CS_06.md

title	week	type	subtitle	reading	tasks
Find hottest country on each continent	6	Case Study	Use sf and raster to quantify maximum temperature for each country and then identify the hottest one on each continent.	Raster Vector Interactions [GCR](https://geocompr.robinlovelace.net/geometric-operations.html#raster-vector){target='blank'}	Calculate annual maximum temperatures from a monthly spatio-temporal dataset Remove Antarctica from the `world` dataset Summarize raster values within polygons Generate a summary figure and table. Save your script as a .R or .Rmd in your course repository

Reading

• Raster Vector Interactions GCR{target='blank'}

Background

The raster data format is commonly used for environmental datasets such as elevation, climate, soil, and land cover. We commonly need to extract the data from raster objects using simple features (vector objects). For example if you had a set of points you collected using a GPS and wanted to know the mean annual temperature at each point, you might extract the data from each location in a raster-based map of temperature.

You could also be interested in some summary of the raster data across multiple pixels (such as the buffered points above, a transect, or within a polygon). For example, you might be interested in the mean elevation within the entire polygon in the above figure.

In this case study we'll work with a timeseries of temperature data from WorldClim. These are near-global rasters of various climatic variables available at several resolutions. For convenience, we'll work with the very coarse data (0.5 degree, which is about 50km), but much finer data are available (~1km).

Objective

Identify the hottest country on each continent (not counting Antarctica) by intersecting a set of polygons with a raster image and calculating the maximum raster value in each polygon.

Tasks

• Calculate annual maximum temperatures from a monthly spatio-temporal dataset
• Remove Antarctica from the world dataset
• Summarize raster values within polygons
• Generate a summary figure and table.
• Save your script as a .R or .Rmd in your course repository

Download starter R script (if desired){target="_blank"}. Save this directly to your course folder (repository) so you don't lose track of it!

Show Hints

The details below describe one possible approach.

Libraries

You will need to load the following packages

library(raster)
library(sp)
library(spData)
library(tidyverse)
library(sf)

Loading the spData() package may return a warning: To access larger datasets...install spDataLarge.... This is not required - you can use the standard lower resolution files and safely ignore this message.

Data

Download monthly WorldClim data

data(world)  #load 'world' data from spData package
tmax_monthly <- getData(name = "worldclim", var="tmax", res=10)

If the Worldclim website is down (e.g. you get an error above), you can get similar long term mean temperature data from the Climatic Research Unit (CRU) here. Download these data in netcdf format using the code below:

library(ncdf4)
download.file("https://crudata.uea.ac.uk/cru/data/temperature/absolute.nc","crudata.nc")
tmean=raster("crudata.nc")

Note: If you use the CRU data instead of the WorldClim data, you will have to edit/adjust the steps below accordingly.

Steps

1. Prepare country polygon data (the world object).
   1. Remove "Antarctica" with filter() because WorldClim does not have data there.
   2. Convert the world object to sp format (the 'old' format) because the raster package doesn't accept sf objects. you can do this with as(world,"Spatial").
2. Prepare Climate Data
   1. Download and load the WorldClim maximum temperature dataset at the lowest resolution (10 degrees) using the code above (tmax_monthly=getData(...)).
   2. Inspect the new tmax_monthly object (you can start by just typing it's name tmax_monthly, then perhaps making a plot()). How many layers does it have? What do these represent? What are the units? You can read more about the data here
   3. Set the appropriate gain() to convert to Degrees C. You can figure this out using information found here and ?gain(). What value do you need to multiply with the data to get degrees C?
   4. Create a new object called tmax_annual that is the annual maximum temperature in each pixel of the raster stack using max(). This will find the maximum temperature in each location across all months.
   5. Use names(tmax_annual) <- "tmax" to change the name of the layer in the new tmax_annual object to tmax. This makes the later code more intuitive than keeping the default name layer.
3. Calculate the maximum temperature observed in each country.
   1. use raster::extract() to identify the maximum temperature observed in each country (fun=max). Also set na.rm=T, small=T, sp=T to 1) handle missing data along coastlines, 2) account for small countries that may not have a full 0.5 degree pixel in them, and 3) return a spatial polygon object instead of just a vector of values.
   2. convert the results of the previous step to sf format with st_as_sf(). Now you have an updated polygon object with a new column of maximium temperature. Cool!
4. Communicate your results
   1. use ggplot() and geom_sf() to plot the maximum temperature in each country polygon (not the original raster layer). To recreate the image below, you will also need +scale_fill_viridis_c(name="Annual\nMaximum\nTemperature (C)"). Note the use of \n to insert a line break in the text string. You can move the legend around with +theme(legend.position = 'bottom').
   2. use dplyr tools to find the hottest country in each continent. You may need group_by() and top_n. To create a nice looking table, you may also want to use select() to keep only the desired columns, arrange() to sort them, st_set_geometry(NULL) to drop the geometry column (if desired).

Your final result should look something like this:

And the summary table will look like this:

name_long	continent	tmax
Algeria	Africa	48.9
Iran	Asia	46.7
United States	North America	44.8
Australia	Oceania	41.8
Argentina	South America	36.5
Spain	Europe	36.1
French Southern and Antarctic Lands	Seven seas (open ocean)	11.8

Note that these data are based on 0.5 degree resolution data and thus will miss small hot places and cannot be directly compared with station-based data.

Extra time? Try this...

Build a leaflet map of the same dataset.

<iframe id="test" style=" height:400px; width:100%;" scrolling="no" frameborder="0" src="CS06_leaflet.html"></iframe>