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A case study in Lisbon, Portugal Mapping the Quality of Life Experience in Alfama Pearl May dela Cruz Pedro Cabral Jorge M...
Introduction <ul><li>“ Socrates, we have strong evidence that the city pleased you; for you would never have stayed if you...
Introduction <ul><li>Lisbon –  </li></ul><ul><ul><li>45 th  out of 420 cities worldwide </li></ul></ul><ul><ul><li>(Mercer...
Objectives <ul><li>Assess the urban QoL in Alfama </li></ul><ul><ul><li>Determine the relationship between objective and s...
Hypotheses <ul><li>There is no linear relationship between objective and subjective QoL </li></ul><ul><li>Subjective QoL i...
Conceptual Framework Quality of Life (QoL) Correlation and Spatial autocorrelation analysis Spatial prediction Multi-Crite...
Study Area Lisbon Portugal
Study Area <ul><li>2001 Educational Attainment in Alfama (INE, 2001) </li></ul>DNKRW 1CBE 2CBE 3CBE CSEC DRW –  Do not kno...
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 Buil...
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 ser...
Research Methodology Objective QoL Subjective QoL Perceptions Field data collection Distance to services GPS points of ser...
Objective Indicators <ul><li>Distance from a nearest service </li></ul><ul><ul><li>Recycling bin </li></ul></ul><ul><ul><l...
Subjective Indicators <ul><li>Physical Domain </li></ul><ul><ul><li>Street cleanliness </li></ul></ul><ul><ul><li>Car circ...
Research Methodology Correlated Not correlated Not correlated Linear regression model Spatial autocorrelation of residuals...
Research Methodology Weighted Sum Spatial predictions of all indicators Multi-Criteria Decision Analysis (MCDA) Specified ...
Results Respondent Survey Points in Alfama
Results Service locations in Alfama Services within Alfama Services Frequency Services Frequency Urban open space 9 Phone ...
Results – Polyserial Correlation Objective Subjective Distance from a nearest service Street cleanliness Car circulation P...
Results – Moran’s I Test Subjective Indicator Moran's Index P-value Street cleanliness -0.075618 0.453490 Car Circulation ...
Voronoi Polygons - Physical 1. Car Parking Space 2. Street Cleanliness 3. Green Space 4. Car Circulation
Variogram Modeling 2. Safety at home 1. Recycling bin accessibility 3. Public transport facilities accessibility
Results – Ordinary Kriging Excellent Extremely Poor Average Above Average Below Average Safety at Home
Results – Ordinary Kriging Excellent Extremely Poor Average Above Average Below Average Recycling Bin Accessibility
Results – Ordinary Kriging Public Transport Facilities Accessibility Excellent Extremely Poor Average Above Average Below ...
8.942 0.009 -11.301 Public Transport Facilities Accessibility Prediction  Prediction Variance Observed Values Residuals Z-...
Results – Weighted Sum Housing Quality 0.314 Physical QoL Domain Weight Car circulation 0.19 Car parking space 0.22 Green ...
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 <ul><li>Objective and subjective indicators are not significantly correlated </li></ul><ul><li>“ A...
Discussion & Conclusion <ul><li>Problems occurred in correlation </li></ul><ul><ul><li>Sample size </li></ul></ul><ul><ul>...
Discussion & Conclusion <ul><li>Inter-correlations within subjective QoL shows moderate to high correlations </li></ul><ul...
Future Work <ul><li>Multiple correlation </li></ul><ul><li>Improve the experimental and semivariogram  </li></ul><ul><li>C...
Recommendation <ul><li>Larger geographic region </li></ul><ul><li>Enough and well-distributed sample size </li></ul><ul><l...
<ul><li>Thank you! </li></ul>
References <ul><li>Campbell, A., Converse, P., and Rodgers, W., The Quality of American Life: Perceptions, Evaluations and...
References <ul><li>Olsson, U., Drasgow, F., and Dorans, N., The polyserial correlation coefficient, Psychometrika, 47(3), ...
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Mapping the Quality of Life Experience in Alfama: A Case Study in Lisbon, Portugal Pearl May dela Cruz, Pedro Cabral

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Mapping the Quality of Life Experience in Alfama: A Case Study in Lisbon, Portugal
Pearl May dela Cruz, Pedro Cabral - Institute of Statistics and Information Management, New University of Lisbon
Jorge Mateu - Department of Mathematics, Jaume I University

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Mapping the Quality of Life Experience in Alfama: A Case Study in Lisbon, Portugal Pearl May dela Cruz, Pedro Cabral

  1. 1. A case study in Lisbon, Portugal Mapping the Quality of Life Experience in Alfama Pearl May dela Cruz Pedro Cabral Jorge Mateu
  2. 2. Introduction <ul><li>“ 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.” </li></ul><ul><li>— Plato </li></ul>
  3. 3. Introduction <ul><li>Lisbon – </li></ul><ul><ul><li>45 th out of 420 cities worldwide </li></ul></ul><ul><ul><li>(Mercer 2010 Quality of Living Survey) </li></ul></ul>Quality of Living vs. Quality of Life (QoL)
  4. 4. Objectives <ul><li>Assess the urban QoL in Alfama </li></ul><ul><ul><li>Determine the relationship between objective and subjective QoL </li></ul></ul><ul><ul><li>Evaluate the current situation in Alfama </li></ul></ul><ul><ul><li>Determine which subjective indicators have the highest priorities </li></ul></ul><ul><ul><li>Determine if subjective QoL is spatially autocorrelated </li></ul></ul>
  5. 5. Hypotheses <ul><li>There is no linear relationship between objective and subjective QoL </li></ul><ul><li>Subjective QoL is not spatially dependent </li></ul>
  6. 6. 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
  7. 7. Study Area Lisbon Portugal
  8. 8. Study Area <ul><li>2001 Educational Attainment in Alfama (INE, 2001) </li></ul>DNKRW 1CBE 2CBE 3CBE CSEC DRW – Do not know how to read and write UNIVC – Completed university course 1CBE – Completed 1º cycle of basic education 1COBE – Studying 1º cycle of basic education 2CBE – Completed 2º cycle of basic education 2COBE – Studying 2º cycle of basic education 3CBE – Completed 3º cycle of basic education 3COBE – Studying 3º cycle of basic education CSEC – Completed secondary education COHI – Studying in high school MEDC – Completed medium course COUNI – Studying in the University 15 186 74 87 91 117 144 0 200 400 600 800 1000 1200 1400 1600 MEDC UNIVC 1COBE 2COBE 3COBE COHI COUNIV Frequency 548 1437 390 475 338
  9. 9. 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 Number of Buildings 363 Before 1919
  10. 10. Study Area Social Services in Alfama
  11. 11. Study Area Market and Food Services in Alfama
  12. 12. Research Methodology Objective QoL Subjective QoL Perceptions Field data collection Distance to services GPS points of service locations Residential Survey Likert scale INPUT DATA
  13. 13. Research Methodology Objective QoL Subjective QoL Perceptions Field data collection Distance to services GPS points of service locations Residential Survey Likert scale INPUT DATA
  14. 14. Objective Indicators <ul><li>Distance from a nearest service </li></ul><ul><ul><li>Recycling bin </li></ul></ul><ul><ul><li>Parking lot </li></ul></ul><ul><ul><li>Police station </li></ul></ul><ul><ul><li>Recreational center </li></ul></ul><ul><ul><li>Market </li></ul></ul><ul><ul><li>Urban open space </li></ul></ul><ul><ul><li>Main street </li></ul></ul><ul><ul><li>Public transport stop </li></ul></ul><ul><ul><li>Restaurant </li></ul></ul><ul><ul><li>Institution </li></ul></ul><ul><ul><li>High and low order shop </li></ul></ul>10.24 m 23.12 m
  15. 15. Subjective Indicators <ul><li>Physical Domain </li></ul><ul><ul><li>Street cleanliness </li></ul></ul><ul><ul><li>Car circulation </li></ul></ul><ul><ul><li>Parking space sufficiency </li></ul></ul><ul><ul><li>Green space availability </li></ul></ul><ul><li>Social Domain </li></ul><ul><ul><li>Safety at home </li></ul></ul><ul><ul><li>Safety at streets </li></ul></ul><ul><ul><li>Health care center accessibility </li></ul></ul><ul><ul><li>Supermarket accessibility </li></ul></ul><ul><ul><li>Public transport facility accessibility </li></ul></ul><ul><ul><li>Recreational center accessibility </li></ul></ul><ul><ul><li>Recycling bin accessibility </li></ul></ul><ul><ul><li>Neighborhood Interaction </li></ul></ul><ul><li>Economic Domain </li></ul><ul><ul><li>Level of education </li></ul></ul><ul><ul><li>Affordability of housing cost </li></ul></ul><ul><ul><li>Housing quality </li></ul></ul>
  16. 16. Research Methodology Correlated Not correlated Not correlated Linear regression model Spatial autocorrelation of residuals Variogram analysis Regression-kriging Correlation Analysis Environmental Correlation Correlated Spatial autocorrelation of variables Ordinary kriging IDW >1 parameter Pure nugget effect Voronoi Polygons Cross Validation Cross Validation Correlated Spatial Prediction Not correlated
  17. 17. Research Methodology Weighted Sum Spatial predictions of all indicators Multi-Criteria Decision Analysis (MCDA) Specified weights of respondents Weighted Sum Overall QoL map
  18. 18. Results Respondent Survey Points in Alfama
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. Voronoi Polygons - Physical 1. Car Parking Space 2. Street Cleanliness 3. Green Space 4. Car Circulation
  23. 23. Variogram Modeling 2. Safety at home 1. Recycling bin accessibility 3. Public transport facilities accessibility
  24. 24. Results – Ordinary Kriging Excellent Extremely Poor Average Above Average Below Average Safety at Home
  25. 25. Results – Ordinary Kriging Excellent Extremely Poor Average Above Average Below Average Recycling Bin Accessibility
  26. 26. Results – Ordinary Kriging Public Transport Facilities Accessibility Excellent Extremely Poor Average Above Average Below Average
  27. 27. 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 1 st 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 3 rd Qu. 3.934 0.045650 4.000 0.5704147 2.670 Max. 4.682 0.051803 5.000 -2.359038 0.0003426 1.8702533 Safety at Home Public Transport Facilities Accessibility Recycling Bin Accessibility Results – Cross Validation (CV) Residuals Z-score -0.04447 -0.0822 Residuals Z-score 0.01018 0.01915 Residuals Z-score 0.000343 0.009
  28. 28. Results – Weighted Sum Housing Quality 0.314 Physical 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
  29. 29. Results – Weighted Sum Physical Quality of Life
  30. 30. Results – Weighted Sum Economic Quality of Life
  31. 31. Results – Weighted Sum Social Quality of Life
  32. 32. Results – Weighted Sum Overall Quality of Life Overall QoL Domain Weights Physical 0.246 Social 0.377 Economic 0.377
  33. 33. Discussion & Conclusion <ul><li>Objective and subjective indicators are not significantly correlated </li></ul><ul><li>“ Absence of evidence is not evidence of absence.” (Altman & Bland, 1995) </li></ul>
  34. 34. Discussion & Conclusion <ul><li>Problems occurred in correlation </li></ul><ul><ul><li>Sample size </li></ul></ul><ul><ul><li>Geographical scale </li></ul></ul><ul><ul><li>Significance criterion </li></ul></ul><ul><ul><li>Validity doubts </li></ul></ul><ul><li>“ Moving between scales trade off the loss of heterogeneity for the gain of predictability.” (Costanza et al., 2007) </li></ul>
  35. 35. Discussion & Conclusion <ul><li>Inter-correlations within subjective QoL shows moderate to high correlations </li></ul><ul><li>Spatial-autocorrelation has similar issues with correlation </li></ul><ul><li>Poor CV of variogram does not mean it is wrong ( i.e. anisotropy, nonstationarity) </li></ul><ul><li>Low objective QoL threshold experience (LOTE), means higher covariation with subjective QoL (Cummins, 2000) </li></ul><ul><li>Measured QoL is only a depiction at a particular time </li></ul>
  36. 36. Future Work <ul><li>Multiple correlation </li></ul><ul><li>Improve the experimental and semivariogram </li></ul><ul><li>Consider anisotropy and nonstationarity </li></ul><ul><li>Cluster analysis </li></ul>
  37. 37. Recommendation <ul><li>Larger geographic region </li></ul><ul><li>Enough and well-distributed sample size </li></ul><ul><li>Involvement of local government </li></ul>
  38. 38. <ul><li>Thank you! </li></ul>
  39. 39. References <ul><li>Campbell, A., Converse, P., and Rodgers, W., The Quality of American Life: Perceptions, Evaluations and Satisfaction, Russel Sage Foundation, New York (1976) </li></ul><ul><li>Rogerson, R., Quality of life and city competitiveness, Urban Studies, 36(5), 969-985 (1999) </li></ul><ul><li>Tuan Seik, F., Subjective assessment of urban quality of life in Singapore (1997-1998), Habitat International, 24(1), pp. 31-49 (2000) </li></ul><ul><li>Costanza, R., Fisher, B., Ali, S., Beer, C., Bond, L. et al., Quality of lif: An approach integrating opportunities, human needs, and subjective well-being, Ecological Economics, 61(2-3), pp. 267-276 (2007) </li></ul><ul><li>Bonnes, M, Uzzell, D., Carrus G., and Kelay, T., Inhabitants’ and experts’ assessments of environmental quality for urban sustainability, Journal of Social Issues, 63(1), pp. 59-78 (2007) </li></ul><ul><li>Das, D., Urban quality of life: A case study of Guwahati, Social Indicators Research, 88(2), pp. 297-310 (2007) </li></ul><ul><li>Tesfazghi, E., Martinez, J., and Verplanke, J., Variability of quality of life at small scales: Addis Ababa, Kirkos sub-city, Social Indicators Research 98(1), pp. 73-88 (2010) </li></ul><ul><li>Bowling, A., Measuring Health: A Review of Quality of Life Measurement Scales, American Journal of Physics, Berkshire, UK (2005) </li></ul><ul><li>Paler-Calmorin, L., and Calmorin, M., Statistics in Education and the Sciences: With Application to Research, Rex Bookstore Inc., Quezon City (1997) </li></ul>
  40. 40. References <ul><li>Olsson, U., Drasgow, F., and Dorans, N., The polyserial correlation coefficient, Psychometrika, 47(3), pp. 337-347 (1982) </li></ul><ul><li>Upton, G., and Fingleton, B., Spatial Data Analysis by Example: Point Pattern and Quantitative Data, Wiley, University of Michigan (1985) </li></ul><ul><li>Bivand, R., Pebesma, E., and Gomez-Rubio, V., Applied Spatial Data Analysis with R, Springer (2008) </li></ul><ul><li>Hengl, T., A practical Guide to Geostatistical Mapping, Office for Official Publicationsof the European Communities, Luxembourg (2009). </li></ul><ul><li>Okabe, A., Boots, B., and Sugihara, K., Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, John Wiley & Sons Ltd (1992) </li></ul><ul><li>Kim, I.Y., and de Weck, L., Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation, Structural and Multidisciplinary Optimization, 31(2), pp. 105-116 (2005) </li></ul><ul><li>Altman, D., and Bland, J., Statistics notes: Absence of evidence is not evidence of absence, BMJ, 311, pp. 485 (1995) </li></ul><ul><li>Veenhoven, R., Why social policy needs subjective indicators, Social Indicators Research, pp. 33-45 (2004) </li></ul><ul><li>Cummins, R., Objective and subjective quality of life: An interactive model, Social Indicators Research, 52(1), pp. 55-72 (2000) </li></ul>

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