利用遥感和随机森林对加纳劳动力市场数据进行网格化

Authors / Jin Yan, Charpe Matthieu, Mei Yang, Li Zeshuo © International Labour Organization 2026 Attribution 4.0 International (CC BY 4.0) This work is licensed under the Creative Commons Attribution 4.0 International. See:https://creativecommons.org/licenses/by/4.0/. The user is allowed to reuse, share (copy and redistrib-ute), adapt (remix, transform and build upon the original work) as detailed in the licence. Theuser must clearly credit the ILO as the source of the material and indicate if changes were madeto the original content. Use of the emblem, name and logo of the ILO is not permitted in con-nection with translations, adaptations or other derivative works. Attribution– The user must indicate if changes were made and must cite the work as follows:Yan, J., Matthieu, C., Yang, M., Zeshuo, L.Gridded-Labour Market Data in Ghana using Remote Sensingand Random Forest. ILO Working Paper 165. Geneva: International Labour Office, 2026.© ILO. Translations– In case of a translation of this work, the following disclaimer must be addedalong with the attribution:This is a translation of a copyrighted work of the International LabourOrganization (ILO). This translation has not been prepared, reviewed or endorsed by the ILO and shouldnot be considered an official ILO translation. The ILO disclaims all responsibility for its content and ac-curacy. Responsibility rests solely with the author(s) of the translation. Adaptations– In case of an adaptation of this work, the following disclaimer must be addedalong with the attribution:This is an adaptation of a copyrighted work of the International LabourOrganization (ILO). This adaptation has not been prepared, reviewed or endorsed by the ILO and shouldnot be considered an official ILO adaptation. The ILO disclaims all responsibility for its content and ac-curacy. Responsibility rests solely with the author(s) of the adaptation. Third-party materials– This Creative Commons licence does not apply to non-ILO copyright ma-terials included in this publication. If the material is attributed to a third party, the user of suchmaterial is solely responsible for clearing the rights with the rights holder and for any claims ofinfringement. Any dispute arising under this licence that cannot be settled amicably shall be referred to arbitra-tion in accordance with the Arbitration Rules of the United Nations Commission on InternationalTrade Law (UNCITRAL). The parties shall be bound by any arbitration award rendered as a resultof such arbitration as the final adjudication of such a dispute. For details on rights and licensing, contact:rights@ilo.org. For details on ILO publications anddigital products, visit:www.ilo.org/publns. ISBN 9789220432976 (print), ISBN 9789220432983 (web PDF), ISBN 9789220432990 (epub), ISBN9789220433003 (html). ISSN 2708-3438 (print), ISSN 2708-3446 (digital) https://doi.org/10.54394/00033744 The designations employed in ILO publications, which are in conformity with United Nationspractice, and the presentation of material therein do not imply the expression of any opinionwhatsoever on the part of the ILO concerning the legal status of any country, area or territory or of its authorities, or concerning the delimitation of its frontiers or boundaries. See:www.ilo.org/disclaimer. The opinions and views expressed in this publication are those of the author(s) and do not nec-essarily reflect the opinions, views or policies of the ILO. Reference to names of firms and commercial products and processes does not imply their en-dorsement by the ILO, and any failure to mention a particular firm, commercial product or pro-cess is not a sign of disapproval. Information on ILO publications and digital products can be found at:www.ilo.org/research-and-publications ILO Working Papers summarize the results of ILO research in progress, and seek to stimulatediscussion of a range of issues related to the world of work. Comments on this ILO Working Paperare welcome and can be sent tocharpe@ilo.org. Authorization for publication: Schmidt, Dorothea ILO Working Papers can be found at:www.ilo.org/research-and-publications/working-papers Suggested citation: Yan, J., Matthieu, C., Yang, M., Zeshuo, L. 2026.Gridded-Labour Market Data in Ghana us-ing Remote Sensing and Random Forest, ILO Working Paper 165 (Geneva, ILO).https://doi.org/10.54394/00033744 Abstract This study presents high-resolution (0.005) gridded labor market data, generated by downscal-ing district-level census data for Ghana using random forest algorithms and remote sensing. Itaddresses the lack of spatially disaggregated labor market data by mapping 17 employment cat-egories – including age, gender, skills, status, sectors, unemployment, and NEET. Auxiliary data(64 variables) such as land cover, nighttime lights, infrastructure, and points of interest are inte-grated to capture demographic, economic, and participation factors. The model achieves highaccuracy (R2 > 90% for most categories) and re