Droughts are increasingly cited as a driver of urbanization across Sub-Saharan Africa, yet little is known about the role they play in shaping the spatial expansion of cities. Combining satellite imagery on built-up areas with climatic data for the period 1984-2015, this study empirically examines whether and to what extent droughts influences the spatial expansion of African cities. It further investigates the heterogeneity of these effects across cities and countries. Findings indicate that extreme droughts significantly accelerate the built-up growth rate of cities, while more frequent but less severe droughts have negligible impacts. Importantly, these effects are strongly differentiated across cities. The 1% most extreme droughts boost by 75% the average speed at which new settlements emerge in the surroundings of a country's largest city, yet they do not alter the expansion of other smaller cities and towns. These drought-induced effects intensify the sprawl of the largest cities and bear important policy implications: extreme droughts put additional pressure on the largest and often overcrowded cities, potentially deepening congestion effects; they also contribute to exacerbate the speed at which cities expand in flood prone areas, thereby magnifying urban flood risk. As climate changes, the frequency of both extreme droughts and extreme rainfall events is projected to increase across the region, aggravating the likelihood of future drought-induced expansions of the largest cities and worsening urban flood risk prospects. These findings call for urgent and tailored risk reduction measures in both cities and rural areas.
Repository name | URI |
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Reproducible Research Repository (World Bank) | https://reproducibility.worldbank.org |
Paper exhibits were reproduced on a computer with the following specifications:
• OS: Windows 10 Enterprise, version 22H2
• Processor: Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz 2.60 GHz (2 processors)
• Memory available: 128 GB
• Software version: Python 3.11, R 4.3.2
~ 3 hours
To successfully reproduce the analysis, follow these steps:
environment.yml
file1. dataset_creation.ipynb
located in the root directory. Update the directory path in cell 2.2. Regressions.R
after updating the directory.3. figures_1_3_5_6_7.R
4. figures_2_4.ipynb
5. Main plots.R
All data sources are publicly available and included in the reproducibility package.
Author | Affiliation | |
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Rafael Van der Borght | World Bank | rvanderborght@worldbank.org |
Oscar A. Ishizawa | World Bank | oishizawa@worldbank.org |
Jean Thuret | jeanthuret34@gmail.com | |
Joaquin Muñoz | World Bank | jmunozdiaz@worldbank.org |
2025-02
Location | Code |
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Sub-Saharan Africa | SSA |
The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.
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Modified BSD3 | https://opensource.org/license/bsd-3-clause/ |
Name | Affiliation | |
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Joaquin Munoz Diaz | World Bank | jmunozdiaz@worldbank.org |
Reproducibility WBG | World Bank | reproducibility@worldbank.org |
Name | Abbreviation | Affiliation | Role |
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Reproducibility WBG | DIME | World Bank - Development Impact Department | Verification and preparation of metadata |
2025-02-06
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