Spatial multicriteria model to analyze residential segregation in the colonias of El Paso, Texas

Residential segregation, as an expression of the socio-economic differences of the population in the territory, is a phenomenon that has been studied from different perspectives, since segregation spaces manifest themselves in different ways, depending on the socio-cultural context in which they occ...

Full description

Saved in:
Bibliographic Details
Main Authors: Adrián Botello Mares, Erick Sánchez Flores
Format: Artículo
Language:spa
Published: Universidad Autónoma de Ciudad Juárez 2020
Subjects:
Online Access:http://erevistas.uacj.mx/ojs/index.php/decumanus/article/view/3991
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Residential segregation, as an expression of the socio-economic differences of the population in the territory, is a phenomenon that has been studied from different perspectives, since segregation spaces manifest themselves in different ways, depending on the socio-cultural context in which they occur. However, having tools that allow its systematic identification and characterization, facilitates its approach as public policy spaces, for the improvement of the population’s living conditions. In this paper, we present the conceptual and methodological bases to approach the phenomenon of residential segregation from a set of spatial variables that explain objectively its distribution, using a multicriteria evaluation model. Particularly, we analyzed the case of study of the border city of El Paso, Texas, and its colonias, considering basic services, accessibility and population characteristics variables, derived from 2015 census data. Results show the highest concentration of residential segregation in the so-called colonias, because of the income conditions, below the poverty line, the distance from the central business district of the city, the lack of public transportation routes, and the deficiencies in residential services. The spatially expressed segregation variables allow for a better understanding of the phenomenon in measurable terms.
ISSN: