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Abstract
This study aims to explore the effect of the neighborhood scale when estimating public services inequality based on the aggregation of social, environmental, and health-related indicators. Inequality analyses were carried out at three neighborhood scales: the original census blocks and two aggregated neighborhood units generated by the spatial “k”luster analysis by the tree edge removal (SKATER) algorithm and the self-organizing map (SOM) algorithm. Then, we combined a set of health-related public services indicators with the geographically weighted principal components analyses (GWPCA) and the principal components analyses (PCA) to measure the public services inequality across all multi-scale neighborhood units. Finally, a statistical test was applied to evaluate the scale effects in inequality measurements by combining all available field survey data. We chose Quito as the case study area. All of the aggregated neighborhood units performed better than the original census blocks in terms of the social indicators extracted from a field survey. The SKATER and SOM algorithms can help to define the neighborhoods in inequality analyses. Moreover, GWPCA performs better than PCA in multivariate spatial inequality estimation. Understanding the scale effects is essential to sustain a social neighborhood organization, which, in turn, positively affects social determinants of public health and public quality of life.
Highlights
Analyzing the relationship between the state of the public social services and environmental health has become a major issue for quality of life analysis [1,2]
Using the global principal components analyses (PCA), it was possible to ascertain that the first component loading of seven multiple accessibility indicators encompassed 67.46% and 65.29% of the percentage of the total variances (PTVs) for the SKATER-based and the self-organizing map (SOM)-based zoning system, respectively, and that these values are higher than the 42.34% PTVs found in the original census blocks
In the geographically weighted principal components analyses (GWPCA), these investigations and interpretations all take place locally in each neighborhood unit, that is, the PTVs for the first component in two aggregated zoning systems and in the original census blocks are all summarized at each data location in the study area
Summary
Analyzing the relationship between the state of the public social services and environmental health has become a major issue for quality of life analysis [1,2]. Public social services and facilities are defined as the urban objects that are designated to fulfill supportive functions related to the health and well-being of the citizens of an urban area [3]. Two key aspects of research on the impact of public services have been identified [6]. The first focuses on inequalities of public service accessibility in neighborhood segregation effects. A broad range of public accessibility variables, such as access to schools [7], access to food [8,9,10], access to green spaces [11,12,13], access to health services [14,15,16,17], access to recreational services [18,19], etc., play a crucial role in social capitaldefinitions, real assets values, and environmental conditions
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