We choose our music to best fit our mood, our spirit or our activities. We use it to celebrate
our victories, to soothe our pain or to give us energy when we are feeling down. This is why ensuring that we can take our music everywhere with us has become one of the objectives
of technological development. Building on these premises we propose to create a visualisation
of the ‘pace’ of the city through an analysis of people’s music listening habits in different parts
of the world; a kind of ‘heartbeat of the city’ visualisation. This ‘map’ can be used in order for one
to choose the place that is in tune with their heart.
Inspired by the ‘Hey You! What Song are you listening to?’ video by Tyler Cullen, we created the City Vibes project in order to map the BPM trends of the songs listened to in 12 cities around the world: New York, Rio de Janeiro, London, Amsterdam, Paris, Rome, Prague, Moscow, Mumbai, Bangkok, Sydney and Tokyo. For this we used the #nowplaying hashtag and the geo-location features
of Twitter to collect the data for the 12 urban spaces. The next step was to build an app using the API of Echo Nest – a technology capable of processing large catalogues of dynamic music data by music content (how the music sounds) and music culture (how the entire online world describes the music) and offer specific audio attributes for each song. Through this app we created an insightful database of the tempos and energy scores for the songs collected. The final result is a visualization where you can see and hear the average BPM and Energy score for the ‘pace’ of every city, for each hour
of the day. The user interface is simple and intuitive due to the use of such visualization tools
as Google Maps, embedded with YouTube videos that recreate the heartbeats of the cities at their average BPMs, and the StatPlanet application, which visualizes the trend of the BPMs hour by hour.
Our hypothesis is that the types of songs being listened to are related to the general atmosphere
of that city. For example, through our visualization we can observe that Bangkok has the highest average BPM and Rio de Janeiro has the highest Energy scores, but also that Amsterdam is the ‘perfect’ party destination with the highest BPMs and Energy scores during the night time, followed closely by New York and Tokyo. We can also identify a trend for the majority of the cities, with 4 periods of the day having a consistent density of high BPMs and Energy scores: at the first hours of the morning, at noon, close to the end of the working day (16:00 – 18:00) and, of course, during the night time (21:00 – 2:00 AM).
At this point, the data was collected manually for a period of 24 hours, but a step of taking this project further, to a more sociological accurate analysis, would be the collection of data in real time, throughout longer periods of time. Besides the Twitter hashtag, which can be difficult to assess due to the high forms of expressions of the natural language, the data collection process can be improved by using such online music listening services as Spotify, Last.fm, Shazam, and SoundCloud.
Authors: Natalia Miszczak, Clement Adam, Andrian Georgiev, Liam Voice, Andrei Florian