An exploratory factor analysis model for slum severity index in Mexico City

Abstract

Today over half of the world population live in urban areas and it is projected that by 2050, two out of three people will live in a city. This increased rural-urban migration coupled with housing poverty has led to the growth and formation of informal settlements commonly known as slums. In Mexico, 25 percent of urban population now live in informal settlements with varying degree of depravity. Although some informal neighbourhoods have contributed to the upward mobility of the inhabitants, but the majority still lack basic services. Mexico City and the conurbation around it, forms a mega city of 21 million people that has been growing in a manner qualified as "highly unproductive, (that) deepens inequality, raises pollution levels" and contains the largest slum in the world, Neza-Chalco-Izta. Urban reforms are now aiming to better the conditions in these slums and therefore it is very important to have reliable measurement tools to assess the changes that are undergoing. In this paper, we use exploratory factor analysis to define an index of depravity in Mexico City, namely the Slum Severity Index (SSI), based on the UN-HABITATs definition of slum. We apply this novel approach to the Census survey of Mexico and measure the housing deprivation levels types from 1990 -2010. The analysis highlights high variability in housing conditions within Mexico City. We find that the SSI decreased significantly between 1990 -2000 due to several policy reforms, but increased between 2000 -2010. We also show correlations of the SSI with other social factors such as education, health and migration. We present a validation of the SSI using Grey Level co-occurrence Matrix (GLCM) features extracted arXiv:1807.06994v1 [stat.AP] 18 Jul 2018 from Very-High Resolution (VHR) remote-sensed satellite images. Finally, we show that the SSI can present a cardinally meaningful assessment of the extent of the difference in depravity as compared to a similar index defined by CONEVAL, a government institution that studies poverty in Mexico.

"Domains of Deprivation Framework" for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review

“…Many of these studies measured deprivation as a one-dimension concept of unplanned urbanization based on a physical classification of buildings, roads, and other features (Arribas-Bel et al, 2017). There are also authors who attempted to expand the number of domains and datasets to cover access to services, transportation infrastructure, and environmental risk by incorporating spatial data from volunteered geographic databases and bespoke geo-located household surveys (Hacker et al, 2013;Ajami et al, 2019;Roy et al, 2020). data source, approach, and use of maps Approach A majority (67.8%) of the articles that applied a framework produced a multiple deprivation index based on a summative (composite) approach where indicators were weighted, based on equal or expert weighting systems (Baud et al, 2008;Baud et al, 2009;Exeter et al, 2016;McLennan et al, 2019) (Table 2).…”

“Domains of Deprivation Framework” for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review

“…Many of these studies measured deprivation as a one-dimension concept of unplanned urbanization based on a physical classification of buildings, roads, and other features (Arribas-Bel et al, 2017). There are also authors who attempted to expand the number of domains and datasets to cover access to services, transportation infrastructure, and environmental risk by incorporating spatial data from volunteered geographic databases and bespoke geo-located household surveys (Hacker et al, 2013;Ajami et al, 2019;Roy et al, 2020).…”

“…A majority of the articles that applied a framework (68.1%) produced a multiple deprivation index based on a summative (composite) approach where indicators were weighted, based on equal or expert weighting systems (Baud et al, 2008;Baud et al, 2009;Exeter et al, 2016;Mclennan et al, 2019). To reduce the high dimensionality (large number) of indicators that reflect deprivation and to deal with high correlation between indicators, several studies used dimension reduction strategies, such as factor analysis or principal component analysis as well as data-driven methods that allow the generation of clusters (Marí-Dell'Olmo et al, 2011;Krishnan, 2015;Roy et al, 2020) (Figure 6). In recent years, advancements in methods such as artificial intelligence (AI) have enabled additional analyses of multiple deprivation (Ajami et al, 2019), as well as the development of deprivation measures in relation to fuzziness of concepts (Gao & Sun, 2020).…”

Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach

“…Reliable city-scale data are urgently required for tracking global policy goals such as the urban SDGs, for local policy support, or for disaster preparedness and the management of humanitarian crises (e.g., pandemics such as COVID- 19) [4][5][6]. The increasing availability of spatial data, notably Earth Observation (EO) data, has the potential to fill these data gaps when they are combined with groundbased knowledge [7]. EO allows the continuous gathering of physical and environmental data, e.g., in the form of satellite images of the planet Earth.…”