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 made 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 added along 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- 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 should 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 such 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 International 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 opinion 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- 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 Paper 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. 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 reveals significant spatial heterogeneity, with em- About the authors Yan Jin, Associate Professor, School of Internet of Things, Nanjing University of Posts andTelecommunications & Smart Health Big Data Analysis and Location Services Engineering Lab Matthieu Charpe, Senior Economist, Employment Policy Department, International LabourOrganization, 4 route des Morillons, 1211 Geneva, Switzerland Yang Mei, School of Internet of Things, Nanjing University of Posts and Telecommunications,Nanjing 210023, China Zeshuo Li, School of Internet of Things, Nanjing University of Posts and Telecommunications,Nanjing 210023, China Table of contents List of Figures Figure 1: Selected Labour Market IndicatorsFigure 2: Methodology for gridded-labour market estimationFigure 3: Spatial distribution of (selected) employment categoriesFigure 4: Actual vs estimated statistics -