‘Unicorn’ startups were as rare as the mythical creatures they are named after. As the global startup ecosystem boomed, so did the number of companies that reached the billion-dollar valuation mark. I wanted to understand where unicorn startups came from and specifically see which industries produced them. With this visualization project, I wanted to map the concentration of unicorns and the industries that produced most of them over time.
The global unicorn startup dataset I used had a detailed record of all unicorn startups that were founded between the year 2007 and 2021 across the major countries. While the dataset spans multiple continents, one of its limitations is that it does not give a complete picture of every country in the world.
The first map I made was a choropleth map that shades countries by their total unicorn valuation (in billions). Right off the bat, the United States (536 startups) stands out, dominating other countries that appear significantly lighter. Even though the unicorn economy popped up across multiple countries, this map makes the geographical inequality quite clear and shows how the growth of unicorns concentrated in a few regions. China is second on the list with 168 startups, followed by India with 63 startups. Due to the stark differences in numbers between the USA and other countries, I had to use stepped colors rather than a continuous scale to make the shades of blue more distinguishable and distributed. A continuous scale would have made the entire map appear identical with the same shade of blue with the United States standing out as the darkest shaded region. To make the map more useful, I added an industry filter so viewers can explore which countries lead in specific industries.
The second visualization asks a bigger question: Which industries within the country drove unicorn growth? My first attempt to visualize this was through a proportional symbol map showing all the different industries by scale in each country. However, in countries with a high diversity in unicorn industries, the circles overlapped and the map immediately started to look cluttered. The first version of this map made me realize how too many data points can add visual noise and prevent viewers from looking at the bigger picture.
In order to make the information clearer, I created individual maps for each industry and arranged them into a 5 x 3 grid. The one-map-per-industry view was effective in showing the geographic footprint of each sector at a glance. Splitting up the maps helped in demonstrating how smaller industries like Edtech are distributed geographically, which were previously getting lost in the combined map.
To add more context, I added line charts beside each row of the map to show the scale of growth of each industry over time. The line graphs can be used to compare growth rates of industries directly. The explosive growth of industries could clearly be visualized via the line graph. Putting these two visualizations together in a dashboard makes the bigger picture visible about how unicorn startups grew in each industry and in which geographical area they were more likely to take off. I also adjusted the bubble size across each of the map groups proportionally, so that viewers get an idea of which industry is dominant across the globe.
The third visualization is an animated map tracking the unicorn companies founded across the globe between the year 2007 and 2021. In 2007 the map appeared almost empty with just a handful of dots (representing startups) popping up here and there. As the years progress, the growth of unicorns can be seen across the major startup economies. The exponential growth of many industries is especially noticeable between the years 2018 and 2021.
A limitation of the dataset I was working with was that it had incomplete data after the year 2022, which manifested as a steep decline in unicorn startups across industries. It was interpreted by users as a drastic event that led to the sudden decline in the number of unicorn startups. To avoid misleading viewers, I chose not to represent information following the year 2021 on any of my visualizations.
Reflections:
I had a lot of fun exploring different ways of representing data geographically. I realized how powerful ‘time’ can be in the storytelling process, as it can show trends in a much more impactful manner than static maps and graphs. Working through multiple iterations of each visualization helped me showcase the story of my dataset more effectively. Trying to combine maps and charts in a single dashboard was a little challenging at first, but once it all came together, it ended up strengthening my message even more.
If I were to work on this project more, I’d want to factor in the startup outcomes or status of the unicorn companies (acquired, closed, etc) to find larger global trends.