Assessing the Utility of Satellite Imagery with Differing Spatial Resolutions for Deriving Proxy Measures of Slum Presence in Accra, Ghana

Remote Sensing of Urban Poverty and Gentrification

In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.

Using satellite imagery to understand and promote sustainable development

Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature that uses satellite imagery to understand these outcomes, with a focus on approaches that combine imagery with machine learning. We quantify the paucity of ground data on key human-related outcomes and the growing abundance and improving resolution (spatial, temporal, and spectral) of satellite imagery. We then review recent machine learning approaches to model-building in the context of scarce and noisy training data, highlighting how this noise often leads to incorrect assessment of model performance. We quantify recent model performance across multiple sustainable development domains, discuss research and policy applications, explore constraints to future progress, and highlight research directions for the field.

Simulating Scenarios of Future Intra-Urban Land-Use Expansion Based on the Neural Network–Markov Model: A Case Study of Lusaka, Zambia

Forecasting scenarios of future intra-urban land-use (intra-urban-LU) expansion can help to curb the historically unplanned urbanization in cities in sub-Saharan Africa (SSA) and promote urban sustainability. In this study, we applied the neural network–Markov model to simulate scenarios of future intra-urban-LU expansion in Lusaka city, Zambia. Data derived from remote sensing (RS) and geographic information system (GIS) techniques including urban-LU maps (from 2000, 2005, 2010, and 2015) and selected driver variables, were used to calibrate and validate the model. We then simulated urban-LU expansion for three scenarios (business as usual/status quo, environmental conservation and protection, and strategic urban planning) to explore alternatives for attaining urban sustainability by 2030. The results revealed that Lusaka had experienced rapid urban expansion dominated by informal settlements. Scenario analysis results suggest that a business-as-usual setup is perilous, as it signals an escalating problem of unplanned settlements. The environmental conservation and protection scenario is insufficient, as most of the green spaces and forests have been depleted. The strategic urban planning scenario has the potential for attaining urban sustainability, as it predicts sufficient control of unplanned settlement expansion and protection of green spaces and forests. The study proffers guidance for strategic policy directions and creating a planning vision.

Modelling the Wealth Index of Demographic and Health Surveys within Cities Using Very High-Resolution Remotely Sensed Information

A systematic and precise understanding of urban socio-economic spatial inequalities in developing regions is needed to address global sustainability goals. At the intra-urban scale, access to detailed databases (i.e., a census) is often a difficult exercise. Geolocated surveys such as the Demographic and Health Surveys (DHS) are a rich alternative source of such information but can be challenging to interpolate at such a fine scale due to their spatial displacement, survey design and the lack of very high-resolution (VHR) predictor variables in these regions. In this paper, we employ satellite-derived VHR land-use/land-cover (LULC) datasets and couple them with the DHS Wealth Index (WI), a robust household wealth indicator, in order to provide city-scale wealth maps. We undertake several modelling approaches using a random forest regressor as the underlying algorithm and predict in several geographic administrative scales. We validate against an exhaustive census database available for the city of Dakar, Senegal. Our results show that the WI was modelled to a satisfactory degree when compared against census data even at very fine resolutions. These findings might assist local authorities and stakeholders in rigorous evidence-based decision making and facilitate the allocation of resources towards the most disadvantaged populations. Good practices for further developments are discussed with the aim of upscaling these findings at the global scale.

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.

Urban Water Services in Fragile States: An Analysis of Drinking Water Sources and Quality in Port Harcourt, Nigeria, and Monrovia, Liberia

Establishing and maintaining public water services in fragile states is a significant development challenge. In anticipation of water infrastructure investments, this study compares drinking water sources and quality between Port Harcourt, Nigeria, and Monrovia, Liberia, two cities recovering from political and economic instability. In both cities, access to piped water is low, and residents rely on a range of other private and public water sources. In Port Harcourt, geographic points for sampling were randomly selected and stratified by population density, whereas in Monrovia, locations for sampling were selected from a current inventory of public water sources. In Port Harcourt, the sampling frame demonstrated extensive reliance on private boreholes and a preference, in both planned and unplanned settlements, for drinking bottled and sachet water. In Monrovia, sample collection focused on public sources (predominantly shallow dug wells). In Port Harcourt, fecal indicator bacteria (FIB) were detected in 25% of sources (N = 566), though concentrations were low. In Monrovia, 57% of sources contained FIB and 22% of sources had nitrate levels that exceeded standards (N = 204). In Monrovia, the convenience of piped water may promote acceptance of the associated water tariffs. However, in Port Harcourt, the high prevalence of self-supply and bottled and sachet drinking water suggests that the consumer's willingness to pay for ongoing municipal water supply improvements may be determined by service reliability and perceptions of water quality.

Estimating spatial inequalities of urban child mortality

BACKGROUND

Recent studies indicate that the traditional rural-urban dichotomy pointing to cities as places of better health in the developing world can be complicated by poverty differentials. Knowledge of spatial patterns is essential to understanding the processes that link individual demographic outcomes to characteristics of a place. A significant limitation, however, is the lack of spatial data and methods that offer flexibility in data inputs.

OBJECTIVE

This paper tackles some of the issues in calculating intra-urban child mortality by combining multiple data sets in Accra, Ghana and applying a new method developed by Rajaratnam et al. (2010) that efficiently uses summary birth histories for creating local-level measures of under-five child mortality (5q0). Intra-urban 5q0 rates are then compared with characteristics of the environment that may be linked to child mortality.

METHODS

Rates of child mortality are calculated for 16 urban zones within Accra for birth cohorts from 1987 to 2006. Estimates are compared to calculated 5q0 rates from full birth histories. 5q0 estimates are then related to zone measures of slum characteristics, housing quality, health facilities, and vegetation using a simple trendline R² analysis.

RESULTS

Results suggest the potential value of the Rajaratnam et al. method at the micro-spatial scale. Estimated rates indicate that there is variability in child mortality between zones, with a spread of up to 50 deaths per 1,000 births. Furthermore, there is evidence that child mortality is connected to environmental factors such as housing quality, slum-like conditions, and neighborhood levels of vegetation.

Connecting the Dots Between Health, Poverty and Place in Accra, Ghana

West Africa has a rapidly growing population, an increasing fraction of which lives in urban informal settlements characterized by inadequate infrastructure and relatively high health risks. Little is known, however, about the spatial or health characteristics of cities in this region or about the spatial inequalities in health within them. In this article we show how we have been creating a data-rich field laboratory in Accra, Ghana, to connect the dots between health, poverty, and place in a large city in West Africa. Our overarching goal is to test the hypothesis that satellite imagery, in combination with census and limited survey data, such as that found in demographic and health surveys (DHSs), can provide clues to the spatial distribution of health inequalities in cities where fewer data exist than those we have collected for Accra. To this end, we have created the first digital boundary file of the city, obtained high spatial resolution satellite imagery for two dates, collected data from a longitudinal panel of 3,200 women spatially distributed throughout Accra, and obtained microlevel data from the census. We have also acquired water, sewerage, and elevation layers and then coupled all of these data with extensive field research on the neighborhood structure of Accra. We show that the proportional abundance of vegetation in a neighborhood serves as a key indicator of local levels of health and well-being and that local perceptions of health risk are not always consistent with objective measures.

Do the Most Vulnerable People Live in the Worst Slums? A Spatial Analysis of Accra, Ghana

Slums are examples of localized communities within third world urban systems representing a range of vulnerabilities and adaptive capacities. This study examines vulnerability in relation to flooding, environmental degradation, social-status, demographics, and health in the slums of Accra, Ghana by utilizing a place-based approach informed by fieldwork, remote sensing, census data, and geographically weighted regression. The study objectives are threefold: (1) to move slums from a dichotomous into a continuous classification and examine the spatial patterns of the gradient, (2) develop measures of vulnerability for a developing world city and model the relationship between slums and vulnerability, and (3) to assess if the most vulnerable individuals live in the worst slums. A previously developed slum index is utilized, and four new measures of vulnerability are developed through principle components analysis, including a novel component of health vulnerability based on child mortality. Visualizations of the vulnerability measures assess spatial patterns of vulnerability in Accra. Ordinary least squares, spatial, and geographically weighted regression model the ability of the slum index to predict the four vulnerability measures. The slum index performs well for three of the four vulnerability measures, but is least able to predict health vulnerability underscoring the complex relationship between slums and child mortality in Accra. Finally, quintile analysis demonstrates the elevated prevalence of high vulnerability in places with high slum index scores.

When urban taps run dry: sachet water consumption and health effects in low income neighborhoods of Accra, Ghana

Intraurban differentials in safe drinking water in developing cities have been exacerbated by rapid population growth that exceeds expansion of local water infrastructure. In Accra, Ghana, municipal water is rationed to meet demand, and the gap in water services is increasingly being filled by private water vendors selling packaged "sachet" water. Sachets extend drinking water coverage deeper into low-income areas and alleviate the need for safe water storage, potentially introducing a health benefit over stored tap water. We explore correlates of using sachets as the primary drinking water source for 2093 women in 37 census areas classified as slums by UN-Habitat, and links between sachet water and reported diarrhea episodes in a subset of 810 children under five. We find that neighborhood rationing exerts a strong effect on a household's likelihood of buying sachet water, and that sachet customers tend to be the poorest of the poor. Sachet use is also associated with higher levels of self-reported overall health in women, and lower likelihood of diarrhea in children. We conclude with implications for sachet regulation in Accra and other sub-Saharan cities facing drinking water shortages.