Climate Mortality

This repository contains code originally developed in partial fulfillment of my DATA 670 Data Analytics Capstone class at UMUC in Fall 2021.

It is intended for processing a large set of data files from NOAA, WHO, and the CMIP5 climate change projections. The overall project is to build a model projecting climate-change impacts on human mortality in the continental United States. This model will be built based on current NOAA weather records and WHO mortality data from around the world, on the assumption that local climate is a significant contributor to the causes and rates of death in different countries.

Author: Sarah Messer

Setup

Install python dependencies, typically with a command like this one:

$ pip install -r requirements.txt

Copy "files.yaml.SAMPLE" to "files.yaml" in the same directory.
Download and uncompress the source data.
Alter the paths in "files.yaml" to match the download locations and local directory structure.
Create empty directories for the "filtered_dir" and "output_dir" locations specified in your modified files.yaml.

References

Sources describing and providing the data used in these files follow. The actual data files are not included here, but questions about source-file format and contents may be resolved by reviewing these references:

Lawrimore, J. H., Ray, R., Applequist, S., Korzeniewski, B., & Menne, M. J. (2020, October 23). Global Summary of the Month (GSOM), Version 1. Retrieved from NOAA National Centers for Environmental Information: https://www.ncei.noaa.gov/metadata/geoportal/rest/metadata/item/gov.noaa.ncdc:C00946/html
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A., & Ramirez-Villegas, J. (2020, January 20). High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Retrieved from National Library of Medicine: https://pubmed.ncbi.nlm.nih.gov/31959765/
World Health Organization. (2021). Global Health Estimates. Retrieved from World Health Organization: https://www.who.int/data/global-health-estimates

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