Spatial Analysis and Mapping with R

# Preface

This tutorial introduces the reader to some of the amazing capabilities of `R`

to work with and map geographic data. Geographic data are data that contain spatial attributes (or spatial data) that define a geographic space (location, area, elevation, etc.) and non spatial attributes (f.e., population density, pollutant concentrations, temperature).

This tutorial was developed for one the units of the course “ENVS 420: Research Seminar in Environmental Sciences” offered at the University of Baltimore. However, it is hoped that readers outside of ENVS 420 who are interested in geospatial analysis and with a basic familiarity of `R`

find this tutorial useful.

The use of an integrated developer environment (IDE) or an IDE like configuration such as the IDE `RStudio`

(https://rstudio.com/) or the `Nvim-R`

plug-in for the integration of `vim/neovim`

and `R`

(https://github.com/ jalvesaq/Nvim-R/tree/stable) is recommended but not necessary.

The tutorial was written with `RMarkdown`

(v. 2.6) (Allaire et al., 2020; Xie et al., 2018, 2020) in R (v. 4.0.2) (R Core Team, 2020).

Required `R`

packages:

`dplyr`

(Wickham et al., 2020)`openxlsx`

(Schauberger & Walker, 2020)`RColorBrewer`

(Neuwirth, 2014)`sf`

(Pebesma, 2018)(Tennekes, 2018)`tmap`

`tidyr`

(Wickham et al., 2020)

## Data

Datatasets used are archived in a zip compressed file (`SpatialAnalysisData.zip`

) that can be downloaded at SpatialAnalysisData. The link will connect you to a cloud storage service (MEGA, https://mega.nz) and ask you to download the file. By accessing the cloud storage service and downloading the file/data you agree to the terms of service of MEGA and to the terms of use of the code and data.

# References

Allaire, J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., Chang, W., & Iannone, R. (2020). *rmarkdown: Dynamic Documents for R*. https://github.com/rstudio/rmarkdown

Neuwirth, E. (2014). *RColorBrewer: ColorBrewer Palettes*. https://CRAN.R-project.org/package=RColorBrewer

*The R Journal*,

*10*(1), 439–446. doi: 10.32614/RJ-2018-009

R Core Team. (2020). *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing. https://www.R-project.org/

Schauberger, P., & Walker, A. (2020). *openxlsx: Read, Write and Edit xlsx Files*. https://CRAN.R-project.org/package=openxlsx

Tennekes, M. (2018). tmap: Thematic maps in R. *Journal of Statistical Software*, *84*(6), 1–39. doi: 10.18637/ jss.v084.i06

*dplyr: A Grammar of Data Manipulation*. https://CRAN.R-project.org/package=dplyr

*tidyr: Tidy Messy Data*. https://CRAN.R-project.org/package=tidyr

*R Markdown: The definitive guide*. Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown

*R Markdown Cookbook*. Chapman; Hall/CRC. https://bookdown.org/yihui/rmarkdown-cookbook

Cover image: Land Use in Greater Baltimore (2002).

Data source: Maryland Archived Land Use Land Cover 2002, Maryland Department of Planning, online at https://data.imap.maryland.gov/datasets/96116be90edb4e8d933048f345c3a487?geometry=-79.386%2

C38.060%2C-75.096%2C39.558#www.mdp.state.md.us/OurWork/landuse.shtml. Accessed on April 17, 2021.