Submissions Accepted for Presentation at the World Bank Land Conference 2024

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Session Overview
01-01: Using new spatial data to assess land use & household welfare
Tuesday, 14/May/2024:
8:00am - 10:00am

Session Chair: Olivier Dupriez, World Bank, United States of America
Location: MC 13-121

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Big data from space for informed land management: towards a global 4D monitoring of the built environment

Thomas Esch1,2, Edward Charles Anderson3, Klaus Deininger3, Rashmin Gunasekera3, Remi Jedwab4,5, Mattia Marconcini1, Daniela Palacios-Lopez1, Hogeun Park3, Julian Zeidler1

1German Aerospace Center (DLR), Germany; 2Stuttgart University of Applied Sciences, Germany; 3World Bank, USA; 4George Washington University, USA; 5New York University, USA

The worldwide expansion of human settlements is a major driver of global change and in particular the sprawl of cities poses great challenges. Within just a few years, megacities are sprouting up, urban agglomerations sprawl into hazard zones and entire landscapes are fragmented and irretrievably transformed. But how can the opportunities offered by prospering settlements be put to good use? And how can risk exposure and negative environmental and societal impacts be mitigated? These are key challenges for land governance. And it is here where satellite-based earth observation and machine learning can make important contributions. This article presents the World Settlement Footprint (WSF), digital maps derived from "Big Earth Data", as a valuable tool for information gathering and decision support. The information and knowledge provided by the WSF can help planning, science, policy and business to enable sustainable and informed governance practices and land use strategies.


An anatomy of urbanization in Sub-Saharan Africa

Pierre-Philippe Combes2, Clement Gorin3, Shohei Nakamura1, Mark Roberts1, Benjamin Stewart1

1The World Bank; 2Sciences Po; 3University of Paris 1

This paper provides a detailed descriptive analysis of patterns of urbanization across Sub-Saharan Africa. Despite the rapidity of Sub-Saharan Africa's urbanization, little is known about the anatomy of patterns of urbanization across the region due to a lack of detailed and accurate official data on urban populations. To address this gap, this paper applies the “dartboard” algorithm to high-resolution gridded population data for the region, which is derived from digitized maps of the footprints of all buildings in the region. This allows for a consistent definition of urban areas across countries, overcoming the measurement problems that arise from relying on official definitions of urban areas. Using this definition, the paper presents evidence on key empirical regularities that relate to disparities across the urban hierarchies, as well as on the internal structures of cities. Analysis of how the derived urbanization characteristics relate to country characteristics is also presented.


Where Is poverty concentrated? New evidence based on internationally consistent urban and poverty measurements

Shohei Nakamura, Pierre-Philippe Combes, Robin Moellerherm, Charlotte Robert, Mark Roberts, Benjamin Stewart, Slava Yakubenko

World Bank, United States of America

The lack of comparable urban definitions across countries has presented a significant challenge in effectively addressing poverty in both urban and rural areas. This study aims to tackle this issue by comparing subnational poverty statistics across countries, integrating internationally consistent definitions of urban areas into the World Bank’s official global poverty measurement framework. Focusing primarily on 16 Sub-Saharan African countries, the analysis reveals that poverty rates tend to be lower in densely populated urban areas. However, the findings also highlight that urban areas have a higher concentration of impoverished populations than previously estimated. These results underscore the importance of employing consistent urban definitions in cross-country poverty analysis and call for a reevaluation of geographically targeted policies to expedite poverty reduction efforts.


Estimating household-level economic characteristics from high-resolution satellite imagery

Satej Soman1,2, Susana Constenla-Villoslada1,2, Emily Aiken1,2, Joshua E. Blumenstock1,2,3

1School of Information, University of California, Berkeley; 2Global Policy Lab, University of California, Berkeley; 3Goldman School of Public Policy, University of California, Berkeley

Understanding economic development, land rights and management, and structural transformation requires accurate and high-resolution measurements of poverty and growth. Fine-grained estimates of living standards are also critical to effectively target policies and evaluate development interventions. However, most low-income countries lack the resources and administrative capacity to regularly collect household-level socioeconomic information. Research in the past few years has developed methods for poverty measurement at the village, neighborhood, and satellite tile level based on machine learning (ML) and satellite imagery. This project advances the literature by producing and evaluating the first national-scale satellite-based poverty estimates at the household level, using two large field surveys in Togo and Bangladesh. We also provide guidance to policymakers and practitioners on which aspects of living standards can be reliably measured from satellite imagery, as well as the cost-benefit tradeoffs in choosing appropriate machine learning techniques for such prediction tasks.


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