Group Name’s Group Project

Declaration of Authorship

We, [insert your group’s names], pledge our honour that the work presented in this assessment is our own. Where information has been derived from other sources, we confirm that this has been indicated in the work. Where a Large Language Model such as ChatGPT has been used we confirm that we have made its contribution to the final submission clear.

Date:

Student Numbers:

Brief Group Reflection

What Went Well	What Was Challenging
A	B
C	D

Priorities for Feedback

Are there any areas on which you would appreciate more detailed feedback if we’re able to offer it?

Response to Questions

See the raw file for examples of how to hide computational output as there is code hidden here.

1. Who collected the InsideAirbnb data?

( 2 points; Answer due Week 7 )

An inline citation example: As discussed on “Inside airbnb” (n.d.), there are many…

A parenthetical citation example: There are many ways to research Airbnb (see, for example, “Inside airbnb,” n.d.)…

2. Why did they collect the InsideAirbnb data?

( 4 points; Answer due Week 7 )

One of way to embed output in the text looks like this: after cleaning, we were left with 85,127 rows of data.

This way is also supposed to work ({python} f"{df.shape[0]:,}") but I’ve found it less reliable.

3. How did they collect it?

( 5 points; Answer due Week 8 )

4. How does the method of collection (Q3) impact the completeness and/or accuracy of the InsideAirbnb data? How well does it represent the process it seeks to study, and what wider issues does this raise?

( 11 points; Answer due Week 9 )

5. What ethical considerations does the use of the InsideAirbnb data raise?

( 18 points; Answer due Tuesday, 17 December 2024 )

6. With reference to the InsideAirbnb data (i.e. using numbers, figures, maps, and descriptive statistics), what does an analysis of Hosts and the types of properties that they list suggest about the nature of Airbnb lettings in London?

( 15 points; Answer due Tuesday, 17 December 2024 )

7. Drawing on your previous answers, and supporting your response with evidence (e.g. figures, maps, EDA/ESDA, and simple statistical analysis/models drawing on experience from, e.g., CASA0007), how could the InsideAirbnb data set be used to inform the regulation of Short-Term Lets (STL) in London?

( 45 points; Answer due Tuesday, 17 December 2024 )

Sustainable Authorship Tools

Using the Terminal in Docker, you compile the Quarto report using quarto render <group_submission_file>.qmd.

Your QMD file should automatically download your BibTeX and CLS files and any other required files. If this is done right after library loading then the entire report should output successfully.

Written in Markdown and generated from Quarto. Fonts used: Spectral (mainfont), Roboto (sansfont) and JetBrains Mono (monofont).

References

“Inside airbnb” (n.d.). Available at: http://insideairbnb.com.