You can install a development version of the VizumApp package using the
command below.
remotes::install_github(repo = "SamNelson081/VizumApp")
library(VizumApp)
runShiny()
Sam Nelson, CSIRO’s Data61, Email: Sam.Nelson@data61.csiro.au
Lydia Lucchesi, Australian National University & CSIRO Data61, Email: Lydia.Lucchesi@anu.edu.au
Petra Kuhnert, CSIRO’s Data61, Email: Petra.Kuhnert@data61.csiro.au
This package builds a Shiny App version of the Vizumap R package that appears here. The app offers four visualisations for visualising uncertainty calculated from spatial data on a map. These visualisations are outlined in Lucchesi et al. (2021).
This app demonstrates the visualisations using two case studies. The first is a US case study described in Lucchesi and Wikle (2017), while the second is based on uncertainty quantification methods developed in Kuhnert et al. (2018) and demonstrated in Lucchesi et al. (2021). Users can select either case study and explore the four different approaches and tune their final visualisation according to the level of transparency and colour scheme required.
VizumApp can be used to visualise your own spatial data with
uncertainties. This requires a shape file for the region of interest and
a .csv file containing the predictions, uncertainties and OBJECTID that
links with the OBJECTID in the shapefile. For this version of the app
only, we require the label corresponding to the linked ID to be named
OBJECTID.
An example of what these files need to look like, see the Burdekin_Ex
directory in inst/shinyApp/extdata. To read in the shapefile you will
need to select all 4 files (UB.dbf, UB.prj, UB.shp and UB.shx). The
.csv file is read in separately. Once read in, you are ready to select
a visualisation.
To illustrate how to do this, watch this short video
A link to the shiny instance appears here but you can also run the app locally after install by typing the following
library(VizumApp)
runShiny()
The following is not an exhaustive list. We plan to implement options for:
- Downloading and saving your final map.
- Outputting a script that allows you to copy into an R Markdown document or directly into R.
- Allowing the user to specify the column ID that links the estimates to the shapefile.
- Selecting the distribution and probability threshold for the excedance map. Currently this is hard wired as an exponential distribution and probability of 0.8.
To contribute to VizumApp, please follow these
guidelines.
Please note that the VizumApp project is released with a Contributor
Code of Conduct. By contributing to this project, you agree
to abide by its terms.
VizumApp version 0.9.2 is licensed under GPLv3.
Nelson, S., Lucchesi, L. and Kuhnert. P.M. (2022). VizumApp: A Shiny App for visualizing uncertainty in spatial data using the Vizumap R package, DOI: http://hdl.handle.net/102.100.100/439688?index=1
Lucchesi, L.R., Kuhnert, P.M. and Wikle, C.K. (2021) Vizumap: an R package for visualising uncertainty in spatial data, Journal of Open Source Software, https://doi.org/10.21105/joss.02409.
Kuhnert, P.M., Pagendam, D.E., Bartley, R., Gladish, D.W., Lewis, S.E. and Bainbridge, Z.T. (2018) Making management decisions in face of uncertainty: a case study using the Burdekin catchment in the Great Barrier Reef, Marine and Freshwater Research, 69, 1187-1200, https://doi.org/10.1071/MF17237.
Lucchesi, L.R. and Wikle C.K. (2017) Visualizing uncertainty in areal data with bivariate choropleth maps, map pixelation and glyph rotation, Stat, https://doi.org/10.1002/sta4.150.