Nigeria: Using data science to tackle land degradation 

Abubakar Isa-Abubakar, Africa CAN Data Science Fellow, cohort one

Africa CAN Fellow Abubakar Isa-Abubakar worked with the Nigeria Federal Government, in collaboration with GEO-LDN, to use data science technologies to tackle land degradation in Amba Community, Nasarawa State.

The problem 

The Amba Community has been experiencing severe land degradation fueled by factors such as deforestation, soil erosion, unsustainable agricultural practices, and climate change. The lack of comprehensive data on the specific causes and extent of land degradation in Amba Community hindered the government’s ability to assess the situation, develop effective land restoration strategies, and make informed decisions to build resilience and protect livelihoods. 

The approach

Abubakar was integrated into the Nigeria GEO-LDN Global Dialog Forum intersectoral team (including representatives from the Federal Ministry of Environment, the National Space Research and Development Agency, the International Federation of Red Cross and Red Crescent Societies, and the University of Calabar) as a data science technical support. The support he provided was instrumental in several key areas, including creating a concept note, which helped to secure further funding for the project; developing innovative new tools to monitor and assess land degradation across Nigeria; and training local stakeholders on using advanced geospatial tools.

Challenges and lessons

The project experienced initial data gaps, and local stakeholders had limited capacity in data collection and analysis. Addressing these challenges provided valuable lessons on the role of partnerships and capacity development in managing environmental problems. 

Outcomes and results 

By addressing the challenges of fragmented data and the limited infrastructure for systematic collection and analysis of land degradation data in the region, the project resulted in a number of positive outcomes, including:

• A comprehensive assessment of land degradation in Amba Community.
• Innovative tools for tracking land degradation, like the Nigeria Decision Support System app and a new data collection tool. The latter will become part of the GEO-LDN toolbox, where it can be used by other countries and intersectoral teams. 
• Increased data access for policy, practice, and funding allocation for addressing land degradation. 
• Enhanced capacity training for local stakeholders.
• A strategic framework to address land degradation, using data science technologies to map and analyze the state of the land in the target area.

• Read the full case study here.

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Kenya: Using better data to transform carbon emissions monitoring in Kenya 

Agnes Okero, Africa CAN Data Science Fellow, cohort one

The Kenya National Bureau of Statistics engaged Agnes Okero to address the critical challenge of accurately monitoring carbon emissions in Kenya’s road transportation sector.

The problem

The road transport sector in Kenya contributes significantly to greenhouse gas emissions. However, there was a critical gap in reliable data and methodologies for estimating carbon emissions from this sector, hindering the ability to make sound policy decisions. 

The approach

The project aimed to empower stakeholders with the skills and tools needed to effectively manage carbon emissions. Okero worked with multiple stakeholders to broker data-sharing agreements, bringing together different data sources to create a more comprehensive dataset. This included integrating advanced analytics and technologies like machine learning and AI, and combining transport data with information from other sectors to uncover previously hidden insights.

Challenges and lessons 

The project faced a number of challenges, including engaging stakeholders with various interests; navigating concerns around data sharing; negotiating clear, ethical data-sharing agreements; and integrating different data sources in diverse formats while ensuring the quality and reliability of the data.

Outcomes and results

The initiative has significantly improved carbon emissions management within Kenya’s road transport sector. Through a new, standardized emissions model and user-friendly interface, stakeholders can simply input the transport sector data (such as vehicle counts, fuel consumption, and traffic patterns) into the system to obtain accurate carbon emission estimates.

The standardized model developed is already being used by key agencies in Kenya to monitor emissions on the country’s roads and offers a blueprint that other nations can follow. 

• Read the full case study here.

Meet Cohort One

October 2023 to April 2024

• Name: Abubakar Isa-Abubakar
  Location: Nigeria
  Project: Using data science to tackle land degradation and build resilience in Amba Community, Nasarawa, Nigeria.
  Working with: Nigeria Federal Government, in collaboration with GEO-LDN
• Name: Agnes Okero
  Location: Kenya
  Project: Using better data to transform carbon emissions monitoring and inform environmental policy in Kenya.
  Working with: Kenya National Bureau of Statistics 
• Name: Ansoumane Traore
  Location: Guinea
  Project: Developing a WebGIS platform to combat diarrheal diseases in Guinea.
  Working with: Guinean Ministry of Environment and the National Center for Environmental Monitoring and Observation 
• Name: Beni Nzimba Makumbu
  Location: Democratic Republic of the Congo
  Project: Mapping and analysis of land degradation in Lubumbashi, Democratic Republic of the Congo.
  Working with: Ministry of Environment and Sustainable Development of the Democratic Republic of the Congo 
• Name: Brahima Diarra
  Location: Mali
  Project: Analyzing the impact of climate change on malnutrition in children under age five in Mali.
  Working with: Institut National de la Statistique in Mali
• Name: Clement Danso
  Location: Ghana
  Project: Building a digital foundation to transform Ghana’s land use planning applications and accelerate development projects.
  Working with: Land Use and Spatial Planning Authority in Ghana
• Name: Ezekiel Adebayo Ogundepo
  Location: Nigeria
  Project: Using machine learning to model the drivers of cholera and address recurring outbreaks in Yobe State, Nigeria.
  Working with: eHealth Africa

Guinea: Mapping health access to inform climate-resilient healthcare in Guinea

Aboubacar Diallo, Africa CAN Data Science Fellow, cohort two

The Ministry of Health in Guinea engaged Aboubacar Diallo to improve equitable healthcare access through national healthcare facility mapping and spatial accessibility analysis.

The problem

Guinea faces persistent health inequities, especially in rural areas where infrastructure is sparse and climate-related risks like flooding and extreme weather limit access. The Ministry of Health lacked up-to-date geospatial data on healthcare facilities, with many health posts and private clinics missing from national databases. There was also no system to model facility accessibility or climate vulnerability, hindering evidence-based health planning.

The approach

Diallo worked closely with the Ministry’s Bureau de Stratégie et Développement to georeference over 2,300 health facilities and conduct a nationwide analysis using population distribution and climate risk data. He developed a spatial optimization model to identify underserved areas and propose optimal facility placement, and delivered training sessions to strengthen institutional capacity in geospatial tools.

Challenges and lessons

The project faced data access constraints, particularly regarding road networks and climate vulnerability layers. Collaboration across ministries, especially with environmental agencies, was slower than anticipated. Despite these challenges, Diallo established a foundation for integrating climate data into health system planning and highlighted the value of community engagement and mobile clinics in hard-to-reach areas.

Outcomes and results

The project provided the Ministry of Health with an updated national map of healthcare facilities, detailed accessibility models, and recommendations for 20 priority districts needing new health investments. This data-driven approach informed government planning processes and sparked greater interest in climate-resilient service delivery. It also provided a replicable model for other countries working toward universal health coverage.

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Kenya: Linking climate change to cancer risks for smarter health responses

Florence Nguuni, Africa CAN Data Science Fellow, cohort two

CAN Africa supported Florence Nguuni to investigate the connection between climate change and rising cancer risks in Kenya.

The problem

Cancer is the third leading cause of death in Kenya, yet the role of environmental factors such as air pollution, ultraviolet radiation, and contaminated water in cancer incidence remains under-researched. Policy frameworks seldom address these links, and rural communities often lack access to preventive screening and risk information.

The approach

Nguuni combined data from national cancer registries and environmental indicators with qualitative insights from interviews with around 20 experts. She created Power BI dashboards to visualize correlations between climate variables and cancer types, developed community toolkits for high-risk counties, and drafted a policy brief outlining priority actions for climate-resilient cancer prevention.

Challenges and lessons

Limited access to structured climate-health datasets and stakeholder scheduling issues slowed down some aspects of the work. Florence found that integrating health and environmental data requires not only technical capacity but sustained cross-sectoral collaboration. Ensuring long-term policy impact also will require better advocacy pathways and institutional ownership of tools developed.

Outcomes and results

Nguuni’s work helped spotlight the growing evidence that climate change is increasing cancer risks in Kenya. Her policy brief was endorsed by the Ministry of Health and National Environment Management Agency, and her community awareness materials are being scaled by Amref Health Africa and other partners. By piloting new tools and approaches, Florence laid the groundwork for future policy reforms and community-level interventions across East Africa.

Meet Cohort Two

January 2024 to May 2025

• Name: Elishama Yomi-Agbajor
  Location: Nigeria
  Project: Leveraging machine learning to analyze the impact of climate factors on mortality rates in sickle cell anemia patients. 
  Working with: High Performance and Intelligence Computing research group at the University of Nigeria, Nsukka
• Name: Enock Mwesigwa 
  Location: Uganda
  Project: Building machine learning models for flood and disease mapping in the Greater Kampala Metropolitan Area.
  Working with: Uganda Bureau of Statistics 
• Name: Andrew Karanja 
  Location: Kenya
  Project: Integrating aquaculture and farming systems to address food security and malnutrition in rural Kenya.
  Working with: Integrated Multi Trophic Aquaculture – Health Initiative
• Name: Yibeltal Assefa 
  Location: Ethiopia
  Project: Developing predictive models for vector-borne diseases, focusing on malaria prevalence influenced by climate change in Africa.
  Working with: Various organizations
• Name: Florence Nguuni 
  Location: Kenya
  Project: Exploring the relationship between climate change and cancer risks in Kenya and developing evidence-based strategies for prevention and resilience.
  Working with: Various organizations 
• Name: Mohamed Gele 
  Location: Somalia
  Project: Modeling the complex relationship between climate change, food security, and public health in Somalia, providing critical insights to inform policy and resilience strategies.
  Working with: Various organizations
• Name: Peace Ododo 
  Location: Nigeria
  Project: Assessing the impact of climate change on food security and malnutrition in Kebbi State, Nigeria, employing GIS and machine learning to design localized interventions.
  Working with: Various organizations
• Name: Steven Bowah 
  Location: Malawi
  Project: Investigating the impact of flooding on blood transport networks in Northern Malawi, proposing adaptive strategies to maintain healthcare access during climate events.
  Working with: Various organizations
• Name: Aboubacar Diallo
  Location: Guinea
  Project: Enhancing healthcare access in rural Guinea by mapping facilities and analyzing climate impacts on health systems.
  Working with: Ministry of Health, Guinea
• Name: Dan Poku
  Location: Ghana
  Project: Using climate and health datasets to develop a cloud-native machine-learning application to predict respiratory health outcomes in Ghana, aiming to support early-warning public health outreach and inform policy decisions. 
  Working with: Various organizations

Meet cohort three

May 2025 to October 2025

• Name: Berhe Aregawi Gidey
  Location: Ethiopia
  Project: Using climate-driven predictive modeling to identify child malnutrition risks in Tigray.
  Working with: Mekelle University, College of Health Sciences, Ayder Referral Hospital, Ethiopia
• Name: Linda Arthur 
  Location: Ghana
  Project: Mapping and analyzing data to understand the impact of landfill air pollutants and extreme weather on the respiratory health of local communities in Ghana.
  Working with: Accra School of Hygiene, Ghana 
• Name: Gouvidé Jean Gbaguidi 
  Location: Benin
  Project: Guiding malaria elimination interventions through a data-driven approach to resource optimization in Benin.
  Working with: National University of Sciences, Technologies, Engineering and Mathematics of Aborney, Benin
• Name: Israel Odeajo 
  Location: Nigeria
  Project: Modeling the effects of climate variability on the epidemiology of infectious diseases, using insights from outbreaks of cholera, lassa fever, malaria, and typhoid fever in Benue State. 
  Working with: Benue State Ministry of Health, Makurdi, Nigeria
• Name: Racheal Ogbozor
  Location: Nigeria
  Project: Evaluating the effectiveness of climate-adaptive malaria reduction and control interventions in Nigeria.Working with: Auricle Services