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Research project

AfriMapR

Project overview

There is potential to explore the impacts of climate on human health by combining existing datasets monitoring population health over years and climate datasets for the same times and areas. To achieve this, researchers need to know the geographic area that population health data were collected from. Existing population health datasets do not always have this geographic information readily available. In this pilot project we will review such population health datasets and characterise the geographic information available for them. We will reach out to a selection of data owners to inform the development of a workflow for collating such geographic information. We will pilot an online system enabling recording and storing of this geographic information by data owners and ourselves. Part of the project will be to assess the findability of data owners and their willingness and ability to contribute to this process. We will categorise whether geographic data are available at the level of the study or down to individual participants, and whether they can be represented as a single point, a box representing the NSEW limits or coordinates of a polygon boundary. Potential advantages & disadvantages of the different geographic levels will be explored by piloting software tools to query health-relevant data for them. Such software tools will also have the potential to be useful to data owners and others to bring together climate and population health data.

Staff

Lead researcher

Dr Natalia Tejedor Garavito

Principal Enterprise Fellow

Research interests

  • Geospatial data analysis
  • GIS training
  • Health metrics
Other researchers

Professor Andrew Tatem

Personal Chair

Research interests

  • Developing approaches to map population distributions, demographics and dynamics through complementing traditional datasources (census, survey, registries) with new forms of geospatial data from e.g. satellite imagery and mobile devices.
  • Understanding the drivers of small area heterogeneities in population health and development in low and middle income settings.
  • The use of high resolution demographic and mobility data for improving understanding and modelling of pathogen dynamics.

Collaborating research institutes, centres and groups

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