The Kickstart Projects in visualization

Charts & Graphs, Lab Reports

Kickstarter was once a fed a few years ago, most of my designer friends/colleagues who work in the tech industry at least pledge money to one Kickstarter project, and only a few of them received the product they supported.
Thus, I’d like to take a look at the Kickstarter data to check some information about the project categories, how many funds those projects had been received, when is the upsurge of the Kickstarter that everyone was crazy about pledging money into any of the projects, and the status of those projects on 2018.

Tools and datasets

For this information visualization experiment, I got the dataset from Kaggle- an online community for data enthusiasts to find or publish data for the use of machine learning training.
This Kickstarter Project dataset collects the data in between 2009-2018, variables include internal Kickstarter id, the name of projects, main and sub-category, the deadline for crowdfunding, fundraising goal, date launched, amount pledged by “crowd”, the condition the project is in before 2019, number of backers, country pledged from, and the amount of money pledged from crowd. I’ll only focus on playing with certain variables such as amount pledged by “crowd”, the condition the project is in before 2019, and the project categories in order to get the overview of the information I mentioned in the introduction.
The tool for visualizing data, I use Tableau Public to execute this experiment. Tableau Public is a free data viz tool that available for both PC and Mac OS, the way of using Tableau Public for info viz creation is pretty easy and understandable. All you have to do just to clean up the dataset table a little bit before import (or connect) to Tableau, and then drag and drop the variables in the “canvas” to tell your data viz stories!


I am really into these type of information visualization design, so I didn’t even check if Tableau Public can make similar design or not. These type of graphs might be called infographic junks since the audience can’t really read the information and have an overview understanding of the graph. In this case, the composition or the form of those info viz creations are more likely be defined as “artworks”. There are no scale and axis in two of these graphs, which might cause a longer time for the audience to interpret the information, also, there is no annotation for any highlight in two graphs, that also make the reading experience a bit plain, because there is no up and down for visual storytelling. One graph that uses a lot of lines with annotated info is informative but the visual is a bit overwhelming when it’s not interactive.
I personally would still want to create this type of graphs one day, but might use the dataset for abstract topics like art or philosophy related topic.

1.What to tell

Deciding what to tell in those graphs was the first challenge in my mind. My dataset has a bunch of useful variables that I can play with, but the things I care about the most are the current status of the projects and how much money they’ve got from the crowd, so I decided to go for this direction and tell the audience about it.


I’ve tried to play with the data with different graph style like bar, line, dot with sizes and colors, the challenge here was how to make my data set readable or understandable but also beautiful. When I started to design, I thought it might be a good idea to present all the project name and the related info, but the program can’t display and match more than 1000 rows, so I eventually gave up.


I decided to tell a story by presenting a series of information visualization graphs from where do people pledge the money from, what category those “backers” interested in, the amount of money the crowd pledged for different categories, the status of those projects in different categories, and the peak of the Kickstarter money pledging activity.

This story starts from an overview of people in what country might be really interested in crowdfunding projects, and it seems like people in Europe are really into pledging money to the project, North America for sure is on the list since that’s where Kickstarter from. There are few countries in Asia engaged in this activity but not the mainstream. Seems like South America and Africa didn’t involve at all.

Then take a look at the categories and amount of pledged money from different country, people in the US are interested in various topics and tend to pledge more money into projects the most, the second country is the UK but there is a huge reduction for both the total amount of pledge money and the number of categories.

Category Technology not surprisingly is the top category that people tend to pledge the money in, the most popular one is the app project, which is pretty much how people now think of what technology is. The second and third are the Games and Film & Video, video game and documentary are the top topics that people love to support.

Here comes the information I’d like to know the most- the status of those projects! I’ve never funded any project because I just don’t trust the quality of the production, also, there is a huge chance that the project might not be able to deliver the final product, so to me, that’s more like a fraud. Based on the graph, those most funded categories like “apps” or “video games” tend to have a higher rate of failure than success, and the total amount of projects in these categories are way higher than other categories. Also, the Product Design category has the most filed projects, this surprised me a bit since my impression of product failure happens more on software than other types.

Last, the peak of Kickstarter activity happened in 2015, and Technology for sure took the most part of it. We can see the growth started in 2012 and there was a big leap in 2014, and there was a sudden reduction happened over the peak in 2017. This matches the frequency of people to share their supported projects on social media of my circle, now only a few of my friends still interested in crowdfunding projects.


In the very beginning of this experiment, I was so obsessed about those information graphics that have a complex visual element and are hard to read. I was more into the “form” than “function” of those graphics. But as a designer, I’d prefer to create a design that is meaningful and understandable, but not just a fancy info junk. Thus, I stick with this notion to make sure my creation can be interpreted correctly by the audience.
For my future information visualization creation, I’d like to learn more about how to tell a comprehensive story based on the dataset I have, with an understandable but beautiful presenting approach, to let the form follows the function, not the opposite.