This lesson is being piloted (Beta version)

Geospatial Workshop Overview: Setup

Overview

This workshop is designed to be run on your local machine. First, you will need to download the data we use in the workshop. Then, you need to set up your machine to analyze and process geospatial data. We provide below instructions to either install all components manually (option A), or to use a Docker image that provides all the software and dependencies needed (option B).

Data

You can download all of the data used in this workshop by clicking this download link. Clicking the download link will automatically download all of the files to your default download directory as a single compressed (.zip) file. To expand this file, double click the folder icon in your file navigator application (for Macs, this is the Finder application).

For a full description of the data used in this workshop see the data page.

Option A: Local Installation

Software

Software Install Manual Available for Description
GDAL Link Link Linux, MacOS, Windows Geospatial model for reading and writing a variety of formats
GEOS Link Link Linux, MacOS, Windows Geometry models and operations
PROJ.4 Link Link Linux, MacOS, Windows Coordinate reference system transformations
R Link Link Linux, MacOS, Windows Software environment for statistical and scientific computing
RStudio Link   Linux, MacOS, Windows GUI for R
UDUNITS Link Link Linux, MacOS, Windows Unit conversions

We provide below quick instructions for installing the various software needed for this workshop. At points, they assume familiarity with the command line and with installation in general. As there are different operating systems and many different versions of operating systems and environments, these may not work on your computer. If an installation doesn’t work for you, please refer to the installation instructions for that software, listed in the table above.

GDAL, GEOS, and PROJ.4

The installation of the geospatial libraries GDAL, GEOS, and PROJ.4 varies significantly based on operating system. These are all dependencies for sf, the R package that we will be using for spatial data operations throughout this workshop.

Windows

To install the geospatial libraries, install the latest version RTools

macOS

For participants who do not already have homebrew installed, and who may be less comfortable with the command line, the easiest was to obtain the geospatial libraries is to install the latest version of Kyng Chaos’s pre-built package for GDAL Complete. Be aware that several other libraries are also installed, including the UnixImageIO, SQLite3, and NumPy.

After downloading the package in the link above, you will need to double-click the cardbord box icon to complete the installation. Depending on your security settings, you may get an error message about “unidentified developers”. You can enable the installation by following these instructions for installing programs from unidentified developers.

Alternatively, participants who are comfortable with the command line can install the geospatial libraries individually using homebrew:

$ brew tap osgeo/osgeo4mac && brew tap --repair
$ brew install proj
$ brew install geos
$ brew install gdal2 --with-armadillo --with-complete --with-libkml --with-unsupported
$ brew link --force gdal2

Linux

Steps for installing the geospatial libraries will vary based on which form of Linux you are using. These instructions are adapted from the sf package’s README.

For Ubuntu:

$ sudo add-apt-repository ppa:ubuntugis
$ sudo apt-get update
$ sudo apt-get install libgdal-dev libgeos-dev libproj-dev

For Fedora:

$ sudo dnf install gdal-devel proj-devel proj-epsg proj-nad geos-devel

For Arch:

$ pacman -S gdal proj geos

For Debian: The rocker geospatial Dockerfiles may be helpful. Ubuntu Dockerfiles are found here.

UDUNITS

Linux users will have to install UDUNITS separately. Like the geospatial libraries discussed above, this is a dependency for the R package sf. Due to conflicts, it does not install properly on Linux machines when installed as part of the sf installation process. It is therefore necessary to install it using the command line ahead of time.

Linux

Steps for installing the geospatial will vary based on which form of Linux you are using. These instructions are adapted from the sf package’s README.

For Ubuntu:

$ sudo apt-get install libudunits2-dev

For Fedora:

$ sudo dnf install udunits2-devel

For Arch:

$ pacaur/yaourt/whatever -S udunits

For Debian:

$ sudo apt-get install -y libudunits2-dev

R

Participants who do not already have R installed should download and install it.

Windows

To install R, Windows users should select “Download R for Windows” from RStudio and CRAN’s cloud download page, which will automatically detect a CRAN mirror for you to use. Select the base subdirectory after choosing the Windows download page. A .exe executable file containing the necessary components of base R can be downloaded by clicking on “Download R 3.x.x for Windows”.

macOS

To install R, macOS users should select “Download R for (Mac) OS X” from RStudio and CRAN’s cloud download page, which will automatically detect a CRAN mirror for you to use. A .pkg file containing the necessary components of base R can be downloaded by clicking on the first available link (this will be the most recent), which will read R-3.x.x.pkg.

Linux

To install R, Linux users should select “Download R for Linux” from RStudio and CRAN’s cloud download page, which will automatically detect a CRAN mirror for you to use. Instructions for a number of different Linux operating systems are available.

RStudio

RStudio is an GUI for using R that is available for Windows, macOS, and various Linux operating systems. It can be downloaded here. You will need the free Desktop version for your computer.

R Packages

The following R packages are used in the various geospatial lessons.

To install these packages in RStudio, do the following:
1. Open RStudio by double-clicking the RStudio application icon. You should see something like this:

RStudio layout

2. Type the following into the console and hit enter.

install.packages(c("dplyr", "ggplot2", "raster", "rgdal", "rasterVis", "sf"))

You should see a status message starting with:

trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.5/dplyr_0.7.6.tgz'
Content type 'application/x-gzip' length 5686536 bytes (5.4 MB)
==================================================
downloaded 5.4 MB

trying URL 'https://cran.rstudio.com/bin/macosx/el-capitan/contrib/3.5/ggplot2_3.0.0.tgz'
Content type 'application/x-gzip' length 3577658 bytes (3.4 MB)
==================================================
downloaded 3.4 MB

When the installation is complete, you will see a status message like:

The downloaded binary packages are in
/var/folders/7g/r8_n81y534z0vy5hxc6dx1t00000gn/T//RtmpJECKXM/downloaded_packages

You are now ready for the workshop!

Option B: Docker

Docker provides developers with a means for creating interactive containers that contain pre-installed software. A selection of pre-installed software in Docker is called an image. An image can be downloaded and used to create a local container, allowing end-users to get software up and running quickly. This is particularly useful when a local installation of the software could be complex and time consuming. For R users, a Docker image can be used to create a virtual installation of R and RStudio that can be run through your web browser.

Option B involves downloading an Docker image that contains an installation of R, RStudio Server, all of the necessary dependencies listed above, and almost all of the R packages used in the geospatial lessons. You will need to install the appropriate version of Docker’s Community Edition software and then download and use the rocker/geospatial Docker image to create a container that will allow you to use R, RStudio, and all the required GIS tools without installing any of them locally.

Once up and running - you’ll have full access to RStudio right from your browser:

Please be aware that the R package rasterVis is not included in the rocker/geospatial Docker image. If your instructor teaches with this package, this will be an R package you will need to install yourself. All other R packages will already be installed for you.

Downloading and Installing Docker Community Edition

To get started with Docker, download the Docker Community Edition from Docker’s store. Community editions are available for Windows, macOS, and Linux operating systems including Debian, Fedora, and Ubuntu.

The download pages for each of these operating systems contain notes about some necessary system requirements and other pre-requisites. Once you download the installer and follow the on-screen prompts.

Additional installation notes are available in Docker’s documentation for each of these operating systems: Windows, macOS, Debian, Fedora, and Ubuntu.

Using the rocker/geospatial Docker Image via the Command Line

Download and Set-up

Once Docker is installed and up and running, you will need to open your computer’s command line terminal. We’ll use the terminal to download rocker/geospatial, a pre-made Docker image that contains an installation of R, RStudio Server, all of the necessary dependencies, and all but one of the R packages needed for this workshop.

You need to have already installed Docker Community Edition (see instructions above) before proceeding. Once you have Docker downloaded and installed, make sure Docker is running and then enter the following command into the terminal to download the rocker/geospatial image:

$ docker pull rocker/geospatial

Once the pull command is executed, the image needs to be run to become accessible as a container. In the following example, the image is named rocker/geospatial and the container is named gis. The image contains the software you’ve downloaded, and the container is the run-time instance of that image. New Docker users should need only one named container per image.

When docker run is used, you can specify a folder on your computer to become accessible inside your RStudio Server instance. The following docker run command exposes Jane’s GitHub directory to RStudio Server. Enter the file path where your workshop resources and data are stored:

$ docker run -d -P --name gis /Users/jane/GitHub:/home/rstudio/GitHub rocker/geospatial

When she opens her RStudio instance below, she will see a GitHub folder in her file tab in the lower righthand corner of the screen. Windows and Linux users will have to adapt the file path above to follow the standards of their operating systems. More details are available on rocker’s Wiki.

The last step before launching your container in a browser is to identify the port that your Docker container is running in:

$ docker port gis

An output, for example, of 8787/tcp -> 0.0.0.0:32768 would indicate that you should point your browser to http://localhost:32768/. If prompted, enter rstudio for both the username and the password.

Stopping a Container

When you are done with a Docker session, make sure all of your files are saved locally on your computer before closing your browser and Docker. Once you have ensured all of your files are available (they should be saved at the file path designated in docker run above), you can stop your Docker container in the terminal:

$ docker stop gis

Re-starting a Container

Once a container has been named and created, you cannot create a container with the same name again using docker run. Instead, you can restart it:

$ docker start gis

If you cannot remember the name of the container you created, you can use the following command to print a list of all named containers:

$ docker ps -a

If you are returning to a session after stopping Docker itself, make sure Docker is running again before re-starting your container!

Using the rocker/geospatial Docker Image via Kitematic

Download and Install Kitematic

Kitematic is the GUI, currently in beta, that Docker has built for accessing images and containers on Windows, macOS, and Ubuntu. You can download the appropriate installer files from Kitematic’s GitHub release page. You need to have already installed Docker Community Edition (see instructions above) before installing Kitematic!

Opening a Container with Kitematic

Once you have installed Kitematic, make sure the Docker application is running and then open Kitematic. You should not need to create a login to use Kitematic. If prompted for login credentials, there is an option to skip that step. Use the search bar in the main window to find rocker/geospatial (pictured below) and click Create under that Docker repository.

After downloading and installing the image, your container should start automatically. Before opening your browser, connect your Docker image to a local folder where you have your workshop resources stored by clicking on the Settings tab and then choosing Volumes. Click Change and then select the directory you would like to connect to.

When you open RStudio instance below, you will see the contents of the connected folder inside the kitematic directory in the file tab located in the lower righthand corner of the screen.

When you are ready, copy the Access URL from the Home tab:

Paste that url into your browser and, if prompted, enter rstudio for both the username and the password.

Stopping and Restarting a Container

When you are done with a Docker session, make sure all of your files are saved locally on your computer before closing your browser and Docker. Once you have ensured all of your files are available (they should be saved at the file path designated in docker run above), you can stop your Docker container by clicking on the Stop icon in Kitematic’s toolbar.

You can restart your container later by clicking the Restart button.

Managing Docker Containers and Images

To obtain a list of all of your current Docker containers:

$ docker ps -a

To list all of the currently downloaded Docker images:

$ docker images -a

These images can take up system resources, and if you’d like to remove them, you can use the docker prune command. To remove any Docker resources not affiliated with a container listed under docker ps -a:

$ docker system prune

To remove all Docker resources, including currently named containers:

$ docker system prune -a