Publications

Supported by Gavi through its INFUSE initiative, GRID3 Mapping for Health in the Democratic Republic of the Congo (DRC) is a Ministry of Health initiative, delivered in partnership with Flowminder and the Center for International Earth Science Information Network at […]

Learn how to use geospatial data to visualise the locations, distribution, and characteristics of vulnerable populations who receive basic health services. These types of visualisations can support efforts to allocate resources and plan outreach activities for COVID-19 vaccination campaigns. They can also be used to inform planning for other health service delivery.

In this guide, we provide instructions on how to use GRID3 maps for COVID-19 vaccination microplanning. We also highlight how comprehensive data on settlement locations, health facilities, boundaries, and target populations (disaggregated by age and travel distance to health facilities) can be used to help answer important questions during planning of a health campaign.

How far does one need to travel to reach a vaccination site? What could be done to improve access to this service? Can we upgrade existing facilities to improve access to vaccination or do we need to create new fixed or mobile sites in specific areas? Answering such questions is essential to successful immunisation interventions, particularly in the context of the COVID-19 pandemic and governmental efforts to roll out effective vaccination campaigns. 

As part of the GRID3 Mapping for Health project, Flowminder has released a set of gender toolkits to help tackle gender barriers to vaccination in DRC.

Explore how GRID3’s core spatial data – including population estimates, settlements, health facilities, and administrative boundaries – are making a difference in Nigeria and the Democratic Republic of Congo. This report details our achievements between 2023 and 2024, highlighting our […]

The Geospatial Microplanning Toolkit (GMT) is a mobile application that allows health staff to record, edit, and visualize key spatial data and features via an interactive user interface. The GMT was developed to address three issues affecting health planning and […]

This case study examines microplanning approaches as a key component in the Mashako Plan 2.0 to revitalize routine immunization strategies in the Democratic Republic of Congo (DRC).

This impact report provides an overview of GRID3’s activities and successes during the program’s first phase, between 2017 and 2022. It highlights key milestones, achievements, and selected use cases from the Democratic Republic of the Congo, Nigeria, and Zambia. The […]

This report provides an overview of selected core spatial data that are available and accessible in multiple sub-Saharan African countries. The report lists these datasets and their metadata. This report will not recommend a single dataset as being superior to […]

This project is part of a continued collaboration between Sierra Leone’s Ministry of Basic and Senior Secondary Education (MBSSE, referred henceforth as “the ministry”) and GRID3. Identifying gaps in Frequency Modulated coverage is the concern of the ministry and this report, along with recommendations for how these gaps might best be filled in line with the ministry’s Policy on Radical Inclusion in Schools.

Anonymised and aggregated data from Mobile Network Operators is a key data source for understanding the mobility patterns of populations, and improving decision-making and scenario planning during the COVID-19 epidemic. This data can be analysed in near real-time and provide an overview of mobility patterns across all of Namibia

Anonymised and aggregated data from Mobile Network Operators is a key data source for understanding the mobility patterns of populations, and improving decision-making and scenario planning during the COVID-19 epidemic. This data can be analysed in near real-time and provide an overview of mobility patterns across all of Sierra Leone.

The Ministry of Basic and Senior Secondary Education (MBSSE, referred henceforth as “the ministry”)
is interested in identifying suitable school catchment areas across Sierra Leone (SLE) and publishing
a companion strategy for school development. This report outlines the findings from a preliminary school mapping and coverage analysis whose aim is to provide insights into existing school coverage using the population estimates created jointly by Statistics Sierra Leone and GRID3 (WorldPop and Statistics Sierra Leone, 2020) and the existing school locations from the 2019 Annual School Census (MBSSE, 2019).

Analyse du travail de GRID3 pour soutenir l’harmonisation, la production et l’utilisation d’unités juridiques/administratives numérisées, opérationnelles et spatiales statistiques. Cette étude se concentre sur des cas étudiés au Nigéria, en République Démocratique du Congo et en Zambie.

Analyse du travail de GRID3 pour collecter et analyser les données des localités. Ce travail se concentre sur deux pôles primaires: créer une couche des localités améliorée et exhaustive qui permet d’avoir une vue d’ensemble des communautés; utiliser les empreintes des bâtiments, les données géospatiales et algorithmes avec apprentissage automatique, pour classer les structures et zones locales au sein des localités. Cette étude explore les applications des méthodes de GRID3 au Nigéria, en République Démocratique du Congo et en Zambie.

Discusses GRID3’s support of countries’ efforts to create and/or strengthen their enumeration area boundaries. The paper focuses on case studies in the Democratic Republic of Congo, Ghana and Zambia.

Discusses GRID3’s work with local stakeholders and data collectors to build capacity for the production and management of geospatial data on health facilities. This paper focuses on case studies in Nigeria, the Democratic Republic of Congo, Zambia, and Sierra Leone.

Discusses GRID3’s work to support the harmonisation, production, and use of digitised legal/administrative units, operational units, and statistical areas. The paper focuses on case studies in Nigeria, the Democratic Republic of Congo, and Zambia.

Discusses GRID3’s work on collecting and analysing settlements data. GRID3’s settlements work has two areas of focus: creating a comprehensive settlement layer that enables a real-world picture of communities, and using building footprints, geospatial data layers, and machine learning algorithms to classify structures and local areas within settlements. The paper also discusses the applications of GRID3’s methods in Nigeria, the Democratic Republic of the Congo, and Zambia.