Introduction
This visualisation is an exploration of the Airbnb data in Paris. It analysis the spread of the Airbnb’s as per locality and then further explores proximity to the metro as a parameter. The overlay of the road network map gives idea of proximity to roads and not just the metro. Finally the overlay of the arrondissements map gives an idea of the spread of these Airbnb’s across different localities with transportation as a key data point.
Dataset
Airbnb csv data for Paris – https://www.kaggle.com/datasets/thedevastator/airbnb-prices-in-european-cities
Paris road network shapefile – https://geodata.lib.utexas.edu/catalog/stanford-db591kz1574
Paris arrondissements shapefile – https://data.metabolismofcities.org/library/maps/38838/
The data sheet explores Airbnb data in Europe in terms of prices and has city-wise data available as csv files. The data was accessed using Kaggle. The datasheet was a fairly tidy one and didn’t require much cleaning. The data covers the following attributes.
| Column name | Description |
| realSum | The total price of the Airbnb listing. (Numeric) |
| room_type | The type of room being offered (e.g. private, shared, etc.). (Categorical) |
| room_shared | Whether the room is shared or not. (Boolean) |
| room_private | Whether the room is private or not. (Boolean) |
| person_capacity | The maximum number of people that can stay in the room. (Numeric) |
| host_is_superhost | Whether the host is a superhost or not. (Boolean) |
| multi | Whether the listing is for multiple rooms or not. (Boolean) |
| biz | Whether the listing is for business purposes or not. (Boolean) |
| cleanliness_rating | The cleanliness rating of the listing. (Numeric) |
| guest_satisfaction_overall | The overall guest satisfaction rating of the listing. (Numeric) |
| bedrooms | The number of bedrooms in the listing. (Numeric) |
| dist | The distance from the city centre. (Numeric) |
| metro_dist | The distance from the nearest metro station. (Numeric) |
| lng | The longitude of the listing. (Numeric) |
| lat | The latitude of the listing. (Numeric) |
The shape files for mapping were obtained from 2 different sources, one with the roadway mapping and one with the region/arrondissement mapping.
Tools -used
Tools that were used to make this visualisation were Excel, Open Refine, QGIS and Figma. The report was made and published using WordPress.
Process
The making of this visualisation required tools like OpenRefine, excel, QGIS and Figma. Each tool served a separate purpose
Research
The first phase of my process involved researching for data sources that had geographic attributes as a key parameter. I started looking at housing data and then found travel and bnb data in this domain which further intrigued my interest.
My second phase of research involved looking for open-source shapefiles for maps of Paris and I came across a road-network file which I thought could add a lot of perspective to my visualisation and finally found a locality map as well.
Open-Refine- Data Cleaning
I used Open Refine to clean the data. The data was fairly clean, hence I just exported it as a csv
QGIS Data Representation
I had to import the csv in QGIS and add layers of the road network shape file and arrondissements shape file.
Visualisations & Observations
1. Airbnb locations – I overlaid the Airbnb locations over the arrondissement map to get a visualisation of the spread. I used dots to represent the location
2. Metro proximity – I used an expression to highlight metro areas and specifically mark airbnb’s with proximity less than 200 mts to the metro.
if( "metro_dist" <0.2, "metro_dist",0)
3. Metro proximity and road network – I overlaid the above mappings on the road-network map by exporting files in the same dimensions and using Figma to overlay them.
Reflection and Critique
Limitations
QGIS is an open-source software with documentation that is quite detailed for a few cases and minimal for a few others. The software sometimes crashes and I had challenges with importing .shp files. It also sometimes renders .shx in different co-ordinates which again was a challenge while producing this visualisation.
Positives
QGIS is a good tool for geo-spatial visualisations, simple expressions can help generate a lot of insightful data. The layering feature is quite powerful for geographic visualisation of data.
Peer Critique and changes
My final chart had some issues with overlay and it was suggested to me that I find a way to overlay a map without which the visualisation looks incomplete. I worked on it using Figma as a tool and I also eventually found a locality map which matched the co-ordinate system of my data thus improving the visualisation.
Bibliography
Airbnb Prices in European Cities. (n.d.). Retrieved April 11, 2023, from https://www.kaggle.com/datasets/thedevastator/airbnb-prices-in-european-cities
Vasserot Road Network (1810-1836) – University of Texas Libraries GeoData. (n.d.). Retrieved April 11, 2023, from https://geodata.lib.utexas.edu/catalog/stanford-db591kz1574
Arrondissements de Paris | Metabolism of Cities Data Hub. (n.d.). Retrieved April 11, 2023, from https://data.metabolismofcities.org/library/maps/38838/
QGIS Tutorials and Tips — QGIS Tutorials and Tips. (n.d.). Retrieved April 11, 2023, from https://www.qgistutorials.com/en/index.html