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Using spatial and machine learning analyses to assess satisfaction with life in an urban historical downtown area Cover

Using spatial and machine learning analyses to assess satisfaction with life in an urban historical downtown area

Open Access
|Sep 2025

References

  1. Abiodun, OI, Jantan, A, Omolara, AE, Dada, KV, Mohamed, NA & Arshad, H 2018, ‘State-of-the-art in artificial neural network applications: A survey’, Heliyon, vol. 4, no. 11, article number: e00938.
  2. Anantha, N, Stewart, T, Duncan, S & Pacheco, G 2025, ‘Using machine learning to explore the efficacy of administrative variables in prediction of subjective-wellbeing outcomes in New Zealand’, Scientific Reports, vol. 15, no. 1, article number: 6831.
  3. Anselin, L 1995, ‘Local indicators of spatial association — LISA’, Geographical Analysis, vol. 27, pp. 93–115.
  4. Benyamini, Y, Leventhal, H & Leventhal, E 2004, ‘Self-rated oral health as an independent predictor of self-rated general health, self-esteem and life satisfaction’, Social Science & Medicine, vol. 59, no. 5, pp. 1109–1116.
  5. Carrión, F 2001, ‘Centro histórico: relación social, globalización y mitos’ [‘Historic Center: Social Relations, Globalization, and Myths’] in Políticas y Gestión Para La Sostenibilidad Del Patrimonio Urbano, ed. AM Calvo, Pontificia Universidad Javeriana, Bogota, pp. 25–53.
  6. Cerulli, G 2023, Fundamentals of supervised machine learning, Springer Nature, Switzerland.
  7. Chen, Y, Wu, X, Hu, A, He, G & Ju, G 2021, ‚Social prediction: a new research paradigm based on machine learning’, The Journal of Chinese Sociology, vol. 8, no. 1, article number: 15.
  8. Crous, G, Casas, F & González-Carrasco, M 2018, ‘What aspects are important to adolescents to achieve full satisfaction in life?’, Child Indicators Research, vol. 11, no. 6, pp. 1699–1718.
  9. Di Franco, G & Santurro, M 2021, ‘Machine learning, artificial neural networks and social research’, Quality & Quantity, vol. 55, no. 3, pp. 1007–1025.
  10. Erdogan, B, Bauer, TN, Truxillo, DM & Mansfield, LR 2012, ‘Whistle while you work: A review of the life satisfaction literature’, Journal of Management, vol. 38, no. 4, pp. 1038–1083.
  11. Evans, M, Oliver, D, Zhou, X & Shekhar, S 2010, Big data: Techniques and technologies in geoinformatics, Boca Raton, US.
  12. Garrido, S, Méndez, I & Abellán, JM 2013, ‘Analysing the simultaneous relationship between life satisfaction and health-related quality of life’, Journal of Happiness Studies, vol. 14, pp. 1813–1838.
  13. Goldin, SE 2015, ‘Big data: Techniques and technologies in geoinformatics, Photogrammetric Engineering & Remote Sensing, vol. 81, no. 11, pp. 833–834.
  14. Gómez, J, Fernández, S & Mata, R 2001, ‘El paisaje, calidad de vida y territorio’ [‘The landscape, quality of life and territory’], Prisma Social, vol. 37, pp. 27–40.
  15. González Biffis, A & Etulain, JC 2018, ‘Problemáticas y estrategias para la intervención y gestión en centros históricos de Italia, España y América Latina’ [‘Issues and strategies for intervention and management in Historic Centers of Italy, Spain, and Latin America ‘], Cuaderno Urbano, vol. 24, no. 24.
  16. Gordón, S, Murillo, S & Hernández, S 2018, ‘Satisfacción con la vida y desempeño social en México: un enfoque multidimensional’ [‘Life satisfaction and social performance in Mexico: a multidimensional approach’], Sociológica (México), vol. 33, no. 94.
  17. Grömping, U 2006, ‘Relative importance for linear regression in R: The package relaimpo’ Journal of Statistical Software, vol. 17, no. 1.
  18. Gualda, E 2022, ‘Social big data, sociología y ciencias sociales computacionales’ [‘Social big data, sociology and computational social sciences’], Empiria. Revista de Metodología de Ciencias Sociales, vol. 53, pp. 147–177.
  19. Hagmaier, T, Abele, AE & Goebel, K 2018, ‚How do career satisfaction and life satisfaction associate?’, Journal of Managerial Psychology, vol. 33, no. 2, pp. 142–160.
  20. ICQ 2016, Análisis de la Encuesta Multipropósito DMQ [Analysis of the MDQ Multipurpose Survey]. Encuesta Multipropósito en el CHQ.
  21. INEC 2022, Censo de población y vivienda 2022 [2022 Population and housing census].
  22. Kim, J, Jeong, K, Lee, S & Baek, Y 2024, ‘Machine-learning model predicting quality of life using multifaceted lifestyles in Middle-Aged South Korean adults: A cross-sectional study’, BMC Public Health, vol. 24, no. 1, article number: 159.
  23. Leva, G 2005, Indicadores de calidad de vida urbana [Indicators of urban quality of life], Universidad Nacional de Quilmes. Buenos Aires, Argentina.
  24. Lucero, P, Mikkelsen, C, Sabuda, F, Aveni, S & Ondartz, A 2007, ‘Calidad de vida y espacio: una mirada geográfica desde el territorio local’ [‘Quality of life and space: A geographical perspective from the local territory‘], Universidad Nacional de Mar del Plata, vol. 7, pp. 99–125.
  25. MDMQ 2011, ECCO Distrito Metropolitano de Quito [ECCO Metropolitan District of Quito].
  26. MIDUVI 2016, Revitalización del Centro Histórico de Quito [Revitalization of the Historic Center of Quito], Subsecretaría de Hábitat y Asentamientos Humanos.
  27. Mouratidis, K 2021, ‘Urban planning and quality of life: A review of pathways linking the built environment to subjective well-being’, Cities, vol. 115, article number: 103229.
  28. Naranjo Serrano, MG, Trujillo Rodríguez, RA & Velástegui Ricaurte, NM 2020, ‘Núcleos urbanos consolidados en proceso de abandono. El caso del Centro Histórico de Quito’ [‘Consolidated urban cores in the process of abandonment: The case of the Historic Center of Quito‘], III Congreso Internacional ISUF-H. Ciudad Compacta vs. Ciudad Difusa, 1–2.
  29. Pacione, M 2003, ‘Urban environmental quality and human wellbeing—a social geographical perspective’, Landscape and Urban Planning, vol. 65, no. 1–2, pp. 19–30.
  30. Rathore, SS & Kumar, S 2016, ‘A decision tree regression based approach for the number of software faults prediction’, ACM SIGSOFT Software Engineering Notes, vol. 41, no. 1, pp. 1–6.
  31. Rodríguez, S & Cabrera-Barona, P 2024, ‘A machine learning-based assessment of subjective quality of life’, Journal of Computational Social Science, vol. 7, pp. 451–467.
  32. Sumeet Gill, SB & Jangra, V 2024, ‘Anticipating human happiness: Exploring machine learning strategies’ in Universal Threats in Expert Application and Solutions, eds VS Rathore, V Piuri, R Babo & S Karthlik, Proceedings of 3rd UNI-TEAS 2024, vol. 2, pp. 463–472.
  33. Shen, X, Yin, F & Jiao, C 2023, ‘Predictive models of life satisfaction in older people: A machine learning approach’, International Journal of Environmental Research and Public Health, vol. 20, no. 3, article number: 2445.
  34. Smith, DM 1973, The geography of social well-being in the United States: An introduction to territorial social indicators, McGraw-Hill.
  35. Suthaharan, S 2016, ‘Machine learning models and algorithms for big data classification’, Integrated Series in Information Systems, vol. 36. Springer, Boston, MA.
  36. Tonón, G 2008, ‘Investigar la calidad de vida en Argentina’ [‘Researching quality of life in Argentina’], Psicodebate, vol. 8.
  37. Tonón, G 2010, ‘La utilización de indicadores de calidad de vida para la decisión de políticas públicas’ [‘The use of indicators of quality of life for public policies decision’], Polis (Santiago), vol. 9, no. 26, pp. 361–370.
  38. Veenhoven, R 2000, ‘The four qualities of life’, Journal of Happiness Studies, vol. 1, no. 1, pp. 1–39.
  39. Zhang, C, Luo, L, Xu, W & Ledwith, V 2008, ‘Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland’, Science of the Total Environment, vol. 398, pp. 212–221.
DOI: https://doi.org/10.2478/mgrsd-2025-0027 | Journal eISSN: 2084-6118 | Journal ISSN: 0867-6046
Language: English
Page range: 257 - 266
Submitted on: Nov 28, 2024
Accepted on: May 20, 2025
Published on: Sep 14, 2025
Published by: Faculty of Geography and Regional Studies, University of Warsaw
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Jefferson Revelo, Pablo Cabrera-Barona, published by Faculty of Geography and Regional Studies, University of Warsaw
This work is licensed under the Creative Commons Attribution 4.0 License.