Skip to content

Latest commit

 

History

History
83 lines (59 loc) · 2.83 KB

exercises2.md

File metadata and controls

83 lines (59 loc) · 2.83 KB

Exercises day 2

1 Recap and point data

  1. Start off by attaching the tidyverse with the library() function.

  2. Create (or recreate) an object called ac_wy with the command read_csv() (you may need to first download the file from https://github.com/ITSLeeds/highways-course/releases )

  3. What is the class of the ac_wy?

  4. Create a plot of dataset using ggplot2, resulting in a plot that looks a little like this (hint: set transparency with alpha in geom_point(), fix the coordinates with + coord_equal()):

  5. Building on the previous plot, make the size of each dot proportional to the number of people involved (Number_of_Casualties).

  6. Filter the dataset so that it contains only rows with Accident_Severity values of Killed or Serious (hint: != "Slight" may help!).

  7. Further reduce the dataset size by selecting only the following columns for further analysis: Accident_Index, Location_Easting_OSGR, Location_Northing_OSGR, Accident_Severity, Date, Speed_limit.

  8. Create a new plot showing the distribution of speed limits.

  9. Load the sf library.

  10. reate an object called ac_wy_sf, a spatial version of the ac_wy dataset read-in previously (hint: use the function st_as_sf() and coordinate variables Location_Easting_OSGR/Location_Northing_OSGR — set the CRS with crs = 27700)

  11. Plot the result with tmap, showing accident severity (hint: the code below plots the result for the speed limit).

    library(tmap)
tm_shape(ac_wy_sf) +
    tm_dots("Speed_limit")

  12. Set the mode of tmap to viewing with tmap_mode("view") and run the previous command again to create an interactive map.

  13. Use this to visually identify a junction with many crashes.

  14. Read-in a dataset representing Leeds with the following command (note: you need to have downloaded the file from https://github.com/ITSLeeds/highways-course/releases ):

    leeds = readRDS("leeds.Rds")

  15. Find the CRS of leeds with st_crs()

  16. Transform the CRS to OSGB with the command st_transform() (hint: OSGB has the EPSG code 27700).

  17. Find out how many fatal and serious crashes happened in each of the MSOA areas in Leeds

Advanced

  • Advanced 1: Find out which MSOA zone had the highest average number of people in crashes
  • Advanced 2: Look-up packages for clustering (hint: look at https://geocompr.github.io/geocompkg/articles/ )
  • Advanced 3 (difficult): Aggregate point to regular grid cells