housing Cost -0.011197 0.965526

Housing Quality 0.102984 0.147231

Safety at Home 0.209053 0.005651

Public Transport Facilities Accessibility 0.215310 0.004544

Recycling Bin Accessibility 0.234164 0.002270

Voronoi Polygons - Physical1. Car Parking Space 2. Street Cleanliness

3. Green Space 4. Car Circulation

Variogram Modeling

distance

sem

ivar

ianc

e

0.05

0.10

0.15

0.20

0.25

0.30

20 40 60

distance

sem

iva

ria

nce

0.01

0.02

0.03

0.04

0.05

20 40 60 80 100

distance

sem

ivar

ianc

e

0.05

0.10

20 40 60 80 100

2. Safety at home1. Recycling bin accessibility

3. Public transport facilities

accessibility

Results – Ordinary Kriging

Excellent

Extremely Poor

Average

Above Average

Below Average

Safety at Home

Excellent

Extremely Poor

Average

Above Average

Below Average

Results – Ordinary Kriging

Recycling Bin Accessibility

Results – Ordinary Kriging

Public Transport Facilities Accessibility

Excellent

Extremely Poor

Average

Above Average

Below Average

8.942

0.009

-11.301

Public Transport Facilities Accessibility

Prediction Prediction Variance

Observed Values

Residuals Z-score

Min. 2.440 0.009422 1.000

1st Qu. 3.411 0.035374 3.000 -0.571409 -2.916

Median 3.761 0.043237 4.000 0.035570 0.189

Mean 3.681 0.038150 3.681

3rd Qu. 3.934 0.045650 4.000 0.5704147 2.670

Max. 4.682 0.051803 5.000

-2.359038

0.0003426

1.8702533 Residuals Z-score

-0.04447 -0.0822

Residuals Z-score

0.01018 0.01915

Residuals Z-score

0.000343 0.009

Safety at HomePublic Transport

Facilities AccessibilityRecycling Bin Accessibility

Results – Cross Validation (CV)

Housing Quality 0.314

Results – Weighted SumPhysical QoL

Domain Weight

Car circulation 0.19

Car parking space 0.22

Green space 0.28

Social QoL

Domain Weight

Health care accessibility

0.136

Supermarket accessibility

0.117

Public transport facilities accessibility

0.123

Recreational center accessibility

0.099

Recycling bin accessibility

0.087

Neighborhood interaction

0.076

Economic QoL

Domain Weight

Safety at home 0.182

Safety at streets 0.180

Street cleanliness 0.31

Affordability of housing cost

0.343

Level of education 0.343

Results – Weighted Sum

Physical Quality of Life

Results – Weighted Sum

Economic Quality of Life

Results – Weighted Sum

Social Quality of Life

Results – Weighted Sum

Overall Quality of Life

Overall QoL

Domain Weights

Physical 0.246

Social 0.377

Economic 0.377

Discussion & Conclusion Objective and subjective indicators are not

significantly correlated

“Absence of evidence is not evidence of absence.” (Altman & Bland, 1995)

Discussion & Conclusion Problems occurred in correlation

Sample size Geographical scale Significance criterion Validity doubts

“Moving between scales trade off the loss of heterogeneity for the gain of predictability.” (Costanza et al., 2007)

Discussion & Conclusion Inter-correlations within subjective QoL shows moderate to

high correlations

Spatial-autocorrelation has similar issues with correlation

Poor CV of variogram does not mean it is wrong ( i.e. anisotropy, nonstationarity)

Low objective QoL threshold experience (LOTE), means higher covariation with subjective QoL (Cummins, 2000)

Measured QoL is only a depiction at a particular time

Future Work Multiple correlation Improve the experimental and

semivariogram Consider anisotropy and nonstationarity Cluster analysis

Recommendation Larger geographic region Enough and well-distributed sample size Involvement of local government

Thank you!

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