Background

A very prominent feature of the dance music community is the frequent collaborations between artists, whether through original songs or through remixing other artists’ existing songs. Some artist’s impact on the genre can be positively correlated with the number of artists they collaborate with and this impact was something I wanted to quantify and show through a series of visualizations .

Project Concept

For this project I wanted to invite the user to visualize the interconnectivity of house music producers and their frequent collaborations. I chose to represent this data through a postcard inviting the user to a house party that intends to address the party’s:

Who? The list of invitees is comprised of the 82 artists featured in the network graph.
What? An event to highlight the community-based nature of house music and the frequent collaborations between artists.
Where? The most likely venue for the house party is determined by the most frequently visited music venues in NYC — which are depicted in the ‘venues map’.
From Where? Where the attendees are coming from by city is shown on the origin city map.

Source Data

To pull data for this project, I wrote a python script to scrape Spotify of all of the songs from a list of artists I define. The program then creates combinations of each artist on the songs (discarding duplicates) and saves them to a dataset. Lastly the program discards all artists with under 7 collaborations to reduce noise and also removes all artists whose genre does not fall into the house/dance genre. The resulting dataset is a list of ‘relations’ (between ‘Artist 1’ and ‘Artist 2’) is saved to a CSV file that can be uploaded to Gephi to create visualizations. The artists I chose to filter on are 17 artists that I frequently listen to that cover a wide range of micro-genres within the Dance genre. This introduces a bias to the dataset because it is not representative of the entire industry but rather a subset of artists I listen to and the communities they belong to. 

For the maps, I manually collected data from the Artists’ Wikipedia pages for their city of origin and from the website Concert Archives for the artists performance history at NYC venues. For the venues map, I created a spreadsheets with each artist’s name, date of performance, and venue name for each performance, I then created a python script to fetch the coordinates of each venue using the Google Map API, which could then be plotted in Tableau Public. For the origin cities map, I only need the name of each artist and the city name from where they are from for Tableau to recognize and plot the data.

Now let’s zoom in…

Components

Overall Network

The centerpiece visualization is the main network graph, depicting the overall state of the collaborations between the selected artists. Each community is colored with a different color and if there is a large central artist (or artists!), they are given a custom icon. The size of the icons are determined by the number collaborations that artist has and the thickness of the lines shows the number of collaborations between two artists. Some notable songs are highlighted as if written in on the postcard and overall explanations for the graph are written in text blocks surrounding the image in order to help guide the user.

To interpret the graph, users can find the central producers by looking for the custom icons and then looking at the lines that exit their bubble to see their reach. Chris Lake was no surprise to be central to all communities as his influence can be seen through most new artists and is one of the most impactful artists of the past decade. This graph displays this well giving him center stage (literally) with so many connections both inside and outside of his community. The dominance of Green Velvet is also apparent through the graph as he has been a staple to the industry for a much longer amount of time than some of the other artists. This version of the graph, however, does not articulate the added dimension of length of time in the industry – which would be better seen in a breakdown by time.

Time Networks

I wanted to show the progression of certain artists and their communities over the past decade. To do this, I wanted to create small multiples of different time periods to show the different eras. I went back and forth between having each multiple be cumulative or distinct time periods and eventually landed on distinct time periods – 2015-2018, 2019-2021, 2022-2025. Users can track certain artists and see their growth within the industry as their collaboration and reach grows. For example in the first time period, John Summit is one of the smallest artists in a small, isolated community. By the second period, you can see where his presence in the industry grew to have his own community that is interconnected with others. By the final period, though his community is a bit smaller, you can still see how established he is and interconnected with other communities. This helps illustrate how 2020 was John Summit’s breakout year into mainstream popularity.

Artist Bar Charts

After conducting user testing of the network graphs, the main concern that I heard was that it was overwhelming to interpret on its own. After trying different layouts and different textual guides, the users were still feeling overwhelmed when trying to understand the network. This led me to add the same information in bar chart form, since most users are already familiar with bar charts. This had the desired effect, where users responded positively to being able to use the bar chart and overall statists to help make sense of the network, without getting frustrated and lost. The bars are colored according to the communities found in the network graph to maintain the same groupings of artists.

Venue Map + Origin City Map

On the right side of the postcard, there are two maps showing venues in NYC and the origin cities of the featured artist. The first map (NYC Music Venues) has bubbles for popular music venues in NYC — the size of the bubble representing the number of performances from the features artists since 2022. The map shows where there are ‘hotspots’ for music venues like Midtown Manhattan and Bushwick, which is highlighted by an inset map of the area. Following with the theme of a handwritten postcard, there are examples of notable collaboration performances at some of the venues.

The Artist’s Country of Origin map highlights where the artists are from and aims to shows which countries are producing the most house music producers from the network. The map points out Chicago and London, two majors cities that many artists are from. The title also highlights the largest countries which are the United Kingdom and the United States.

Reflection

Overall, I am happy with the way this project turned out. I was hoping to simulate a real party invitation and make the data feel interactive and fun, without losing meaning. In future iterations, I would be interested in expanding the project to be representative of generally popular artists rather than based solely on artists I listen to. Additionally, I would be interested to see how applicable these graphs would be for a different genre that does not have as strong of a culture of collaborations.