Where Are All the Jobs ? : A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries
Globally, both people and economic activity are increasingly concentrated in urban areas. Yet, for the vast majority of developing country cities, little is known about the granular spatial organization of such activity despite its key importance t...
| Main Authors: | , , , |
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| Language: | English |
| Published: |
World Bank, Washington, DC
2022
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/660611647960970611/Where-Are-All-the-Jobs-A-Machine-Learning-Approach-for-High-Resolution-Urban-Employment-Prediction-in-Developing-Countries http://hdl.handle.net/10986/37195 |
| Summary: | Globally, both people and economic
activity are increasingly concentrated in urban areas. Yet,
for the vast majority of developing country cities, little
is known about the granular spatial organization of such
activity despite its key importance to policy and urban
planning. This paper adapts a machine learning based
algorithm to predict the spatial distribution of employment
using input data from open access sources such as Open
Street Map and Google Earth Engine. The algorithm is trained
on 14 test cities, ranging from Buenos Aires in Argentina to
Dakar in Senegal. A spatial adaptation of the random forest
algorithm is used to predict within-city cells in the 14
test cities with extremely high accuracy (R- squared greater
than 95 percent), and cells in out-of-sample ”unseen” cities
with high accuracy (mean R-squared of 63 percent). This
approach uses open data to produce high resolution estimates
of the distribution of urban employment for cities where
such information does not exist, making evidence-based
planning more accessible than ever before. |
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