Presenting Data
Overview
This week is focussed on formalising our understanding of how to tune matplotlib plots to ‘publication quality’ by tweaking fine-grained elements. So this week we face off with the monster that most Python visualisation libraries ultimately depend on: matplotlib.
As a tool it is both incredibly powerful and intensely counter-intuitive if you are used to R’s ggplot. We will also be looking more widely to the future of quantitative methods, the potential of a geographic data science, and the ways in which we can move between spatial and non-spatial paradigms of analysis within the same piece of work.
- You have broadened your thinking about the purpose of data visualisation.
- You are working intensively on the group project.
Preparatory Lectures
| Session | Video | Presentation |
|---|---|---|
| Data Visualisation | Video | Slides |
Other Preparation
Readings
We will briefly touch on the following readings, but they are primarily to signpost other readings on ethics and related topics that might help you with the assessment or with your reflection on the module as a whole:
| Citation | Article | ChatGPT Summary |
|---|---|---|
| Xie (2024) | URL | N/A |
| Elwood and Leszczynski (2018) | URL | N/A |
| Crawford and Finn (2015) | URL | N/A |
| Bemt et al. (2018) | URL | N/A |
Additional Resources
You might want to look at the following reports / profiles with a view to thinking about employability and how the skills acquired in this module can be applied beyond the end of your MSc:
- CARTO The State of Data Science Report <URL>
- Geospatial Skills Report <URL>
- AAG Profile of Nicolas Saravia <URL>
- Wolf et al. (2021) <URL>
While I expect most of you will be focussed on assessments, you should at least skim-read these as they may help you to reflect on what you’ve learned this term in this module and across the programme as a whole, as well as work greater reflection into the group assessment. The other Additional Resources might be useful in terms of looking at the direction of the field, the opportunities in industry, and the kinds of work that people with (sptial) data science skills can do.
Practical
The practical will lead you through the fine-tuning of data visualisations in Matplotlib/Seaborn. In many ways, this should be seen as largely a recap of material encountered in previous sessions. However, you should see this as an important step in the production of outputs and analyses needed for the final project. That said, you would be better off spending time on the substance of the report first and only turning to the fine-tuning of the visualisations if time permits.
The practical focusses on:
- Automating the production of map outputs in Python to create an ‘atlas’.
To access the practical: