Dilinie Seimon

Varsha Ujjinni Vijay Kumar

As two postgrad students on summer vacation but with no travel plans
(during this global pandemic of course), we took up an internship at
King Abdullah University of Science and Technology (KAUST) last summer
and ended up collaborating on a really cool project with [Paula
Moraga](https://www.paulamoraga.com/)! Did we mention that we worked in
Saudi Arabia, while living in Australia? All is well when we have the
internet.

We also ended up [presenting our project at useR!
2021](https://www.youtube.com/watch?v=nLikORFBKgk), and winning an award
for the most outstanding lightning talk! So, keep reading to see how we
got there!

Although there is so much data being collected in multiple disciplines
and made openly available, it may be difficult to find, retrieve and
utilize these resources. Spatial and spatio-temporal data are two data
types which are collected and used in research across a diverse array of
domains.

With the R programming language becoming increasingly popular among
academics, researchers, and scientists, and because spatial data is
easier to interpret when visualized, Paula thought it would be a great
idea to create a repository of data sources and simple tutorials on how
to retrieve and visualize spatial data using R, during our time as
interns at KAUST.

This idea became [rspatialdata](https://rspatialdata.github.io/) - a
collection of data sources and tutorials on downloading and visualising
spatial data using R.

<figure class="center">
<a href="https://rspatialdata.github.io/"><img
src="rspatialdata-home.jpg" width="800"
alt="Screenshot of rspatialdata homepage featuring multicoloured tiles each outlining a different type of spatial data" /></a>
</figure>

[rspatialdata](https://rspatialdata.github.io/) consists of a
non-exhaustive list of R-packages which have been developed as clients
for different spatio-temporal databases and APIs. It also consists of
tutorials on how to use these R packages, understand the different types
of spatial data objects available and create visualizations using them.
After doing a lot of research, we managed to pick out a few R-packages
which we were confident gave up-to-date data from reliable sources.

Coincidentally, much of what we picked out happened to be rOpenSci
packages! We were amazed at how rich and easy to use most of these
packages were. So here is our experience using those packages and some
things we really enjoyed doing!

## Downloading boundaries of countries using [rnaturalearth](https://docs.ropensci.org/rnaturalearth) {#downloading-boundaries-of-countries-using-rnaturalearthhttpsdocsropensciorgrnaturalearth}

The rnaturalearth package (by [Andy
South](https://github.com/andysouth)) facilitates communication with
[Natural earth](http://www.naturalearthdata.com/) map data. It allows
you to easily download and visualize boundaries of countries and
boundaries of states within countries as well.

We used rnaturalearth to download the boundaries of countries in many of
our tutorials - including our tutorials on
[elevation](https://rspatialdata.github.io/elevation.html),
[rainfall](https://rspatialdata.github.io/rainfall.html) and
[humidity](https://rspatialdata.github.io/humidity.html).

## Visualizing hospitals in Lagos, Nigeria using [osmdata](https://docs.ropensci.org/osmdata/) {#visualizing-hospitals-in-lagos-nigeria-using-osmdatahttpsdocsropensciorgosmdata}

osmdata (by [Mark Padgham](/author/mark-padgham/), [Robin
Lovelace](/author/robin-lovelace/), [Maëlle
Salmon](/author/ma%C3%ABlle-salmon/) and [Bob
Rudis](/author/bob-rudis/)) is an R package for downloading spatial data
from [OpenStreetMap (OSM)](https://www.openstreetmap.org/) - a very cool
open source project. We were amazed at the huge variety of spatial
features available to us! We were able to download spatial data about
almost anything from amenities such as colleges, cinemas, hospitals and
banks, to different types of highways and streets such as walking and
bicycle paths, residential streets, motorways and service lanes.

We used the osmdata package to download and visualize hospitals in
Lagos, Nigeria. Then we also downloaded spatial data for different types
of highways, streets and waterways in Lagos, and created the following
map entirely using data retrieved through osmdata!

<figure class="center">
<a href="https://rspatialdata.github.io/osm.html"><img
src="hospitals-lagos-nigeria.jpg" width="600"
alt="A black and white map of Logos, Nigeria, showing highways in orange, smaller streets in grey, rivers in light blue, and navy blue shapes outlining hospital locations" /></a>
</figure>

Have a read through our [Open Street Map data
tutorial](https://rspatialdata.github.io/osm.html) for a complete
tutorial on how to install the osmdata package, find what spatial
features are available, learn how to download these features and also
create some cool maps (including an interactive one)!

## Visualizing vegetation in Mongolia and land cover in Zimbabwe using [MODIStsp](https://docs.ropensci.org/MODIStsp/) {#visualizing-vegetation-in-mongolia-and-land-cover-in-zimbabwe-using-modistsphttpsdocsropensciorgmodistsp}

[MODIS (Moderate Resolution Imaging
Spectroradiometer)](https://modis.gsfc.nasa.gov/) is an instrument
aboard the NASA Terra and Aqua satellites, which orbit the entire Earth
every 1-2 days, acquiring data at different spatial resolutions. The
data acquired by MODIS describes features of the land, oceans and the
atmosphere.

MODIStsp - an acronym for 'MODIS Time Series Processing' (by [Lorenzo
Busetto](https://github.com/lbusett) and [Luigi
Ranghetti](https://github.com/ranghetti)), is an R package for
downloading and pre-processing time series raster data from MODIS [data
products](https://modis.gsfc.nasa.gov/data/dataprod/).

We used the [MODIS Vegetation Index Products (NDVI and
EVI)](https://modis.gsfc.nasa.gov/data/dataprod/mod13.php), to visualize
the Normalized Difference Vegetation Index in Mongolia.

<figure class="center">
<a href="https://rspatialdata.github.io/vegetation.html"><img
src="vegetation-mongolia.jpg" width="600"
alt="A map of Mongolia coloured by the Normalized Difference Vegetation Index(NDVI). The map shows the North of Mongolia to have a higher NDVI(coloured in yellow) compared to the South(coloured in dark blue)" /></a>
</figure>

We also used the [MODIS Land Cover Type
Products](https://modis.gsfc.nasa.gov/data/dataprod/mod12.php) to
visualize the land cover classification in Zimbabwe.

<figure class="center">
<a href="https://rspatialdata.github.io/land_cover.html"><img
src="land-cover-zimbabwe.jpg" width="600"
alt="A map of Zimbabwe with its area colored by the corresponding land cover type - such as grasslands, croplands, barren etc." /></a>
</figure>

Using the MODIStsp package made it so much easier to find what data
products were available and also directly download and save this data
using R. Our
[Vegetation](https://rspatialdata.github.io/vegetation.html) and [Land
Cover](https://rspatialdata.github.io/land_cover.html) tutorials explain
how we used the MODIStsp R package to download data and create cool
maps.

## Visualizing rainfall in Gansu, China and relative humidity in Western Australia using [nasapower](https://docs.ropensci.org/nasapower/) {#visualizing-rainfall-in-gansu-china-and-relative-humidity-in-western-australia-using-nasapowerhttpsdocsropensciorgnasapower}

nasapower (by [Adam Sparks](/author/adam-sparks/)) is a client for the
'[NASA POWER](https://power.larc.nasa.gov/)' global meteorology, surface
solar energy and climatology data API, and aims to make it quick and
easy to download climatology data for analysis, visualization, modeling
and many other purposes.

We used the nasapower package to retrieve rainfall and humidity data,
and it was such a simple task. We only had to submit the duration of
data we needed along with the geographical location, and the package
took care of all the hard work.

We downloaded relative humidity and rainfall data and then visualized
the relative humidity in Western Australia and the rainfall in Gansu,
China.

<figure class="center">
<a href="https://rspatialdata.github.io/humidity.html"><img
src="relative-humidity-western-australia.jpg" width="600"
alt="An outline map of Australia and its states. The area of Western Australia is coloured by its relative humidity, with the coastal regions colored in yellow showing higher relative humidity, and the central region of Australia colored in dark blue showing much lower relative humidity." /></a>
</figure>

<figure class="center">
<a href="https://rspatialdata.github.io/rainfall.html"><img
src="rainfall-gansu-china.jpg" width="600"
alt="An outline map of China and its states. The area of Gansu is coloured by its rainfall level, and shows a gradient from dark blue(low rainfall) to yellow(high rainfall) from North to South." /></a>
</figure>

Take a look at our
[rainfall](https://rspatialdata.github.io/rainfall.html) and
[humidity](https://rspatialdata.github.io/humidity.html) tutorials for a
complete guide on how to use the nasapower package to download data and
how to create cool visualizations using the data.

## Retrieving demographic and health survey data using [rdhs](https://docs.ropensci.org/rdhs/) {#retrieving-demographic-and-health-survey-data-using-rdhshttpsdocsropensciorgrdhs}

The rdhs package (by [OJ Watson](/author/oj-watson/) and [Jeff
Eaton](https://github.com/jeffeaton)) is an API client for [Demographic
and Health Surveys (DHS)](https://www.dhsprogram.com/) data - which is a
collection of population, health, HIV and nutrition data from more than
400 surveys in over 90 countries. This data is considered to be
sensitive and hence requires one to set up an account with DHS and
request permission to access the data.

Although this may look like a tedious process, the rdhs package makes it
very smooth by allowing us to test out the functions of the package
using model data. So one could even start on the analysis while waiting
for data access rights!

Read through our tutorial on [Demographic and Health Surveys
(DHS)](https://rspatialdata.github.io/dhs-data.html) for more details on
how to use the rdhs package.

## Interested in more?

Have a look at our [administrative boundaries
tutorial](https://rspatialdata.github.io/admin_boundaries.html) which
runs you through creating outline maps of countries and their
administrative divisions, our [population
tutorial](https://rspatialdata.github.io/population.html) which runs you
through visualizing population estimates of countries using choropleth
maps and cartograms, and our tutorials on
[elevation](https://rspatialdata.github.io/elevation.html),
[temperature](https://rspatialdata.github.io/temperature.html),
[malaria](https://rspatialdata.github.io/malaria.html), and [air
pollution](https://rspatialdata.github.io/air_pollution.html) for ideas
on how to download and visualize each respective property.

You just read through some of the things we enjoyed while creating
[rspatialdata](https://rspatialdata.github.io/). We are excited for you
to try it out too! So give it a go, get creative with it, and let us
know how you did!

**Our goal for rspatialdata is not to be a comprehensive list of R
packages or tutorials, but to be a starting point for anyone to find and
download spatial data related to different domains, and visualize
different types of spatial data objects using different approaches.**

So if you think of anymore R packages that would fit into this
collection, ideas on new tutorials or cool spatial visualizations, do
reach out to us! Let's talk about how we can collaborate!


As two postgrad students on summer vacation but with no travel plans (during this global pandemic of course), we took up an internship at King Abdullah University of Science and Technology (KAUST) last summer and ended up collaborating on a really cool project with Paula Moraga! Did we mention that we worked in Saudi Arabia, while living in Australia? All is well when we have the internet. We also ended up presenting our project at useR!

Postagens de Rogue Scholar

The Story Behind rspatialdata