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Group_Work.qmd
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Group_Work.qmd
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---
bibliography: bio.bib
csl: harvard-cite-them-right.csl
title: Group Name's Group Project
execute:
echo: false
freeze: true
format:
html:
theme:
- minty
- css/web.scss
code-copy: true
code-link: true
toc: true
toc-title: On this page
toc-depth: 2
toc_float:
collapsed: false
smooth_scroll: true
pdf:
include-in-header:
text: |
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---
## Declaration of Authorship {.unnumbered .unlisted}
We, [insert your group's names], confirm 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?
```{=html}
<style type="text/css">
.duedate {
border: dotted 2px red;
background-color: rgb(255, 235, 235);
height: 50px;
line-height: 50px;
margin-left: 40px;
margin-right: 40px
margin-top: 10px;
margin-bottom: 10px;
color: rgb(150,100,100);
text-align: center;
}
</style>
```
{{< pagebreak >}}
# Response to Questions
```{python}
import os
import pandas as pd
```
```{python}
host = 'https://orca.casa.ucl.ac.uk'
path = '~jreades/data'
file = '2022-09-10-listings.csv.gz'
url = f'{host}/{path}/{file}'
if os.path.exists(file):
df = pd.read_csv(file, compression='gzip', low_memory=False)
else:
df = pd.read_csv(url, compression='gzip', low_memory=False)
df.to_csv(file)
```
## 1. Who collected the data?
::: {.duedate}
( 2 points; Answer due Week 7 )
:::
An inline citation: As discussed on @insideairbnb, there are many...
A parenthetical citation: There are many ways to research Airbnb [see, for example, @insideairbnb]...
## 2. Why did they collect it?
::: {.duedate}
( 4 points; Answer due Week 7 )
:::
```{python}
print(f"Data frame is {df.shape[0]:,} x {df.shape[1]:,}")
```
```{python}
ax = df.host_listings_count.plot.hist(bins=50);
ax.set_xlim([0,500]);
```
## 3. How was the data collected?
::: {.duedate}
( 5 points; Answer due Week 8 )
:::
## 4. How does the method of collection impact the completeness and/or accuracy of its representation of the process it seeks to study, and what wider issues does this raise?
::: {.duedate}
( 11 points; Answer due Week 9 )
:::
## 5. What ethical considerations does the use of this data raise?
::: {.duedate}
( 18 points; Answer due {{< var assess.group-date >}} )
:::
## 6. With reference to the data (*i.e.* using numbers, figures, maps, and descriptive statistics), what does an analysis of Hosts and Listing types suggest about the nature of Airbnb lets in London?
::: {.duedate}
( 15 points; Answer due {{< var assess.group-date >}} )
:::
## 7. Drawing on your previous answers, and supporting your response with evidence (e.g. figures, maps, and statistical analysis/models), how *could* this data set be used to inform the regulation of Short-Term Lets (STL) in London?
::: {.duedate}
( 45 points; Answer due {{< var assess.group-date >}} )
:::
## Sustainable Authorship Tools
Your QMD file should automatically download your BibTeX file. We will then re-run the QMD file to generate the output successfully.
Written in Markdown and generated from [Quarto](https://quarto.org/). Fonts used: [Spectral](https://fonts.google.com/specimen/Spectral) (mainfont), [Roboto](https://fonts.google.com/specimen/Roboto) (<span style="font-family:Sans-Serif;">sansfont</span>) and [JetBrains Mono](https://fonts.google.com/specimen/JetBrains%20Mono) (`monofont`).
## References