Evaluating the Ability to Use Contextual Features to Map Deprived Areas 'Slums' in Multiple Cities

Population living in deprived conditions continues to grow, highlighting the urgent need for accurate high-resolution maps and detailed statistics to plan interventions and monitor changes. Unfortunately, data on deprived areas or "slums"is often unavailable, incomplete, or outdated. Leveraging satellite imagery can offer timely, and consistent information on deprived areas over large area However, there are limited studies that use free and open source data that can be used to map deprived areas over large areas and across multiple cities. To address these challenges, this study examines a scalable and transferable modeling approach to map deprived areas using contextual features extracted from freely available Sentinel-2 data. Models were trained and tested on three Sub-Sahara cities: Lagos Nigeria, Accra Ghana, and Nairobi, Kenya. The results indicate that models in individual city achieved F1 scores from 0.78-0.95 for the three cities. Additionally, the results indicate that the proposed approach may allow for the ability to transfer models from city to city allowing for large area and across city mapping.</p

Stuck in Slums: A Case Study of Slums in Islamabad, Pakistan

This paper focuses on finding answers to the reasons why people keep living in the slums and why they cannot get out of their precarious conditions. This paper looks into different reasons for people being stuck in slums from a religious perspective. Reasons for different religious groups being stuck in slums are not explored fully in the literature. The analysis draws on qualitative research with a sample of 53 semi-structured interviews conducted in 8 katchi abadis in Islamabad, Pakistan. The study shows that slums are nonhomogenous entities and are regarded as a living organism that provide safety, security, and a sense of belonging to some of the residents. The results revealed that both Christian and Muslim slum residents had different reasons for living in slums. There were not only inter-religious differences in the choice of living but intra religious differences had also been found. In the process, the paper highlights that most Christians lived in slums by choice due to strong social capital, with an exception of a few. On the other hand, Muslim slum residents lived in poverty which was a major reason most of the slum dwellers are stuck in slums. Policymakers should meet the needs of the people before implementing any policies. This is because relocation policies can bring misery to some of the slum dwellers. Finally, the paper demonstrated that slums play a pivotal role in the lives of the slum dwellers in keeping them

Towards a scalable and transferable approach to map deprived areas using Sentinel-2 images and machine learning

African cities are growing rapidly and more than half of their populations live in deprived areas. Local stakeholders urgently need accurate, granular, and routine maps to plan, upgrade, and monitor dynamic neighborhood-level changes. Satellite imagery provides a promising solution for consistent, accurate high-resolution maps globally. However, most studies use very high spatial resolution images, which often cover only small areas and are cost prohibitive. Additionally, model transferability to new cities remains uncertain. This study proposes a scalable and transferable approach to routinely map deprived areas using free, Sentinel-2 images. The models were trained and tested on three cities: Lagos (Nigeria), Accra (Ghana), and Nairobi (Kenya). Contextual features were extracted at 10 m spatial resolution and aggregated to a 100 m grid. Four machine learning algorithms were evaluated, including multi-layer perceptron (MLP), Random Forest, Logistic Regression, and Extreme Gradient Boosting (XGBoost). The scalability of model performance was examined using patches of the different deprived types identified through visual image interpretation. The study also tested the ability of models to map deprived areas of different types across cities. Results indicate that deprived areas have heterogeneous local characteristics that affect large area mapping. The top 25 features for each city show that models are sensitive to the spatial structures of deprived area types. While models performed well on individual cities with XGBoost and MLP achieving an F1 scores of over 80%, the generalized model proves to be more beneficial for modeling multiple cities. This approach offers a promising solution for scaling routine, accurate maps of deprived areas to hundreds of cities that currently lack any such map, supporting local stakeholders to plan, implement, and monitor geotargeted interventions

predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.Comment: 26 page

Applications of econometrics and machine learning to development and international economics

In the first chapter, I explore whether features derived from high resolution satellite images of Sri Lanka are able to predict poverty or income at local areas. I extract from satellite imagery area specific indicators of economic well-being including the number of cars, type and extent of crops, length and type of roads, roof extent and roof type, building height and number of buildings. Estimated models are able to explain between 60 to 65 percent of the village-specific variation in poverty and average level of log income. The second chapter investigates the effects of preferential trade programs such as the U.S. African Growth and Opportunity Act (AGOA) on the direction of African countries’ exports. While these programs intend to promote African exports, textbook models of trade suggest that such asymmetric tariff reductions could divert African exports from other destinations to the tariff reducing economy. I examine the import patterns of 177 countries and estimate the diversion effect using a triple-difference estimation strategy, which exploits time variation in the product and country coverage of AGOA. I find no evidence of systematic trade diversion within Africa, but do find evidence of diversion from other industrialized destinations, particularly for apparel products. In the third chapter I apply three model selection methods – Lasso regularized regression, Bayesian Model Averaging, and Extreme Bound Analysis -- to candidate variables in a gravity models of trade. I use a panel dataset of of 198 countries covering the years 1970 to 2000, and find model selection methods suggest many fewer variables are robust that those suggested by the null hypothesis rejection methodology from ordinary least squares

The Morphology of African Cities

This paper illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost. Document type: Boo

Quality of work life in Cartagena, Barranquilla, and Santa Marta

Recientemente, las principales ciudades del Caribe colombiano han alcanzado las menores cifras de desempleo en el país. Sin embargo, presentan altas tasas de informalidad. Así, este artículo tiene como objetivo analizar la situación de la informalidad y la calidad del empleo, en las tres principales ciudades de esta región colombiana. En 2007-2018, la informalidad decreció y las condiciones de vida laboral, analizadas a través del índice multidimensional de calidad del empleo (IMCE), son poco atractivas, en particular, para los ocupados con bajo nivel educativo, empleados domésticos, por cuenta propia y los trabajadores de empresas pequeñas.The main cities of the Colombian Caribbean have recently reached the lowest unemployment figures in the country. However, they exhibit the highest informality rates. The objective of this paper is to analyse the situation of informality and the quality of employment in the three main cities of the Colombian Caribbean. The informality was reduced between 2007-2018 and the conditions of work life, studied through the Multidimensional Index of Quality of Employment (IMCE), are unattractive and less favourable for those with low educational level employment, domestic employees, self-employed workers, or those who work for small companies

IDENTIFICATION OF POVERTY AREAS BY USING MACHINE LEARNING CLASSIFICATION METHODS FROM SATELLITE IMAGERY IN BURAYDAH CITY, IN THE QASSIM REGION OF SAUDI ARABIA

Saudi Arabia is a wealthy country with its many resources, but it has seen an increase in poverty recently because of a high rate of population growth with a high rate of unemployment. Some estimate that the number of Saudi Arabians living in poverty is between two and four million. This research aims to develop a way to detect poverty through remote sensing. The study area is Buraydah City, the largest city of the Qassim region, an important agricultural center that plays a significant role in the economy of Saudi Arabia. The research hypothesized that there are poor areas within Buraydah City and aimed to identify them through satellite imagery by using a Landsat 8 Operational Land Imager (OLI) and a high-resolution imagery from the French Système Pour l\u27Observation de la Terre (SPOT-6) both of 2019. Object-oriented segmentation and classification tools in a Geographic Information System (GIS) were used to distinguish features between poor and richer areas. Three classifiers were applied on the images, which were Maximum Likelihood (ML), Random Tree (RT), and Support Vector Machine (SVM). The best classifier was SVM on the SPOT image, with had an overall accuracy of 80.6% and a kappa coefficient of 0.79. The subsequent analysis of the correlation between classification-derived housing sizes and the poverty showed 2 value of 0.33 and 0.25 respectively, with the average income and national poverty rate. In conclusion, a map showing the areas of poverty was produced by GIS analysis categorizing the areas on the basis of average family income, family size, and the percentage of small houses as poverty-related factors. This research will help officials, charities, decision-makers, and planners to focus their development efforts on the areas of poverty. Moreover, the results will be supporting the new Vision 2030 of Saudi Arabia, which aims to improve the quality of life through upgrades to housing, healthcare, and educational opportunities. The research could be the basis for other future research studies and subsequent experiments, and should encourage researchers to take advantage of satellite images and spatial analysis techniques for other applications