9 Beyond the Choropleth
This week, we turn to bivariate mapping, using unemployment change across London as our example.
In these last two weeks, we work with the help of an AI assistant, Microsoft Copilot, rather than working through a fully worked example. You will be given real data, an end goal, and Copilot as a working partner, and asked to develop your own approach. We focus on producing visualisations here, but the same principle applies to any other way you might use these tools: check what a tool produces and understand what you are doing, including any intermediate steps.
9.1 Lecture slides
You can download the slides of this week’s lecture here: [Link].
9.2 Reading list
Essential readings
- Longley, P. et al. 2015. Geographic Information Science & Systems, Chapter 11: Cartography and Map Production, pp. 1-32. [Link]
Suggested readings
- Cheshire, J. and Uberti, O. 2021. Atlas of the invisible: maps and graphics that will change how you see the world. London: Particular Books.
- Wickham, H., Çetinkaya-Rundel, M., and Grolemund, G. R for Data Science. 2nd edition. Chapter 19: Functions. [Link]
9.3 Coding with Copilot
Let’s say you need to find the distance between a few London landmarks.
| File | Type | Link |
|---|---|---|
| London Landmarks | csv |
Download |
Load the file, and use Copilot to work out the distance between the London landmarks.
Example prompt: “How to calculate the distance between two points in R?”
What does the result actually show? Does it seem right, and how would you know?
9.4 Assignment
For the main part of this week, you will work independently with Copilot on a real analytical question: how has unemployment changed across London between the 2011 and 2021 Census?
The data are provided at Lower Super Output Area (LSOA) level for both years.
Administrative geographies, such as LSOAs, are periodically updated to reflect changes in population and other factors, resulting in occasional boundary adjustments. As a result, the 2011 and 2021 datasets do not use the same set of LSOAs, and cannot simply be joined on a shared code. A lookup table connecting the two is provided below, and you will need to use it to link the datasets together.
You can download the files below and save them in your project folder under data.
| File | Type | Link |
|---|---|---|
| London LSOA Census 2011 Unemployment | csv |
Download |
| London LSOA Census 2021 Unemployment | csv |
Download |
| England and Wales LSOA 2011-2021 Lookup | csv |
Download |
| London LSOA 2021 Spatial Boundaries | GeoPackage |
Download |
Your task is to produce a bivariate map showing how unemployment in 2011 relates to unemployment in 2021 across London. Use the lookup table to link the two datasets onto a shared geography, calculate the change, and map the result in a way that lets you see where things have improved, worsened, or stayed much the same.
Work through this with Copilot: ask it for a starting point, run what it gives you, and check the result at every step rather than assuming it is right.
Check whether the number of LSOAs you are working with looks right after each step. It is easy to end up with more rows than you started with, or fewer, without noticing.
9.5 Before you leave
That is it for today. Next week, we build on this with some further visualisation work, again working with Copilot rather than following a worked example. Onwards.