Main Objective: Operate through the JRC’s Africa Knowledge Platform and serve as an integration engine, connecting thematic sectors and aligning the contributions of the RCoEs around a unified and coherent vision.
Specific Objectives:
Ensure effective coordination of the ArcX programme through a robust and well-managed transversal mechanism;
Enhance the science–policy interface by providing targeted support services that facilitate evidence-based decision-making;
Strengthen knowledge circulation and collective learning across ArcX through an efficient and interoperable knowledge management mechanism;
Promote inter-sectoral networking and partnership opportunities by delivering tailored support services that connect actors across thematic and regional domains.
Starting Year: 2023 Implementation Duration: 6 years
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
The GHS-BUILT-V R2023A dataset depicts the distribution of built-up volumes, expressed as number of cubic meters. This data reports about the total built-up volume. Data are spatial-temporal interpola...
Protected areas in Africa play a critical role in biodiversity conservation, supporting livelihoods, and mitigating climate change. This tool offers a comprehensive analysis of the dynami...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
The GHS Settlement Model layers (GHS-SMOD) GHS-SMOD_GLOBE_R2023A delineate and classify settlement typologies via a logic of cell clusters population size, population and built-up area densities as de...
