Merge branch 'master' of https://github.com/ITSLeeds/TDS

Author: Robinlovelace

practicals/2-od.Rmd

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+Origin-destination data
+================
+Malcolm Morgan and Robin Lovelace
+University of Leeds
+<br/><img class="img-footer" alt="" src="https://comms.leeds.ac.uk/wp-content/themes/toolkit-wordpress-theme/img/logo.png">
+
+# 1 Review Homework
+
+You should now be familiar with the basics of R and the `tidyverse`. If
+you have not completed these tasks go back and do them first:
+
+-   Read Chapters 2, 3, and 4 of [Reproducible road safety research with
+    R](https://itsleeds.github.io/rrsrr/basics.html)
+-   Read Chapters 3 and 5 of [R for Data
+    Science](https://r4ds.had.co.nz/data-visualisation.html)
+
+# 2 Getting started with GIS in R
+
+Note that this practical takes sections from Chapters 2 - 8 of
+[Geocomputation with R](https://r.geocompx.org). You should expand your
+knowledge by reading these chapters in full.
+
+## Pre-requisites
+
+You need to have a number of packages installed and loaded. Install the
+packages by typing in the following commands into RStudio (you do not
+need to add the comments after the `#` symbol)
+
+If you need to install any of these packages use:
+
+``` r
+install.packages("sf")  # Install a package from CRAN
+remotes::install_github("Nowosad/spDataLarge")   # install from GitHub using the remotes package
+```
+
+``` r
+library(sf)          # vector data package 
+library(tidyverse)   # tidyverse packages
+```
+
+-   It relies on **spData**, which loads datasets used in the code
+    examples of this chapter:
+
+``` r
+library(spData)  # spatial data package 
+```
+
+1.  Check your packages are up-to-date with `update.packages()`
+2.  Create an RStudio project with an appropriate name for this session
+    (e.g. `practical2`)
+3.  Create appropriate folders for code, data and anything else
+    (e.g. images)
+4.  Create a script called `learning-OD.R`, e.g. with the following
+    command:
+
+``` r
+dir.create("code") # 
+file.edit("code/learning-OD.R")
+```
+
+## 2.1 Basic sf operations
+
+We will start with a simple map of the world. Load the `world` object
+from the `spData` package. Notice the use of `::` to say that you want
+the `world` object from the `spData` package.
+
+``` r
+world = spData::world
+```
+
+Use some basic R functions to explore the `world` object.
+e.g. `class(world)`, `dim(world)`, `head(world)`, `summary(world)`. Also
+view the `world` object by clicking on it in the Environment panel.
+
+`sf` objects can be plotted with `plot()`.
+
+``` r
+plot(world)
+```
+
+![](2-od_files/figure-gfm/unnamed-chunk-5-1.png)<!-- -->
+
+Note that this makes a map of each column in the data frame. Try some
+other plotting options
+
+``` r
+plot(world[3:6])
+```
+
+![](2-od_files/figure-gfm/unnamed-chunk-6-1.png)<!-- -->
+
+``` r
+plot(world["pop"])
+```
+
+![](2-od_files/figure-gfm/unnamed-chunk-6-2.png)<!-- -->
+
+## 2.2 Basic spatial operations
+
+Load the `nz` and `nz_height` datasets from the `spData` package.
+
+``` r
+nz = spData::nz
+nz_height = spData::nz_height
+```
+
+We can use `tidyverse` functions like `filter` and `select` on `sf`
+objects in the same way you did in Practical 1.
+
+``` r
+canterbury = nz %>% filter(Name == "Canterbury")
+canterbury_height = nz_height[canterbury, ]
+```
+
+In this case we filtered the `nz` object to only include places called
+`Canterbury` and then did and intersection to find objects in the
+`nz_height` object that are in Canterbury.
+
+This syntax is not very clear. But is the equivalent to
+
+``` r
+canterbury_height = nz_height[canterbury, , op = st_intersects]
+```
+
+There are many different types of relationships you can use with `op`.
+Try `?st_intersects()` to see more. For example this would give all the
+places not in Canterbury
+
+``` r
+nz_height[canterbury, , op = st_disjoint]
+```
+
+![Topological relations between vector geometries, inspired by Figures 1
+and 2 in Egenhofer and Herring (1990). The relations for which the
+function(x, y) is true are printed for each geometry pair, with x
+represented in pink and y represented in blue. The nature of the spatial
+relationship for each pair is described by the Dimensionally Extended
+9-Intersection Model
+string.](https://r.geocompx.org/figures/relations-1.png)
+
+# 3 Getting started with OD data
+
+In this section we will look at basic transport data in the R package
+**stplanr**.
+
+Load the `stplanr` package as follows:
+
+``` r
+library(stplanr)
+```
+
+    ## Warning: package 'stplanr' was built under R version 4.2.2
+
+The `stplanr` package contains some data that we can use to demonstrate
+principles in Data Science, illustrated in the Figure below. Source:
+Chapter 1 of R for Data Science (Grolemund and Wickham 2016) [available
+online](https://r4ds.had.co.nz/introduction.html).
+
+![](https://d33wubrfki0l68.cloudfront.net/571b056757d68e6df81a3e3853f54d3c76ad6efc/32d37/diagrams/data-science.png)
+
+First we will load some sample data:
+
+You can click on the data in the environment panel to view it or use
+`head(od_data)` Now we will rename one of the columns from `foot` to
+`walk`
+
+Next we will made a new dataset `od_data_walk` by taking `od_data` and
+piping it (`%>%`) to `filter` the data frame to only include rows where
+`walk > 0`. Then `select` a few of the columns and calculate two new
+columns `proportion_walk` and `proportion_drive`.
+
+We can use the generic `plot` function to view the relationships between
+variables
+
+``` r
+plot(od_data_walk)
+```
+
+![](2-od_files/figure-gfm/unnamed-chunk-15-1.png)<!-- -->
+
+R has built in modelling functions such as `lm` lets make a simple model
+to predict the proportion of people who walk based on the proportion of
+people who drive.
+
+We can use the `ggplot2` package to graph our model predictions.
+
+``` r
+ggplot(od_data_walk) +
+  geom_point(aes(proportion_drive, proportion_walk)) +
+  geom_line(aes(proportion_drive, proportion_walk_predicted))
+```
+
+![](2-od_files/figure-gfm/unnamed-chunk-17-1.png)<!-- -->
+
+Exercises
+
+1.  What is the class of the data in `od_data`?
+2.  Subset (filter) the data to only include OD pairs in which at least
+    one person (`> 0`) person walks (bonus: on what % of the OD pairs
+    does at least 1 person walk?)
+3.  Calculate the percentage who cycle in each OD pair in which at least
+    1 person cycles
+4.  Is there a positive relationship between walking and cycling in the
+    data?
+5.  Bonus: use the function `od2line()` in to convert the OD dataset
+    into geographic desire lines
+
+# 4 Processing origin-destination data in Bristol
+
+This section is based on [Chapter 12 of Geocomputation with
+R](https://geocompr.robinlovelace.net/transport.html). You should read
+this chapter in full in your own time.
+
+We need the `stplanr` package which provides many useful functions for
+transport analysis and `tmap` package which enables advanced mapping
+features.
+
+``` r
+library(stplanr)
+library(tmap)
+```
+
+We will start by loading two datasets:
+
+``` r
+od = spDataLarge::bristol_od
+zones = spDataLarge::bristol_zones
+```
+
+Explore these datasets using the functions you have already learnt
+(e.g. `head`,`nrow`).
+
+You will notice that the `od` datasets has shared id values with the
+`zones` dataset. We can use these to make desire lines between each
+zone. But first we must filter out trips that start and end in the same
+zone.
+
+``` r
+od_inter = filter(od, o != d)
+desire_lines = od2line(od_inter, zones)
+```
+
+Let’s calculate the percentage of trips that are made by active travel
+
+``` r
+desire_lines$Active = (desire_lines$bicycle + desire_lines$foot) /
+  desire_lines$all * 100
+```
+
+Now use `tmap` to make a plot showing the number of trips and the
+percentage of people using active travel.
+
+``` r
+desire_lines = desire_lines[order(desire_lines$Active),]
+
+tm_shape(desire_lines) +   # Define the data frame used to make the map
+  tm_lines(col = "Active", # We want to map lines, the colour (col) is based on the "Active" column
+           palette = "plasma", # Select a colour palette
+           alpha = 0.7,    # Make lines slightly transparent
+           lwd = "all") +  # The line width (lwd) is based on the "all" column
+  tm_layout(legend.outside = TRUE) + # Move the ledgend outside the map
+  tm_scale_bar()           # Add a scale bar to the map
+```
+
+![](2-od_files/figure-gfm/unnamed-chunk-27-1.png)<!-- -->
+
+Now that we have geometry attached to our data we can calculate other
+variables of interest. For example let’s calculate the distacne
+travelled and see if it relates to the percentage of people who use
+active travel.
+
+``` r
+desire_lines$distance_direct_m = as.numeric(st_length(desire_lines))
+```
+
+Note the use of `as.numeric` by default `st_length` and many other
+functions return a special type of result with `unit`. Here we force the
+results back into the basic R numerical value. But be careful! The units
+you get back depend on the coordinate reference system, so check your
+data before you assume what values mean.
+
+``` r
+ggplot(desire_lines) +
+  geom_point(aes(x = distance_direct_m, y = Active, size = all)) +
+  geom_smooth(aes(x = distance_direct_m, y = Active))
+```
+
+![](2-od_files/figure-gfm/unnamed-chunk-29-1.png)<!-- -->
+
+The blue line is a smoothed average of the data. It shows a common
+concept in transport research, the distance decay curve. In this case it
+shows that the longer the journey the less likely people are to use
+active travel. But this concept applies to all kinds of travel
+decisions. For example you are more likely to travel to a nearby coffee
+shop than a far away coffee shop. Different types of trip have different
+curves, but most people always have a bias for shorter trips.
+
+# 5 Homework
+
+1.  Read Chapters 2-5 of [Geocomputation with
+    R](https://r.geocompx.org/transport.html)
+2.  Work though Sections 13.1 to 13.4 of the Transport Chapter in
+    [Geocomputation with R](https://r.geocompx.org/transport.html)
+3.  Bonus: Read more about using the [tmap
+    package](https://r-tmap.github.io/tmap/)
+4.  Bonus: Read more about the [ggplot2
+    package](https://ggplot2.tidyverse.org/)
+5.  Bonus: Read Chapter 7 & 8 of [Geocomputation with
+    R](https://r.geocompx.org/transport.html)
+
+# 6 References
+
+<div id="refs" class="references csl-bib-body hanging-indent">
+
+<div id="ref-grolemund_r_2016" class="csl-entry">
+
+Grolemund, Garrett, and Hadley Wickham. 2016. *R for Data Science*.
+O’Reilly Media.
+
+</div>
+
+</div>