Where Are All the Jobs ? A Machine Learning Approach for High Resolution Urban Employment Prediction inDeveloping 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 to policy and urbanplanning. This paper adapts a machine learning based algorithm to predict the spatial distribution of employmentusing input data from open access sources such as Open Street Map and Google Earth Engine. The algorithm is trainedon 14 test cities, ranging from Buenos Aires in Argentina to Dakar in Senegal. A spatial adaptation of the random forestalgorithm is used to predict within-city cells in the 14 test cities with extremely high accuracy (R- squared greaterthan 95 percent), and cells in out-of-sample ”unseen” cities with high accuracy (mean R-squared of 63 percent). Thisapproach uses open data to produce high resolution estimates of the distribution of urban employment for cities wheresuch information does not exist, making evidence-based planning more accessible than ever before.