{"id":38736,"date":"2025-04-17T02:20:13","date_gmt":"2025-04-17T06:20:13","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=38736"},"modified":"2025-04-17T02:22:02","modified_gmt":"2025-04-17T06:22:02","slug":"world-happiness-index-and-related-factors","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/world-happiness-index-and-related-factors\/","title":{"rendered":"World Happiness Index and Related Factors"},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n\n

A country\u2019s perceived \u2018happiness\u2019 is influenced by a number of factors \u2013 some obvious and others unapparent and difficult to measure. The World Happiness Report\u2019s 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.<\/p>\n\n\n\n

Though the report is very comprehensive, it is seemingly impossible to cover (and measure!) every factor that affects a population\u2019s 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 \u2013 both culturally and geographically.<\/p>\n\n\n\n

UNESCO World Heritage Sites are locations deemed culturally significant throughout the world and give an insight into a country\u2019s cultural ties. World Heritage Sites often also attract tourism, boosting local economies.<\/p>\n\n\n\n

The World Risk Report is published annually with an analysis of a country\u2019s susceptibility to \u201can extreme natural event becoming a disaster\u201d (tr1gg3rtrash). These disasters include tornadoes, hurricanes, monsoons, and so on, and the associated risk is heavily dependent on the country\u2019s 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 \u2013 i.e. \u2018low\u2019, \u2018high\u2019, \u2018very high\u2019.<\/p>\n\n\n\n

Data Selection<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

The dataset I started with is the World Happiness Index and Inflation dataset, which contains an \u2018index\u2019 for each country or region on the supposed happiness of its citizens. The metrics used to determine this index \u2013 both economic and social \u2013 are also included in the dataset and are all self-reported by their respective citizens.<\/p>\n\n\n\n

The next set I added was the UNESCO World Heritage Sites dataset and created a relation to my original dataset by joining on the \u2018Country\u2019 and \u2018Place\u2019 variables \u2013 which both contained the names of countries. This dataset was specific to 2016 and contained information on each country\u2019s 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.<\/p>\n\n\n\n

Lastly, I added the World Risk Index dataset, which contained metrics on each country \u2013 spanning multiple years \u2013 and their respective \u2018risk\u2019 index based on vulnerability, exposure, etc. When creating a relation to the base table, I joined on both \u2018Year\u2019 and \u2018Country\u2019\/\u2018Region\u2019 to make sure the data shown would be from the correct year, since I filter down to show only data from 2016.<\/p>\n\n\n\n

Visualizations<\/h2>\n\n\n\n

Happiness Score<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n

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 \u2018temperature\u2019 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).<\/p>\n\n\n\n

UNESCO World Heritage Sites<\/h3>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

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\u2019s 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 \u2018Sites \/ million km2\u2019 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. <\/p>\n\n\n\n

Population Density<\/h3>\n\n\n\n

<\/p>\n\n\n\n

A similar approach was used to show a country\u2019s 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 \u2013 Denmark (highest happiness score), Afghanistan (lowest happiness score), United States of America (where this report was written). The coloring of the two extremes\u2019 label boxes updates for each metric for easy comparisons.<\/p>\n\n\n\n

Risk Index<\/h3>\n\n\n\n

<\/p>\n\n\n\n

Lastly, the countries\u2019 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.<\/p>\n\n\n\n

Going Forward<\/h2>\n\n\n\n

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.<\/p>\n\n\n\n

Interactive Tableau<\/h2>\n\n\n\n

https:\/\/public.tableau.com\/app\/profile\/sofia.harmon\/viz\/Book1_17447754808470\/Story2?publish=yes<\/a><\/p>\n\n\n\n

References<\/h2>\n\n\n\n

Helliwell, John F., et al., editors. World Happiness Report 2024<\/em>. Wellbeing Research Centre, University of Oxford, 2024, https:\/\/worldhappiness.report\/ed\/2024\/<\/a>.<\/p>\n\n\n\n

Cadotte, Marc. The Number of UNESCO World Heritage Sites by Country and National Statistics<\/em>. Version 2, 2016, Figshare, https:\/\/doi.org\/10.6084\/m9.figshare.3250534.v2<\/a>.<\/p>\n\n\n\n

tr1gg3rtrash. Global Disaster Risk Index Time Series Dataset<\/em>. Version 1, 2023, Kaggle, https:\/\/www.kaggle.com\/datasets\/tr1gg3rtrash\/global-disaster-risk-index-time-series-dataset<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Introduction A country\u2019s perceived \u2018happiness\u2019 is influenced by a number of factors \u2013 some obvious and others unapparent and difficult to measure. The World Happiness Report\u2019s 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…<\/p>\n","protected":false},"author":4473,"featured_media":38405,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[149,341,1],"tags":[115,5,31,94],"coauthors":[1901],"class_list":["post-38736","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-labs","category-maps","category-visualization","tag-data-visualization","tag-information-visualization","tag-maps","tag-visualization"],"jetpack_featured_media_url":"https:\/\/i0.wp.com\/studentwork.prattsi.org\/infovis\/wp-content\/uploads\/sites\/3\/2025\/03\/world_hapiness.jpg?fit=612%2C590&ssl=1","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paBdcV-a4M","_links":{"self":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/38736","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/users\/4473"}],"replies":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/comments?post=38736"}],"version-history":[{"count":7,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/38736\/revisions"}],"predecessor-version":[{"id":38746,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/posts\/38736\/revisions\/38746"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media\/38405"}],"wp:attachment":[{"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/media?parent=38736"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/categories?post=38736"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/tags?post=38736"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/studentwork.prattsi.org\/infovis\/wp-json\/wp\/v2\/coauthors?post=38736"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}