Introduction
A country’s perceived ‘happiness’ is influenced by a number of factors – some obvious and others unapparent and difficult to measure. The World Happiness Report’s mission is to be able to quantify this idea into an index and relate it to other social, political, or economic metrics to find common themes. Their data is published each year with updated metrics and is made publicly available for people to draw their own conclusions and analyses.
Though the report is very comprehensive, it is seemingly impossible to cover (and measure!) every factor that affects a population’s happiness in one report. This is where one can introduce metrics from independent studies in order to find patterns and correlations not originally included. For this report, I chose to include metrics not included in the original dataset that related to each country’s relative location – both culturally and geographically.
UNESCO World Heritage Sites are locations deemed culturally significant throughout the world and give an insight into a country’s cultural ties. World Heritage Sites often also attract tourism, boosting local economies.
The World Risk Report is published annually with an analysis of a country’s susceptibility to “an extreme natural event becoming a disaster” (tr1gg3rtrash). These disasters include tornadoes, hurricanes, monsoons, and so on, and the associated risk is heavily dependent on the country’s geographical location. Social factors affect the risk index as well when taking into account preparedness for disaster and ability to cope. The factors are combined to give each country a risk index from 0 to 100 (with 100 being the most at risk), and then are categorized into buckets – i.e. ‘low’, ‘high’, ‘very high’.
Data Selection
The dataset I started with is the World Happiness Index and Inflation dataset, which contains an ‘index’ for each country or region on the supposed happiness of its citizens. The metrics used to determine this index – both economic and social – are also included in the dataset and are all self-reported by their respective citizens.
The next set I added was the UNESCO World Heritage Sites dataset and created a relation to my original dataset by joining on the ‘Country’ and ‘Place’ variables – which both contained the names of countries. This dataset was specific to 2016 and contained information on each country’s population and area, along with the number of UNESCO World Heritage Sites. Because this dataset was limited to 2016, I filtered the data shown from the first dataset to only be from the year 2016 for consistency.
Lastly, I added the World Risk Index dataset, which contained metrics on each country – spanning multiple years – and their respective ‘risk’ index based on vulnerability, exposure, etc. When creating a relation to the base table, I joined on both ‘Year’ and ‘Country’/‘Region’ to make sure the data shown would be from the correct year, since I filter down to show only data from 2016.
Visualizations
Happiness Score
The first map represents the countries happiness score in 2016 (scaling from around 1 to around 8), where each one is given a color based on a ‘temperature’ gradient. This gradient is used throughout the different maps and uses orange/yellow to indicate positive values and green/blue to indicate less positive values. As seen in the map, Denmark is the country with the highest happiness score (7.8) and Afghanistan is the country with the lowest score (3.4).
UNESCO World Heritage Sites
A similar mapping concept is used here to show the number of world heritage sites in a given country (orange colors indicating a higher number) standardized by the country’s area. The map was originally slightly biased towards countries with larger areas having a higher number of world heritage sites, so to accommodate for the varying sizes the variable ‘Sites / million km2’ is used to measure the ratio of sites to the size of the country. The percentile of this variable to the global measurements is then used to create a color scale in order to better represent the amounts, rather than having a map with majority colors being the middle of the scale.
Population Density
A similar approach was used to show a country’s population density (the number of people per km2) and similarly the percentile of this measurement is used in creating a color scale. Again the same three countries are highlighted – Denmark (highest happiness score), Afghanistan (lowest happiness score), United States of America (where this report was written). The coloring of the two extremes’ label boxes updates for each metric for easy comparisons.
Risk Index
Lastly, the countries’ risk index is plotted to show their comprehensive rating for their predisposition to natural disasters and their ability to weather those storms (literally). A percentile is used once again to show the index relative to other countries rather than just the absolute value.
Going Forward
I would like to see more crossovers of the metrics so as to compare them directly in one visualization instead of using small multiples. Additionally I would be interested in seeing a deep dive on one metric over time for each country through some form of animation.
Interactive Tableau
https://public.tableau.com/app/profile/sofia.harmon/viz/Book1_17447754808470/Story2?publish=yes
References
Helliwell, John F., et al., editors. World Happiness Report 2024. Wellbeing Research Centre, University of Oxford, 2024, https://worldhappiness.report/ed/2024/.
Cadotte, Marc. The Number of UNESCO World Heritage Sites by Country and National Statistics. Version 2, 2016, Figshare, https://doi.org/10.6084/m9.figshare.3250534.v2.
tr1gg3rtrash. Global Disaster Risk Index Time Series Dataset. Version 1, 2023, Kaggle, https://www.kaggle.com/datasets/tr1gg3rtrash/global-disaster-risk-index-time-series-dataset.