Disaggregation regression application • disagapp

Disagapp is an application written in R that can be used to perform disaggregation regression analyses using {disaggregation}.

Disagapp was built using the {shinyscholar} template which was itself forked from {wallace} v2.0.5 (CRAN, website)

It is currently in pre-alpha.

Install disagapp via Github and run the application with the following R code.

install.packages("devtools")
devtools::install_github("simon-smart88/disagapp")
library(disagapp)
run_disagapp()

Components and modules

The application is divided in components that are steps in the analysis and modules that are possible options in each step of the analysis. Each of the modules calls a function of the same name, either in this package or in {disaggregation}.

Response - load the response data

Combine spreadsheet and shapefile: upload both files and merge them
Upload spreadsheet: upload the data and combine it with boundary data
Upload shapefile: when the data is already merged in a shapefile
Example datasets: load an example dataset
Edit data: remove unwanted parts of the data
Simplify polygons: simplify the geometries of the boundary data

Covariates - load covariates to use in the analysis

Accessibility: The time required to travel to cities or healthcare. Provided by the Malaria Atlas Project via malariaAtlas
Climate: Various bioclimatic variables relating to temperature and precipitation. Provided by Worldclim via {geodata}
Land use: The percentage of land covered by different classes of land use. Provided by the Copernicus programme
Nighttime lights: Satellite imagery of the intensity of nighttime lights. Provided by NASA via blackmarbler developed by the World Bank
Distance to water: The distance to surface water. Provided by ESRI using data from USGS and ESA
Upload covariates: Upload your own covariates in the .tif format

Aggregation - load an aggregation raster

Population density: Population data provided by Worldpop
Upload aggregation: Upload your own aggregation raster in the .tif format
Uniform: Generate a uniform aggregation raster

Prepare - prepare the data for modelling

Generate mesh: generate a spatial mesh from the response data
Summarise and resample covariates: resample covariates so that they have the same resolution and extent
Scale covariates: scales the covariates so that their coefficients can be compared fairly
Covariate correlations: examine correlations between covariates
Reduce covariate resolution: generate lower resolution covariates to speed up model fitting
Finalise data preparation: combine all the data together

Fit - fit a disaggregation model

Fit model: using either gaussian, binomial or poisson likelihood functions and logit, log or identity link functions

Predict - make predictions

Make predictions: generate predictions from the model
Transfer predictions: transfer predictions to a new area of interest

Reproduce

Download session code: download a .Rmd file that completely replicates the analysis
Reproduce environment: use renv to capture dependencies, allowing the analysis to be reproduced exactly
Download covariates: download copies of the covariate data
Download package references: download a list of all the packages used in the analysis

disagapp