Selecting Data
Overview
- You have formalised your understanding of how to link data in Python.
- You are working on the group project.
Preparatory Lectures
You are strongly advised to watch these videos on linking data and spatial data, as well how to group data within pandas; however, you will not be asked to present any of these because our attention is now on the final assessments. As well, you should by now be familiar with the concept of how to join data from the GIS module (CASA0005), so this focusses on two things: 1) how to do this in Python (with a bit of SQL thrown in); and 2) how to approach this process with large or mismatched data sets.
| Session | Video | Presentation |
|---|---|---|
| Linking Data | Video | Slides |
| Linking Spatial Data | Video | Slides |
Other Preparation
Readings
Come to class prepared to discuss the following readings:
| Citation | Article | ChatGPT Summary |
|---|---|---|
| Elwood and Wilson (2017) | URL | N/A |
| O’Sullivan and Manson (2015) | URL | N/A |
| Mattern (2015) | URL | N/A |
Study Guide
- How do the concepts of “physics envy” and “geography envy” relate to the evolution of GIScience and the increasing use of urban dashboards?
- Compare and contrast the “Week 10: Ethics” approach to critical GIS with the integrated approach advocated by Elwood and Wilson (2017). What are the strengths and weaknesses of each approach?
- Mattern (2015) argues that urban dashboards can obscure the complexity of cities by “bracketing out” certain variables and simplifying representations. How does this critique connect to the concerns raised by Elwood and Wilson (2017)?
- How does the emphasis on generalization in physics-based approaches to social phenomena highlighted by @O’Sullivan and Manson (2015) challenge traditional geographical perspectives that prioritize local and particular knowledge?
- What are the shared concerns and potential synergies between the arguments of Elwood and Wilson (2017) and O’Sullivan and Manson (2015)?
Here we focus on what you can now bring to the table that might help you to dinstinguish yourself from someone who did a ‘data science degree’; through what we study here (and in your other modules) you have been exposed to ways of thinking about data critically and ethically that are rarely part of an Informatics or Machine Learning degree. But as we hope you’re now conviced: these things matter. It’s not just that being critical and ethical is a good way to do your job (whatever that might end up being), it’s that being critical and ethical is a good way to do your job better. You will writing better code. You will write better assessments. You will draw better conclusions.
Practical
The practical will lead you through the selection of data in pandas and the equivalent using SQL via DuckDB.
The practical focusses on:
- Comparing different approaches to data linkage
- Linking data as part of a visualisation process.
To access the practical: