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Where Are All the Jobs ?: A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries

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dc.contributor.authorBarzin, Samira
dc.contributor.authorRentschler, Jun
dc.contributor.authorO’Clery, Neave
dc.contributor.authorAvner, Paolo
dc.date.accessioned2022-03-23T14:48:55Z
dc.date.available2022-03-23T14:48:55Z
dc.date.issued2022-03
dc.description.abstractGlobally, 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.en
dc.identifierhttp://documents.worldbank.org/curated/en/660611647960970611/Where-Are-All-the-Jobs-A-Machine-Learning-Approach-for-High-Resolution-Urban-Employment-Prediction-in-Developing-Countries
dc.identifier.doi10.1596/1813-9450-9979
dc.identifier.urihttps://hdl.handle.net/10986/37195
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Paper;9979
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo
dc.subjectDEVELOPMENT ECONOMICS
dc.subjectURBAN ECONOMICS
dc.subjectCITIES
dc.subjectFIRM LOCATIONS
dc.subjectBIG DATA
dc.subjectSATELITE DATA
dc.subjectCOMPUTATIONAL METHODS
dc.subjectMACHINE LEARNING
dc.titleWhere Are All the Jobs ?en
dc.title.subtitleA Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countriesen
dc.typeWorking Paperen
dc.typeDocument de travailfr
dc.typeDocumento de trabajoes
dspace.entity.typePublication
okr.crossref.titleWhere are All the Jobs? A Machine Learning Approach for High Resolution Urban Employment Prediction in Developing Countries
okr.date.disclosure2022-03-22
okr.date.doiregistration2025-04-10T10:37:39.071932Z
okr.date.lastmodified2022-03-22T00:00:00Zen
okr.doctypePolicy Research Working Paper
okr.doctypePublications & Research
okr.docurlhttp://documents.worldbank.org/curated/en/660611647960970611/Where-Are-All-the-Jobs-A-Machine-Learning-Approach-for-High-Resolution-Urban-Employment-Prediction-in-Developing-Countries
okr.guid660611647960970611
okr.identifier.doi10.1596/1813-9450-9979
okr.identifier.externaldocumentum090224b088d3cdd7_1_0
okr.identifier.internaldocumentum33763828
okr.identifier.reportWPS9979
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/660611647960970611/pdf/Where-Are-All-the-Jobs-A-Machine-Learning-Approach-for-High-Resolution-Urban-Employment-Prediction-in-Developing-Countries.pdfen
okr.topicPoverty Reduction::Employment and Shared Growth
okr.topicPoverty Reduction::Small Area Estimation Poverty Mapping
okr.topicCommunities and Human Settlements::Urban Communities
okr.topicUrban Development::Urban Economic Development
okr.topicUrban Development::Urban Environment
okr.unitGGSVP Chief Economist (GGSCE)
relation.isAuthorOfPublication30005afb-5036-51b6-b3fb-626d539c298a
relation.isAuthorOfPublicationaba5095c-0b26-4b6b-a711-2f6efb69a279
relation.isAuthorOfPublication.latestForDiscovery30005afb-5036-51b6-b3fb-626d539c298a
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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