Spatial Variability Analysis of Quality of Life and Its Determinants: A Case Study of Medellín, Colombia

Fabio Humberto Sepúlveda Murillo (), Jorge Chica Olmo () and Norely Margarita Soto Builes ()
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Fabio Humberto Sepúlveda Murillo: University of Medellin
Jorge Chica Olmo: University of Granada, Spain
Norely Margarita Soto Builes: University of Medellin

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2019, vol. 144, issue 3, No 11, 1233-1256

Abstract: Abstract According to the Gini indicator, Medellín—the capital of the Department of Antioquia, Colombia—has been considered the most unequal city in Colombia for several consecutive years with regard to inequity in its residents’ quality of life (QoL) level. Therefore, this research mainly aimed to explore the spatial variations in the QoL of households and the determinants that explain it, using some geographically weighted techniques. This analysis becomes substantial when it is intended to contribute to government policies and programs that seek the well-being of individuals. For this purpose, an indicator that integrated both objective and subjective variables to measure the QoL of households in Medellín was constructed. The local and global spatial autocorrelation indexes were used to visualize and analyze the geographic structure of the quality of life indicator. The global or conventional principal components analysis and the geographically weighted principal components analysis were used to identify spatial trends and explore the spatial variations of the determinants that explain the QoL, respectively. The results confirm that the QoL and the factors explaining it are highly spatially heterogeneous in Medellín, being extremely supportive of appropriate authorities for spatial planning and developing strategies that help to improve the living conditions of homes in the city.

Keywords: Quality of life; Spatial non-stationarity; Spatial autocorrelation; Principal components analysis; Geographically weighted principal components analysis (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11205-019-02088-x

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