Subject Matter:
The World Happiness Report 2021 focuses on the effects of COVID-19 and how people all over the world have fared. In Chapter 3, World Happiness Report 2021 explored how the East Asian countries or regions had dealt with the pandemic and how multiple factors had affected emotional well-being. This chapter captured my interest.
While Chapter 3 of World Happiness Report 2021tried to explain East Asia’s success of containing the spread of covid 19 and talked less about happiness ladder scores, I aim to make a data visualization piece to show an overview of “how happy” East Asian countries are under covid 19.
Goals
The goal of the project is to provide an overview of “how happy” East Asian countries are as to the general public.
To be specific, the primary goal is to present how East Asian countries performed on the happiness ladder score and the 6 factors the World Happiness Report used to explain those happiness scores(1. Log GDP per capita 2. Social support 3. Healthy life expectancy 4.Freedom to make life choices and 5.Generosity 6. Perception of corruption).
My audience, in short, are anyone who has an interest to learn more about East Asia’s happiness under covid 19. They might have no previous experience of reading data visualization works or have a certain degree of experience in data analysis.
My outcome would be a poster which can be either printed or viewed on a laptop screen.
Data
To start with, I retrieved data from the World Happiness Report official site, which includes data concerning:
1. Data panel including past world happiness report data from 2015-2020: Data Panel
2. World wide happiness data based on 2020 Gallup World Poll: Data for Figure 2.1
The original happiness score data come from the Gallup World Poll surveys from 2018 to 2020. They are based on answers to the main life evaluation question asked in the poll. The poll used the Cantril ladder, which asks respondents to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale.
The sub bars show the estimated extent to which each of six factors – levels of GDP, life expectancy, generosity, social support, freedom, and corruption – are estimated to contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors.
While data sets retrieved from the World Happiness Report website are clean and clear, I used open refine to stack some data and imported all the sheets into a google spreadsheet so that I can have a better overview of the 3 data sets. My spreadsheet is here.
To start with, I retrieved data from the World Happiness Report official site, which includes data concerning:
1. Data panel including past world happiness report data from 2015-2020: Data Panel
2. World wide happiness data based on 2020 Gallup World Poll: Data for Figure 2.1
Visualization Dashboard
I used Tableau to visualize my synthesized dataset. With Tableau, I created a map of world happiness scores, two tables of happiness scores ranks of East Asian countries based on 2018 and 2021 World Happiness Reports and a chart presenting generosity, social support, freedom, and perception of corruption throughout East Asia countries based on the 2021World Happiness Report.
Since my final outcome would be a poster (which is not interactive), instead of creating a dashboard with Tableau, I stacked all those tables/charts into Figma and turned them into something that looks like below:
What you see above is rarely a data visualization poster. First, it still lacks important information from the World Happiness report dataset, which is GDP per capita and expected healthy life range for each country. In addition, it is hardly readable and lacks explanatory texts.
For previous reasons, I added following elements to my poster:
1.Introduction paragraph explaining what the poster is about, what data it is based on, what does happiness ladder score mean, and what are the variables the World happiness report gauged.
2.Boxes which contains information concerning:
a.A country/area’s Happiness Ladder Score
b.GDP per capita of the country
d.Healthy life expectancy of the country
3.Explanatory paragraph concerning the compared tables of happiness scores ranks of East Asian countries based on 2018 and 2021 World Happiness Reports.
4.Explanatory paragraph concerning the last chart.
With those changes incorporated, I created my poster as you would see in the next section:
Poster：East Asia 2018-2020: World Happiness Report 2021
User Test
I sent out pic 2 to 2 user test participants and asked them to view the poster on their phone. For each participant, I asked 4 questions:
- Please evaluate how familiar you are with data and data visualization (lowest:1 highest:10)
- Which part of my poster do you like the most? Why?
- Which part of my poster do you dislike the most? Why?
- Overall, do you have any recommendations for this poster?
I finally heard back from 2 participants, one is very familiar with data and data visualization (scored 9) another has medium familiarity with data and data visualization (score 6).
What do they like about the poster?
The layout: ” I really like how those charts/maps were put together!”
The information: “Really enjoyed point about how little happiness index has changed. That paragraph is fantastic.”
What do they dislike about the poster?
The color: “Blue is a little bit depressing to me. It does not make me feel happy.” “The color range you chose is not wide enough. It is hard to tell what’s the difference between medium-happiness countries and high happiness countries”
The information: “I dislike the last map the most. I think if you had a simple two-bar chart “freedom to make life choices” compared between two colors for 2018 and 2020 it would be better.”
To me, this kind of feedback is interesting to see. My original plan was to find 2 participants with high familiarity with data and data visualization(score 8-10) and lower familiarity with data and data visualization(score below 4), so that I could listen to feedback from both extremes. However, I later found out that it is really hard to find anyone I know with low data knowledge…
I totally agree with the “color comment.” Although you can have an overview of what’s the happiest and what’s the least happy region on my map, even I had to make a “Region Happiness Score” table to find out the precise answer.
I am not sure about the comment concerning the last chart. One thing to mention is that the chart you see in my Visualization Dashboard is the wrong one. (pic 1) I revisited the 2021 World Happiness Report website and read through how data was calculated and then replaced original scores of the 6 social factors with explanation scores (which stands for correlation.) On one side, I want to show how various social factors correlate with happiness. On the other side, I agree that for the last chart, there are too many things going on.
Reflection
Do we have an all-in-one data visualization tool?
This question stemmed from my experience making this map. I actually tried different ways to put visualized parts together(charts/maps/numbers). While my main to visualize the datasets was Tableau, I also created a Carto map to enhance my understanding of the dataset. In addition, I was bouncing between Openrefine and Google spreadsheets to separate /synthesize datasets and run table calculations(because it is more complicated to run that kind of analysis with Tableau.)
Layout is another reason I asked the question. I chose Figma as the layout tool, because it has grid system. While I wanted the poster to be appealing,I also wanted all the visualized parts to be consistent concerning colors and font size.
Color Matters!!!
As mentioned before, color is one of the main issues of this data visualization poster. It is sometimes hard to chose: should I use a single hue or should I use 2 constructing hues? How many colors should I involve? Which color for which kind of data?
A lesson I learnt is: Only experiments and tests would tell the answer.
User test provides valuable information
Color is not the only problem which could be solved by user testing.
For future data visualization works, I would conduct a user survey to learn more about my audience’s perception of the topic first. Knowing what people want to know and what people associate the topic with would provide a much better start point than I had in this project.
Also, I would love to expand the number of participants in future data visualization projects. As a UX researcher, 2 can barely be a participant number which leads to reliable results. It would be interesting to see test results from 6-10+ participants for my poster.