11-datasets.qmd

# Data Sets {#sec-chp11}

```{r}
#| warning: false
library(sf)
```

## Madrid AirBnb

This dataset contains a pre-processed set of properties advertised on the AirBnb website within the region of Madrid (Spain), together with house characteristics.

### Availability {.unnumbered}

The dataset is stored on a Geopackage that can be found, within the structure of this project, under:

```{r}
path <- "data/assignment_1_madrid/madrid_abb.gpkg"
```

```{r}
db <- st_read(path)
```

### Variables {.unnumbered}

For each of the `r length(db)` properties, the following characteristics are available:

-   `price`: \[string\] Price with currency
-   `price_usd`: \[int\] Price expressed in USD
-   `log1pm_price_usd`: \[float\] Log of the price
-   `accommodates`: \[integer\] Number of people the property accommodates
-   `bathrooms`: \[integer\] Number of bathrooms
-   `bedrooms`: \[integer\] Number of bedrooms
-   `beds`: \[integer\] Number of beds
-   `neighbourhood`: \[string\] Name of the neighbourhood the property is located in
-   `room_type`: \[string\] Type of room offered (shared, private, entire home, hotel room)
-   `property_type`: \[string\] Type of property advertised (apartment, house, hut, etc.)
-   `WiFi`: \[binary\] Takes `1` if the property has WiFi, `0` otherwise
-   `Coffee`: \[binary\] Takes `1` if the property has a coffee maker, `0` otherwise
-   `Gym`: \[binary\] Takes `1` if the property has access to a gym, `0` otherwise
-   `Parking`: \[binary\] Takes `1` if the property offers parking, `0` otherwise
-   `km_to_retiro`: \[float\] Euclidean distance from the property to the El Retiro park
-   `geom`: \[geometry\] Point geometry

### Projection {.unnumbered}

The location of each property is stored as point geometries and expressed in longitude and latitude coordinates:

```{r}
st_crs(db)
```

### Source & Pre-processing {.unnumbered}

The data are sourced from [Inside Airbnb](http://insideairbnb.com/). A Jupyter notebook in Python (available at `data/assignment_1_madrid/clean_data.ipynb`) details the process from the original file available from source to the data in `madrid_abb.gpkg`.

## England COVID-19

This dataset contains:

-   daily COVID-19 confirmed cases from 1st January, 2020 to 2nd February, 2021 from the [GOV.UK dashboard](https://coronavirus.data.gov.uk);

-   resident population characteristics from the 2011 census, available from the [Office of National Statistics](https://www.nomisweb.co.uk/home/census2001.asp); and,

-   2019 Index of Multiple Deprivation (IMD) data from [GOV.UK](https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019) and published by the Ministry of Housing, Communities & Local Government.

The data are at the Upper Tier Local Authority District (UTLAD) level - also known as [Counties and Unitary Authorities](https://geoportal.statistics.gov.uk).

### Availability {.unnumbered}

The dataset is stored on a Geopackage:

```{r}
sdf <- st_read("data/assignment_2_covid/covid19_eng.gpkg")
```

### Variables {.unnumbered}

The data set contains 508 variables:

-   `objectid`: \[integer\] unit identifier
-   `ctyua19cd`: \[integer\] Upper Tier Local Authority District (or Counties and Unitary Authorities) identifier
-   `ctyua19nm`: \[character\] Upper Tier Local Authority District (or Counties and Unitary Authorities) name
-   `Region`: \[character\] Region name
-   `long`: \[numeric\] longitude
-   `lat`: \[numeric\] latitude
-   `st_areasha`: \[numeric\] area in hectare
-   `X2020.01.31` to `X2021.02.05`: \[numeric\] Daily COVID-19 cases from 31st January, 2020 to 5th February, 2021
-   `IMD...Average.score` - `IMD.2019...Local.concentration`: \[numeric\] IMD indicators - for details see [File 11: upper-tier local authority summaries](https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019).
-   `Residents`: \[numeric\] Total resident population
-   `Households`: \[numeric\] Total households
-   `Dwellings`: \[numeric\] Total dwellings
-   `Household_Spaces`: \[numeric\] Total household spaces
-   `Aged_16plus` to `Other_industry`: \[numeric\] comprise 114 variables relating to various population and household attributes of the resident population. A description of all these variables can be found [here](data/assignment_2_covid/census_vars.csv)
-   `geom`: \[geometry\] Point geometry

### Projection {.unnumbered}

Details of the coordinate reference system:

```{r}
st_crs(sdf)
```