Spatial segregation and urban form in Mexican cities

Garnica-Monroy, Rubén and Alvanides, Seraphim (2019) Spatial segregation and urban form in Mexican cities. Environment and Planning B: Urban Analytics and City Science, 46 (7). pp. 1347-1361. ISSN 0265-8135

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Abstract

With a population of more than 120 million people, most Mexican cities suffer from socioeconomic inequalities and these are worsened by spatial inequalities resulting from their urban form and structure. A number of studies have been published looking at limited public services and how this may affect those on lower incomes in Mexico. However, very few studies have used spatial accessibility as a means of explaining urban inequalities and informing city planning. Spatial accessibility is defined as the measurable geographical access from residential locations to destinations for services and public goods. This paper investigates the potential of a spatial accessibility index to provide evidence for making location/planning decisions that benefit the largest number of residents. To achieve this aim, we combine three geographical secondary data sets with geospatial analysis. First, two important urban public services, i.e. health and education, are located, and catchment areas are then generated according to their respective sizes. For the second set, the integration values of each city at metropolitan and district scales are calculated using Space Syntax to observe how its configuration may influence the distribution of activities and the way the inhabitants live and move. The third set is demographic data from the 2010 national census of Population and Housing, analysed at the residential block level. This paper focuses on the results of combining two accessibility indices (cumulative opportunities and Space Syntax’s integration value) that examine the degree to which the lack of a basic public service could be associated with the spatial segregation of an area to provide evidence (comprehensible by decision-makers) for making location and planning decisions that benefit the largest number of residents.

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