extract_means_for_geography

Takes a NetCDF file of gridded wildfire data and shapefile for
geographical regions and extracts mean values for each shapefile region.
Information on NetCDF files:
https://climatedataguide.ucar.edu/climate-tools/NetCDF#:~:text=An%20nc4%20files%20is%20a,readily%20handle%20netCDF%2D4%20files.
We use a daily time series of gridded wildfire-related
PM2.5 concentration from the Finnish Meteorological Institute's SILAM-CTM
model. This is available open-source:
https://doi.org/10.57707/fmi-b2share.d1cac971b3224d438d5304e945e9f16c.

internal

Tools for producing climate-health indicators and supporting
official statistics from health and climate data. Implements analytical
workflows for temperature-related mortality, wildfire smoke exposure,
air pollution, suicides related to extreme heat, malaria, and
diarrhoeal disease outcomes, with utilities for descriptive statistics, model
validation, attributable fraction and attributable number estimation,
relative risk estimation, minimum mortality temperature estimation,
and plotting for reporting. These six indicators are endorsed by
the United Nations Statistical Commission for inclusion in the
Global Set of Environment and Climate Change Statistics.
Implemented methods include distributed lag non-linear models (DLNM),
quasi-Poisson time-series regression, case-crossover analysis,
Bayesian spatio-temporal models using the Integrated Nested Laplace
Approximation ('INLA'), and multivariate meta-analysis for
sub-national estimates. The package is based on methods developed
in the Standards for Official Statistics on Climate-Health
Interactions (SOSCHI) project
<https://climate-health.officialstatistics.org>. For methodologies,
see Watkins et al. (2025) <doi:10.5281/zenodo.14865904>,
Brown et al. (2024) <doi:10.5281/zenodo.14052183>,
Pearce et al. (2024) <doi:10.5281/zenodo.14050224>,
Byukusenge et al. (2025) <doi:10.5281/zenodo.15585042>,
Dzakpa et al. (2025) <doi:10.5281/zenodo.14881886>, and
Dzakpa et al. (2025) <doi:10.5281/zenodo.14871506>.

Kenechi Omeke

climatehealth

Statistical Tools for Modelling Climate-Health Impacts

Charlie Browning

Etse Yawo Dzakpa

Gladin Jose

Matt Pearce

Ellie Watkins

Claire Hunt

Beatrice Byukusenge

Cassien Habyarimana

Venuste Nyagahakwa

Felix Scarbrough

Treesa Shaji

Bonnie Lewis

Maquines Odhiambo Sewe

Vijendra Ingole

Sean Lovell

Antony Brown

Euan Soutter

Gillian Flower

David Furley

Joe Panes

Charlotte Romaniuk

Milly Powell

Wellcome 

Office for National Statistics 

extract_means_for_geography function

<dl><dt>ncdf_path</dt>
<dd>Path to a NetCDF file</dd>
<dt>shp_path</dt>
<dd>Path to a shapefile .shp of the geographical
boundaries for which to extract mean values of wildfire-related PM2.5</dd>
<dt>region_col</dt>
<dd>Character. The name of the column containing region data
in the shapefile. Defaults to 'region'</dd>
<dt>output_value_col</dt>
<dd>Character. The name of the value column to include
in the output. Defaults to mean_PM</dd></dl>

Arguments

Extract mean wildfire PM2.5 values for shapefile regions from NetCDF file — extract_means_for_geography

<dl>

<dt>ncdf_path</dt>
<dd>Path to a NetCDF file</dd>


<dt>shp_path</dt>
<dd>Path to a shapefile .shp of the geographical
boundaries for which to extract mean values of wildfire-related PM2.5</dd>


<dt>region_col</dt>
<dd>Character. The name of the column containing region data
in the shapefile. Defaults to 'region'</dd>


<dt>output_value_col</dt>
<dd>Character. The name of the value column to include
in the output. Defaults to mean_PM</dd>

</dl>

Extract mean wildfire PM2.5 values for shapefile regions from NetCDF file

extract_means_for_geography: Extract mean wildfire PM2.5 values for shapefile regions from NetCDF file

Description

Usage

Value

Arguments