Implementation of Spatial Correlations and Kernel Densities to Identify Deficiencies in the Coverage of Public Health Infrastructure Chapter in Scopus uri icon

abstract

  • There is a justified perception of shortage of public health infrastructure on the outskirts of cities. This deficiency in urban planning is aggravated in the peri-urban settlements inhabited by population living in poverty conditions, which unlike the suburbs for the middle and upper classes, planned according to the North American model and the Garden City theory, they show distinct conditions of precariousness, deprivation and a worrying limitation for accessing to urban services. In order to mitigate this lag in urban planning, it is important to first identify the lack of coverage of urban health facilities and understand the spatial correlations that intervene in the access of the population living at the edges of the urban growth to the benefits of a consolidated city. Thus, this research proposes an exploratory spatial data analysis (ESDA), which reviews the spatial correlations based on the number of active cases of people with COVID-19 per neighborhood for Mexico City, as a local indicator of spatial association (LISA). Afterwards, it compares them with heat maps using public health facilities as origins and weighted by the saturation of their intensive care units, to finally integrate them cartographically through the implementation of kernel densities, generated from hospital capacity and coverage. This methodology shows patterns of deficiencies in the coverage of public health infrastructure that corroborates the perception of abandonment of popular neighborhoods which in the context of the SARS-CoV-2 virus pandemic, have shown a higher incidence of cases, thus highlighting the precarious conditions in which they find themselves. Through the identification of these spatial arrangements, it is possible to plan urban development policies that improve the conditions of Latin American cities in the face of an accelerated urban expansion. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

publication date

  • January 1, 2025