Need: Analysis of geographical variables on population health is common in the NHS, although difficult due to the lack of granularity that open data affords. This project would seek to demonstrate and implement spatial and/or surface regression techniques to open data in order to create standardised mapping layers which could be used as a spatial variable in spatial health modelling. Additionally, exploration of spatial statistical methods to demonstrate drawing inference from interacting different mapping layers appropriately.
Current Knowledge/Examples & Possible Techniques/Approaches: Colleagues in the Data Analytics Learning Laboratory (DALL) have demonstrated a surface level regression for pollen counts.
Related Previous Internship Projects: n/a as first year of the scheme.
Enables Future Work: Resource for spatial analysis.
Outcome/Learning Objectives: Public facing Github repo with generated granular data alongside a publication outlining the appropriateness and limitations of methods used.
Datasets: Open data such as pollution and traffic data
Desired skill set: When applying please highlight any experience around mapping, regression (especially spatial and surface), coding experience (including any coding in the open), any other data science experience you feel relevant.
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