Urban Inequalities from Space

Urbanisation and digitalisation are two of the megatrends characterising contemporary human society. Digital broadband access is an essential enabler, and despite its large growth potential, it can differ across territories. Taking a comparative approach from a global perspective, this chapter studies the relationship between urbanisation and digitalisation by looking at the quality of broadband access in urban centres using geospatial data processing. It is based on a combination of open and free data sourced from earth observation (Copernicus and Landsat programmes) to map and classify human settlements, with social sensing data to assess broadband quality with open data released by Ookla® at the grid level. We analyse the database in a stratified way to identify whether urban centres in high-income countries are better in terms of broadband connectivity compared to those in developing economies; whether urban centre population size is an advantage in the regions of the world where connectivity is low; and whether urban centres that have experienced stronger population growth in recent years display an advantage in terms of digitalisation. This work sheds light on the nature and type of deprivation related to uneven access to infrastructure, especially digital ones. The results indicate significant geographical and income disparities in terms of internet download speeds across the world. The performance of mobile and fixed broadband connectivity is different, and mobile connectivity offers a higher performance alternative to fixed networks in less affluent countries.

Societal inequalities manifest at a range of scales, from coarse (inter-continent) to fine (intra-city). Satellite-measured night-time lights (NTL) have shown value for capturing and estimating socioeconomic characteristics, including economic activity, well-being, and poverty. However, multi-scale mapping and visualization of inequalities, especially their relative gradations and spatial patterns, have remained a challenge. To narrow this gap, we developed an approach that combines globally available built-up surface, population density, and night-time light intensity data. The integration of these earth observation-derived variables through a spatial visualization frame reveals patterns of societal inequalities at different scales. Our findings suggest that: (1) Outlining and mapping settlements using night-time lights alone underrepresent settlements of low-income countries, as both rural and suburbia of larger cities of the Global South are scarcely lit at night. (2) Combining population and built-up density that spatially locate people on the surface of the Earth with NTL provides insights on deprivation related to the lack of electricity and the services that come with it. (3) Night-time lights and inequality maps are the results of many factors that need to be addressed at different scales. A body of scientific literature that we review has just started to describe the variety of night-time light sources and the spatial variation within and across countries. New, fine-resolution NTL, population, and built-up density that are now becoming available may provide additional insights.

Informal settlements are an integral feature of global urbanization, and their future dynamics will impact cities’ development. Current projections of informal settlements’ growth estimate these areas’ expansion, presenting increasing challenges for cities and countries. However, data scarcity on the phenomena of informal settlements impedes answering essential questions on their location, size, and population density that could contribute to a more nuanced understanding of informal growth dynamics. This study combines spatial data from 30 cities’ spatial distribution of informal settlements with the Atlas of Informality, containing 447 neighborhoods, to map the spatial distribution of informal settlements within cities worldwide. In addition, the research locates informal settlements within urban centers to understand the relationship between urban and peri-urban expansion. This exploratory study quantifies informal settlements’ presence and concentration in peri-urban areas. The location mapping reveals that up to 60% of informal settlement areas are in peri-urban zones beyond municipal administrative boundaries. However, this phenomenon differs depending on the world region, albeit the study’s sample size limits current findings. Such insights are highly relevant in influencing policy actions to achieve SDG Target 11.1, as interventions targeted at slums or informal settlements are linked to political and administrative boundaries.

We present a pilot implementation to identify and define slums spatially, using the integrated deprived area mapping system (IDEAMAPS)’ deprivation framework. This pilot allows the integration of diverse datasets toward using a data lake architecture developed by INEGI and a grid-based approach to build an ecosystem of interoperable information with an interactive user interface. The pipeline also offers spatial scalability, transferability, and flexibility to work with different data sources, while the grid-based design can facilitate the exploration of interactions among variables. The proposed methodology was tested on a hypothetical case of use in Mexico City, integrating more than 150 variables related to slum conditions in the IDEAMAPS’ framework. Additionally, we created a visualization tool to explore the integrated data.

It is estimated that more than half of city dwellers in sub-Saharan Africa currently live in deprived urban areas, often called slums or informal settlements, although these terms cover different urban realities. While the first target of Sustainable Development Goal (SDG) 11 is “to ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums,” there is a huge gap in timely spatial data to support evidence-based policies and monitor progress toward that objective. In this study, we document the potential of Earth Observation (EO) for mapping and characterizing deprived urban areas (DUAs) to narrow this gap. First, we provide a synthesis of user requirements that can be met without resorting to ancillary sources such as censuses and socioeconomic surveys, and we propose a list of cost criteria that should be minimized in EO workflows. Next, we present the city-scale and DUA-scale workflows that we developed based on three case studies and an assessment of their suitability for supporting pro-poor policies, in light of the cost criteria. We also share the main lessons learned and propose some avenues for future research.

Updated and reliable data on slums’ location, extent, morphology, and living conditions is critical to implementing and evaluating the effectiveness of slum improvement programs. Object-based image analysis (OBIA) allows extracting such information from imagery using shape, texture, density, and context to resemble human recognition of image objects. Although slums share similar characteristics, such as density, locations, and building orientations, they may differ locally, which requires adaptations of the OBIA ruleset; this reduces its transferability. Also, the most common approach in measuring transferability is using accuracy assessment by comparing the OBIA result with reference data created by domain experts using visual indicators. However, image interpreters have various local and professional experiences. Consequently, their choice of indicators to conceptualise slums and how they are delineating objects may lead to ambiguous results regarding mapped slum existence and extent. Our research aims to understand how respondents’ backgrounds impact slum conceptualisations and transferability measurement. We used three subsets in Jakarta, Indonesia, with different morphological characteristics and asked respondents with varied backgrounds to indicate slums and their characteristics. Our research concludes that different sources of uncertainties come from different understandings of slums, and the inability of rule-based OBIA to handle all uncertainties limits the ability to create a transferable OBIA ruleset for slum detections. We also conclude that the usage of accuracy assessment in measuring the performance of the image classification algorithm might be misleading when we are unaware of uncertainties in reference data.

Lima, Peru, is a highly dynamic urban region home to perpetually evolving informal areas. Earth observation (EO) studies on these areas focused almost solely on their inhabited parts, the informal housing. In this study, we propose to extend the focus to another component of the informal settlements: informal graveyards. Their emerging morphologies in Lima are similar to informal housing, making this particular distinction challenging. Furthermore, both graveyards and housing typically experience joint, intertwined spatial development. The adjacency of graveyards and informal housing causes social and public health risks. Therefore, detection of boundaries between graveyards and adjacent (in)formal housing is essential, e.g. as an information basis for preventing the spread of diseases and supporting public health and safety in general. However, housing invasions on burial grounds have not yet been systematically monitored. Therefore, this study aims to develop a method for the distinction of graveyards from (in)formal housing. We combined anthropological field observations with fully convolutional networks (FCNs) with dilated convolution of increasing spatial kernels to acquire features of deep level of abstraction on Pleiades optical satellite images. The trained neural network developed reaches good accuracies in mapping informal graveyards, (in)formal housing, and non-built areas with an average F1 score of 0.878.

Officially, by 2019, more than five million households in 734 Brazilian municipalities were in slums, accounting for 7.8% of the total households. Due to the country’s continental dimensions, there is a diversity of forms and names for these settlements, officially called by the Brazilian Institute of Geography and Statistics (IBGE 2020a) substandard agglomerations (SAs), a proxy for slums, mapped by IBGE from official census data and other ancillary information and comprises areas containing at least 51 households on public or private land whose residents do not have legal land tenure, face substandard road structures and lot sizes, and have no adequate sanitation services. The SA data is available as georeferenced vector files, last updated in 2020, containing approximately 13,000 polygons. Although the delimitation of SA considers the entire country’s territory, it is only done every 10 years with the demographic census and considers population and household counts, not urbanized area expansion from a remote sensing perspective. On the other hand, the MapBiomas initiative produces annual collections of land use and land cover maps, between 1985 and 2020 in the case of collection 6, using Landsat time series (spatial resolution of 30 m) and applying artificial intelligence techniques such as random forest and U-net in a cloud computing environment (Google Earth Engine), with a total of 23 different classes of land use and land cover, including urbanized areas. By combining the two data collections (SA and MapBiomas), this research uses the SA boundaries (fixed in 2020) overlaid on the patches of urbanized areas in Brazil (annually between 1985 and 2020) and, from this, calculates the annual increments of urbanized area for each SA, subdividing the calculations into two spatial subsets: inside and outside the SA. This is done to answer the following question: What are the spatial–temporal dynamics of the SA (slums) urbanization in Brazil? Additionally, the predominant types of land cover transitions to urbanized areas in these spatial subsets are calculated. Finally, we aggregate the resulting statistics at regional (states) and local (municipalities) scales for all 5573 Brazilian cities, analyzing the most predominant patterns. The data processing is based on cloud processing techniques using the Google Earth Engine (GEE) platform. The results pointed out that, in 36 years throughout Brazil, there was an expansion of the occupation of SA areas of 84.000 ha, equivalent to 3.8 times the size of the city of Amsterdam, in the Netherlands. In the two main cities of the Amazon area, Manaus and Belém, the growth of the urban sprawl inside SAs corresponded to more than 50% of the total urbanized area growth in the time series, suggesting that slum growth is not an exception but almost a rule in the region. Compared to São Paulo and Rio de Janeiro, the two more consolidated Brazilian capitals where the slum growth corresponded to 24% and 10%, respectively, the results indicate that the pace and magnitude of slum expansion in different regions of the country present distinct temporalities and spatiality opposing already consolidated areas to new fronts of urban poverty expansion. Furthermore, considering the transitions between classes, we estimated the urban sprawl of SA over native vegetation areas over time and compared it with the same type of transition in areas of formal urbanization. As a result, we found that SA corresponds to the lowest total and proportional occupations of native vegetation.

Rapid urbanization in Africa and Asia is responsible for 80% of all global cropland losses caused by urban expansion. The lack of data has made it difficult to estimate the impact of successive land policies on arable land loss. In this chapter, Landsat satellite imagery was used to examine the mean normalized difference vegetation index (NDVI) of 75 visually selected peri-urban farmlands between 1986 and 2022. The mean NDVI of farmland changed significantly after 2005 as economic development policies forced the government to intervene in large-scale projects such as affordable housing. Based on the estimate by Trends.Earth, a free, open-source tool, grassland and cropland area decreased by 46% and 35%, respectively, while built-up areas grew by 297%. Between 2005 and 2015, land consumption rates (0.92%) exceeded population growth rates (0.035%), suggesting that the city has been expanding horizontally into peri-urban farmland. Increasing household land fragmentation and rising property values are exacerbating farmland loss, leading to food insecurity and a dependency on imports such as dairy derivatives. The loss of agricultural land also affects farmers who have no skills other than farming, are economically insecure, and are unemployed. These findings provide policymakers with additional insights into the ways in which urban agriculture can be incorporated into local, regional, and national urban development plans.

The gross domestic product (GDP) is probably the most important economic measurement worldwide. Although it is officially reported at the country level, the heterogeneities within the countries sparked interest in estimating the GDP at the city level. In this chapter, we introduce an innovative mixed method approach for the spatial disaggregation of the GDP at the intra-urban level. The method uses open data from satellite imagery, street networks, and distributed population counts, combined with local experts’ knowledge. The use of open datasets and local experts’ knowledge makes it possible to use the proposed method in cities with scarce data and small budgets. We use Medellin (Colombia) as a case study. We found that this approach performs better in those areas of the city associated with medium-to-low incomes, whereas the precision decreases in middle to high-income areas.

This chapter presents a concise synthesis of recent research efforts, emphasizing the combined use of ecosystem services and landscape metric concepts for quantifying provision, quality, and accessibility to ecosystem services as indicators of socio-ecological well-being in deprived urban areas in the Majority World. Such analyses are challenging due to the common lack of official and reliable data related to socioeconomic, demographic, ecological, and land use/land cover variables. The recommended analytical steps leverage freely available earth observation products with global coverage, requiring no proprietary software and enabling barrier-free application. Integration of readily available data sets is possible during image classification, post-processing, and ensuing spatio-ecological evaluation. The study highlights the importance of differentiating between ecosystem functions and services and separating land use from land cover to ensure accurate attributions. Additionally, incorporating spatial and temporal aspects, as well as considering beneficiaries, is essential for assessing ecosystem services. Local stakeholder and community interactions are advised to gain a comprehensive understanding of the local context. Future research should explore challenges associated with sustainable management of ecosystem service provision areas in densely populated informal settlements. This includes prioritizing specific services, developing tailored valuation approaches, and quantifying the influences of landscape configuration and composition. Addressing discrepancies between actual and intended land use remains critical for advancing the understanding of ecosystem services in urban environments. This approach underscores the importance of leveraging remote sensing data and fostering local stakeholder engagement for effective ecosystem service management in deprived urban areas.