Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/lydialucchesi/Vizumap

R package for visualizing uncertainty in spatial data
https://github.com/lydialucchesi/Vizumap

Last synced: 3 months ago
JSON representation

R package for visualizing uncertainty in spatial data

Awesome Lists containing this project

README 

        
---

output: rmarkdown::github_document

---



```{r setup, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  message = FALSE,

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# Vizumap 

## An R package for visualizing uncertainty in spatial data. 



[![R build status](https://github.com/lydialucchesi/Vizumap/workflows/R-CMD-check/badge.svg)](https://github.com/lydialucchesi/Vizumap/actions)



There is a [`Vizumap` pkgdown site](https://lydialucchesi.github.io/Vizumap/) with a vignette.



A [`Vizumap` paper](https://doi.org/10.21105/joss.02409) is available in the Journal of Open Source Software (JOSS). If you use `Vizumap`, please cite this paper.



## Installation



You can install `Vizumap` using the command below.



```

remotes::install_github(repo = "lydialucchesi/Vizumap", build_vignettes = TRUE, force = TRUE)

```



## About the package



Approaches for visualising uncertainty in spatial data are presented in this package. These include the three approaches developed in [Lucchesi and Wikle (2017)](https://onlinelibrary.wiley.com/doi/full/10.1002/sta4.150) and a fourth approach presented in [Kuhnert et al. (2018)](https://publications.csiro.au/publications/#publication/PIcsiro:EP168206). The package is outlined in [Lucchesi et al. (2021)](https://doi.org/10.21105/joss.02409).



#### Bivariate map



In these bivariate choropleth maps, two colour schemes, one representing the estimates and another representing the margins of error, are blended so that an estimate and its error can be conveyed on a map using a single colour. 



#### Pixel map



In this approach, each map region is pixelated. Pixels are filled with colours representing values within an estimate's margin of error. Regions that appear as a solid colour reflect smaller margins of error, while more pixelated regions indicate greater uncertainty. These maps can be animated to provide a novel uncertainty visualisation experience.



#### Glyph map



In this method, glyphs located at region centroids are rotated to represent uncertainty. The colour filling each glyph corresponds to the estimate.



#### Exceedance probability map



The final map-based exploration is through exceedance probabilities, which are visualised on a map to highlight regions that exhibit varying levels of departure from a threshold of concern or target. 



## Examples



A vignette is available and contains examples for each map type.



```

vignette("Vizumap")

```



## Contribute



To contribute to `Vizumap`, please follow these [guidelines](CONTRIBUTING.md).



Please note that the `Vizumap` project is released with a [Contributor Code of Conduct](CONDUCT.md). By contributing to this project, you agree to abide by its terms.



## License



`Vizumap` version 1.2.0 is licensed under [GPLv3](LICENSE.md).



## Citation



Lucchesi et al., (2021). Vizumap: an R package for visualising uncertainty in spatial data. Journal of Open Source Software, 6(59), 2409, https://doi.org/10.21105/joss.02409



```

@article{lucchesi2021vizumap,

  title={Vizumap: an R package for visualising uncertainty in spatial data},

  author={Lucchesi, Lydia R and Kuhnert, Petra M and Wikle, Christopher K},

  journal={Journal of Open Source Software},

  volume={6},

  number={59},

  pages={2409},

  year={2021}

}

```



## History of Vizumap



Vizumap began as a visualisation project at the University of Missouri in 2016. Chris Wikle, professor of statistics, posed an interesting research question to Lydia Lucchesi, a student curious about data visualisation and R.



How do you include uncertainty on a map displaying areal data estimates?



Over the course of a year, they put together three methods for visualising uncertainty in spatial statistics: the bivariate choropleth map, the pixel map, and the glyph map. By mid-2017, there were maps, and there was a lot of R code, but there was not a tool that others could use to easily make these types of maps, too. That’s when statistician Petra Kuhnert recommended developing an R package. Over the course of a month, Petra and Lydia developed Vizumap (originally named VizU) at CSIRO Data61 in Canberra, Australia. Since then, the package has been expanded to include exceedance probability maps, an uncertainty visualisation method developed by Petra while working on a Great Barrier Reef (GBR) project.



Vizumap has been used to visualise the uncertainty of American Community Survey estimates, the prediction errors of sediment estimates in a GBR catchment, and most recently the [uncertainty of estimated locust densities in Australia](https://www.nature.com/articles/s41598-020-73897-1/figures/4). We would like to assemble a Vizumap gallery that showcases different applications of the package’s mapping methods. If you use Vizumap to visualise uncertainty, please feel free to send the map our way. We would like to see it!



## References



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](https://publications.csiro.au/publications/#publication/PIcsiro:EP168206), 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](https://onlinelibrary.wiley.com/doi/full/10.1002/sta4.150), Stat, https://doi.org/10.1002/sta4.150.



Lucchesi, L.R., Kuhnert, P.M. and Wikle, C.K. (2021) [Vizumap: an R package for visualising uncertainty in spatial data](https://doi.org/10.21105/joss.02409), Journal of Open Source Software, https://doi.org/10.21105/joss.02409.