Methodology

The VU Inequality research team sought to determine how urban fragmentation and inequality manifest differently across cities in the Global South. To do this, the research team chose 6 cities that fared similarly on measures related to inequality. The primary measures used in comparing cities were the GINI Coefficient and the City Prosperity Index.

After considering the available data, the research team chose the following cities:

Africa	Asia	Latin America
Accra, Ghana	Ho Chi Minh City (HCMC), Vietnam	Buenos Aires, Argentina
Nairobi, Kenya	Mumbai, India	Mexico City, Mexico

City Selection Process

The GINI Coefficient measures inequality by examining income distribution. The UN Department of Economic and Social Affairs defines the GINI Coefficient as follows:

The most widely used indicator of inequality is the Gini coefficient, which ranges from 0 (perfect equality) to 100 or 1 (complete inequality, in the sense that one person has all the income – or consumption – while others have none). The closer the coefficient is to 100 (or 1, depending on the scale used), the more unequal the distribution. The Gini coefficient of within-country inequality measures the distribution of income (or consumption) among individuals or households in each country (UN DESA, 2020).

The cities chosen for VU Inequality all had GINI coefficients ranging between .4-.59.

The City Prosperity Index (CPI) uses 6 dimensions to measure prosperity in urban areas. The CPI was created by UN-Habitat based on the following definition of “prosperity”:

Prosperity implies success, wealth, thriving conditions, well-being as well as confidence in the future and opportunities for all. Further, prosperous cities offer a profusion of public goods, allowing for equitable access to ‘commons’ and the development of sustainable policies. Based on this, UN-Habitat conceptualized urban prosperity as follows: Productivity, Infrastructure, Quality of Life, Equity and Social Inclusion, Environmental Sustainability, and Urban Governance and Legislation (UN-Habitat, 2012).

The research team searched for cities with comparable CPIs, and all the cities chosen had CPIs that fell between 46%-55%.

Data Analysis Process

The research team took an exploratory research approach, beginning with questions, observing patterns, and developing hypotheses in order to discover relevant independent variables that might support explanatory research. By selecting cities that had similar GINI Coefficients and City Prosperity Indices, the research team had the freedom to explore the unique landscape of each city while holding the level of inequality roughly constant across the cities.

The research team searched for geospatial data in five key areas: housing, transit, health and wellness, economic opportunity, and demography. The team focused on analyzing areas like housing density, the location of public transportation nodes, the location of healthcare facilities, the proximity of low-income areas to the cities’ central business districts (CBD), and population density in neighborhoods across the city.

Our analysis integrated both quantitative and qualitative data and included studies of the built environment, topography, and urban form. Drawing from Kohli et al.’s Slum Ontology (2012), our data incorporated spatial information on multiple levels.

The team examined topography and city plans on the “Environs” level, the patterns of waterways and roads on the “Settlement” level, and the location of buildings like medical facilities and schools on the “Object” level (Kohli, 2012).