Introduction:

In lab four, I aimed to make a map to show 2020 world happiness report data visually. Hopefully, the map should :

1. Provide an overview of 2020 world happiness report data.
2. Is happiness correlated with GDP per capita?
3. Is happiness correlated with perception of social support?
4. Is happiness correlated with healthy life expectancy?

The intended audience is the public who is curious about world happiness status in the wired year of  2020. And the audience should be able to view and interact with the map.

Inspiration

The map you see above (figure 1) is inspired by the following data visualization works related to world happiness report:

Inspiration No.1 Map of overall Country Happiness scores, by Eric Adlard(figure 2)

Figure 2.1 and figure 2.2 are a series of maps that visualized happiness scores from nationally representative samples between the years 2013-2016. Providing several maps seems to be a good way of visualizing multi-dimensions of one dataset. My question is that what if I want to present multi-dimensions of one dataset on one map?

Inspiration No.2 World Happiness, by Alexander Bastidas Fry with data from the World Happiness Report (figure 3)

The interactive map of world happiness made by Alexander Bastidas helped to answer my previous question. The answer is interaction. By providing interactive filters, the map allows its audience to personalize the information they see.

While the map did a good job of showing world happiness scores with a 3D globe, unhappy: red, happy: green. I am not very sure if I should use this color palette or not. People may associate the idea of happiness with different colors.

Inspiration No.3 World Happiness With An Interactive Map, by Haley Hammer (figure 4)

Haley Hammer’s interactive map showed another example of how the audience may interact with a map. As you see above, a user can click on the year bar to view and compare visualized world happiness score from 2016-2019.

My Approach:

Reference:

– Dataset I used is the 2020 world health report dataset retrieved from Kaggle, which is credited for Helliwell, John F., Richard Layard, Jeffrey Sachs, and Jan-Emmanuel De Neve, eds. 2020. World Happiness Report 2020. New York: Sustainable Development Solutions Network

– Additional information: I also consulted the F&Q page of the World Happiness Report website for 1. how was the data collected and cleaned 2. what does certain jargons mean (eg. ladder score).

Method:

Step 1: Import geo-file and data

-I first made sure that data make sense: There were 24 columns in my chosen dataset. I firstly checked on each cell to make sure that all the data looks good. Then I changed country names to make them consistent with those in the map file.

-I then defined the scope of the map. The 2020 world happiness report dataset contains data concerning happiness scores for 249 countries. For this reason, I used world countries’ borders as the base map.

-Then the dataset was imported into Carto with the “analysis” feature: I chose to add columns from the 2nd table, and imported aspects needed for my map.

Step 2: Styling the map

A. Color: My hypothesis is that green, as a neutral color, could best represent the concept of happiness. In addition，high brightness should make people feel happier while high darkness would be associated with unhappiness.

For this reason, I used green of high darkness for low happiness-score countries and green of high brightness for high happiness-score countries.

B. Legend: I wanted to make sure that my audience would be able to understand my color codes. For this reason, I added a legend to explain: 1. What does happiness mean in this map 2, What’s the lowest happiness score in the dataset. 3. What’s the highest happiness score in the dataset.

C. Pop-ups: I then added pop-ups containing information about 1. Country name 2.Exact happiness ladder score of the country in 2020 3. 2020 GDP per capita.

Step 3:Add widgets to the map: 

After styling, I added an interactive widget to the map which divided countries into 5 levels based on their happiness ladder scores. By clicking on each bucket, the audience could have a sense of how countries at the level of happiness are distributed worldwide.

I then added 3 more widgets to the map: perception of social support, healthy life expectancy, and perception of corruption. The audience may interact with any of those widgets to customize the information output of the map.

Results and Interpretation

Overall, North America, North-western Europe, and Australia are the happiest areas in World Happiness Report 2020.

According to figure 5, the happiest countries/areas tend to have high scores on GDP per capita, rating of social support, and long healthy life expectancy.

In comparison, the least happy countries/areas tend to have lower scores on GDP per capita, rating of social support, and long healthy life expectancy.

However, interestingly, some areas which have an above medium rating on social support facet have low happiness ladder score. In addition, most of them have a very high perception of corruption.

While further correlation analysis should be done to validate if there are correlations between happiness ratings and factors I introduced in the map, at least I saw a trend that happier areas tend to have a longer healthy life expectancy a higher rating on a social support system, and a higher GDP per capita.

Reflection and Next Step

For my final project, I would continue to work on world happiness report 2021 data. My project aims to visualize data shared by the report authors and introduce the following facets to its audience:

-Overview of countries’ happiness scores in 2020

-Change in world happiness scores through 2015 to 2020

– Association between mortality and happiness during Covid 19.

Instead of making an interactive map with carto, I want to challenge myself by making a poster which shows more facets of various datasets. Since I had a good feedback on the color codes I am using right now, I may continue to use the fonts and color codes for 2021 World Happiness Report visualization!