Please visit this repository on GitHub for detailed information about this project. You can also refer to this link for the interactive map.
Please note that this is still an on-going project and it subject to change.
Introduction
This project uses transportation and its related datasets as a portal, a thread, and a platform to dig a narrative from public governmental datasets, revealing something that was not often being noticed, namely the night (when compared to daytime) bus in London – how it relates to the conditions of people living in this metropolitan that is concealed by night, buses, and maps.
This project is constructed using R programming language with few necessary packages, mainly shiny and tidyverse. At the current stage, it is represented as an interactive map with multiple layers, allowing users to switch between layers to form their own interpretations. This project is planned to transform to and create a scrolly-telling website, enabling more information to be displayed and interactivity to be integrated.
This is also a part of my major project towards a master’s degree at Goldsmiths, University of London.
How to present this project
The image above is the interface of the map. In the up-right corner is the controlling panel for layers. These layers visualise the density of bus stops according to London wards and LSOAs, the income deprivation rankings, and the price paid to purchase a property in 2023 in selected areas by colours.
Users can treat it as a typical map website specifically for the Greater London area but with four additional layers on top of an ordinary open-street map. What is essential for these layers is that they are critical aspects of this project, which create the correlation when the user wanders on the map, switching between different layers, dragging between different areas, and hovering in various locations.
This project can also be present in a physical space – see images above – and this is how I do it.
I set the project in a relatively dark space to mimic a night environment, with multiple screens placed around a chair to simulate the experience of riding a bus. The side screen here serves as a metaphor, where the videos played in the virtual window of the player application are the videos I shot through the actual window of the bus. Here, the virtual window and the actual window are collapsed into one that exists digitally on the screen and can be played and replayed over and over again with this stacked and removable existence.
The projection in front of the audience is installed this way on purpose. When the audience sits in the chair, their shadows can be projected on the wall to create a feeling that they are also riding the bus at night with other passengers and generate connections between their bodies, the screen in front of them, and the projection behind them.
Findings: Bus as a platform
I have affinities toward buses, and I believe they can be a platform to study, and they are a unique existence for a city. When riding a bus, you are experiencing a kind of in-betweenness: between your place of departure and destination through time, between the outside city scenes and inside bus environment through windows, between the person sitting on your right side and left side through your body, and of course, between people and location through data. It is this kind of in-betweenness that makes the “bussing” unique and explorable.
At the current stage, with current layers, this project’s findings are universal but still provoking.
- Overall, no matter whether viewed by wards or LSOAs, night bus stations tend to concentrate on Central London areas and spread out to the suburbs, or vice versa. Moreover, the LSOA layer provides an interesting perspective that the area with the most densely populated night bus stations in the central area (City of London 001F) is the place that has fewer people living there, which can be evidenced by its large size.
- The centre-edge situation for night bus stations is not evenly distributed, as some suburban areas are populated with night bus stations while others are free of them. More interestingly, night-bus-hot areas tend to construct a line, while some night-bus-zero areas can merge into one larger area.
- As for the correlations, it is clear in the comparison between multiple layers that when an area has more night bus stops, the residents there tend to have less income, and the property prices tend to be lower than in other areas without night bus stations. Besides, when viewing the LSOA level, the lines constructed by a sequence of night bus stations tend to overlap with the lines connected by adjacent areas with high income deprivations.