##As someone who consumes the news on a daily basis, change seems to be the only things that is consistent. However, despite the immense quantity of information that is delivered to us daily, it is often difficult to step back and see the changes, whether positive or negative, that have occurred over the course of time. For this project, I wanted to explore which countries have seen the most drastic changes over the span of the last decade. In order to achieve this comparison, I collected an abundance of time series data related to various factors which served as key estimators for broader indicators. Specifically, I first collected data from the 2024 World Happiness Report. In this report, I was able to collect information related to subjective well-being. Further, I collected data concerning healthy individuals’ life expectancy at birth as measured in years. This data was based on the World Health Organization’s (WHO) Global Health Observatory data repository. Next, from this 2024 World Happiness Report I used data regarding individuals’ perception of corruption. According to the report, this “measure is the national average of the survey responses to two questions in the GWP: ‘Is corruption widespread throughout the government or not’ and ‘Is corruption widespread within businesses or not?’” (Appendix 1: Statistical Appendix of Chapter 2 of World Happiness Report 2024). Next I collected data from the World Health Organization related to the percentage of 1-year-olds who received their BCG immunizations. I felt that this metric was a telling indicator of how importantly adults prioritize healthcare. Further, I collected GDP data for each country from the World Integrated Trade Solution. After, I collected additional data from the World Bank related to electricity, the gender parity index (GPI), and credit discrimination. The first variable electricity refers to the proportion of each countries’ population that has access to electricity. Secondly, GPI is an “index for gross enrollment ratio in primary education is the ratio of girls to boys enrolled at primary level in public and private schools” (World Bank Group). Essentially this measures the access that girls have to a primary education relative to their boy classmates. Finally, credit discrimination is a binary variable which determines whether there are laws in place that hinder women’s ability to access lines of credit (0) or not (1). Lastly, I found data related to food insecurity and more specifically, the prevalence of undernourishment collected by the Food and Agriculture Organization of the United Nations. The food insecurity variable is classified by “high”, “medium”, and “low” food insecurity. I feel that this data provides a general overview for the states of various countries in terms of health and wellbeing, financial strength, gender equality, and infrastructure. There are 189 countries featured in the dataset, each classified by the regions: “Eastern Mediterranean”, “Europe”, “Africa”, “Americas”, “Western Pacific”, and “Asia.”
##In the first visual, I sought to further explore infrastructural advances related to access to electricity. I created a faceted scatterplot which compares various countries GDP, measured in USD, to their access to electricity (%). Each facet represents a different region and is animated to move over the course of time. From these visuals various trends become clear. Firstly, it is apparent that the regions of Africa and the Western Pacific have a wider spread of countries with varying levels of electricity access. While all regions show trends towards greater electricity access, Africa lacks the general progress that the other regions show. Further from this visual, China and the United States show dramatic increases in their GDP relative to the other countries featured. This is no surprise being that these are the largest two economies of the world. Further, since 2013, Asia and the Eastern Mediterranean saw dramatic increases in terms of their access to electricity. Most notably, Mongolia, East Timor, and Bangladesh saw an astounding 54.2%, 53.8% and 50.7% change relatively during that time period which highlights major infrastructural advances.
##Link to Shiny App: https://oliviat26.shinyapps.io/VAXFINAL/
##Next, I looked at how vaccinations for one-year-olds changed over time for the various countries. As highlighted by the line graph, the countries of Guinea-Bissau, Ireland, Mexico, Portugal, and Ukraine saw the most extreme fluctuations in these vaccinations. Both Mexico and Guinea-Bissau saw drastic declines in percentages during the Covid-19 pandemic, the peak of which ranged from 2020 to 2021. These changes are likely a manifestation of the political, social, and economic factors which hinder access to vaccinations and healthcare. I also think that it would be interesting to examine the rapid vaccination decline in Portugal between 2013 and 2015. By switching the data perspective, it becomes largely apparent that the various regions listed showcase minimal volatility in comparison to the specific countries.
##Link to Shiny App: https://oliviat26.shinyapps.io/CORRUPTIONFINAL2/
##Further, I looked to compare the ways in which citizens’ perceptions of corruption changed over time. After analyzing the data, I found that the countries of Armenia, Estonia, Georgia, Tajikistan, and Tanzania experienced the greatest changes in corruption levels. Interestingly enough four of the five countries included illustrate a negative trend line which signifies a greater trust in the government and a declined belief of corruption. Georgia is the exception, and it shows a steep increase overtime. While the country starts in 2013 with the lowest perceived level of corruption, it ends 2023 as the highest. During the last decade Georgia has experienced a variety of power shifts which led to great skepticism and political unrest which is showcased by the increase in perceived corruption.
##Link to Tablaeu: https://public.tableau.com/app/profile/olivia.tiboni/viz/FoodInsecurityMap_17325521140550/Sheet1?publish=yes
##Additionally, I created a map using Tableau to showcase food insecurity in countries over time. The levels of food insecurity are defined as being low if the percentage of undernourished individuals is less than or equal to 20%, medium if greater than 20%, and high if greater than 40%. When the visual is played, it becomes apparent that the countries of the central and eastern regions experience increased food insecurity over the time horizon. Syria, Venezuela, Mozambique, and Kenya all have the greatest relative change which is highlighted by their countries changing colors. During the time frame, war, corruption, and famine has led these countries to experience significant food insecurities.
##The following visual compares food insecurity to the predicted life expectancy at birth. As illustrated by the graph, there appears to be a negative correlation between prevalence of food insecurity and predicted life expectancy at birth. I have also sized the bubbles by that particular countries GDP per capita. In doing so, it is evident that the wealthier countries are clustered around the bottom right-hand corner meaning they have lower rates of food insecurity and higher life expectancy as well. The graph is also colored by regions and showcases the dispersed nature of the African countries and their significantly lower life expectancy. From the dataset, Haiti, Sierra Leone, and Malawi saw the most dramatic changes in their life expectancy. Haiti has the most significant change of a 11.54 year increase between 2013 and 2018 following the 2010 earthquake. These countries do see increases in their average life expectancy however, the average age remains far below the overall average.
#For this next visualization, I sought to determine if regions have made more progress with financial equality for women. As seen throughout the progression of time, the Eastern Mediterranean region shows dramatic increases in credit equality. Also, it is interesting to see that in 2013 the Asian countries all had some sort of financial discrimination against women, but that has begun to decrease. I have included an interactive chart for viewers to see the countries which passed laws during the last decade granting credit equality to women. Sorting it by the year, Rwanda and Togo are the two countries which passed these laws most recently in 2023.
#GPI versus Credit Discrimination
#GPI Side-By-Side Boxplot
#Individual Country Line Graph GPI
##Next, I wanted to compare how the gender parity index compared amongst countries that have discriminatory versus equal credit laws. I created a boxplot which shows that countries that have discriminatory laws related to credit have a slightly lower median GPI relative to those countries that have credit equality. More notable, the spread of the countries’ GPI with credit discrimination is far greater and in both directions. This is further seen by the following graph which plots the side-by-side boxplots for each region. The Eastern Mediterranean region has the greatest spread and the lowest reaching GPI value, with Africa next behind. In order to better compare the countries, I organized the data to showcase which countries had the most significant changes in their GPI. The countries include are Djibouti, Eritrea, India, Jamaica, and Kuwait. While most of the countries are moving closer to 1, a value representing equality. Kuwait and Eritrea deviate significantly. While Kuwait’s value reaches roughly 1.15, Eritrea’s falls to just above 0.85. This suggests that in Kuwait girls have greater access to education, while in Eritrea gender inequality disproportionately disadvantages females in the classroom.
##Link to Tablaeu:https://public.tableau.com/app/profile/olivia.tiboni/viz/Happiness_17325596497560/Sheet1?publish=yes
##Finally, I created a Tableau visualization which compares countries’ average happiness over time relative to their GDP. The colors of the graph are related to happiness level which is defined as “low”, “medium”, and “high” depending on whether the indices are less or equal to 3.48, less than 3.48, or greater than 5.68. The size of the dot on each country is also proportional to that countries’ GDP. The graph shows a moderate correlation between countries with larger GDPs and those that are happier. When looking at the countries individually, it is clear that Lebanon and Afghanistan see drastic drops in their happiness scores. Lebanon’s drop occurred during 2020 which aligned with the Covid-19 pandemic, economic and political instability, and the Beirut port explosion. Alternatively, Afghanistan saw a drop in 2017 which corresponds to a time of greater terrorist activity and humanitarian crises. The map shows the changes over time and highlights lower happiness levels in Africa and the Eastern Mediterranean regions.
##After having completed this project I can better see how economic, political, and economic factors impact various regions and countries. Generally speaking, the African region saw the most dramatic changes most notably related to food insecurity and changes in life expectancy. While there is no country or region in particular that had the most dramatic changes across all categories, different metrics can highlight the occurrence specific events. I have also observed that these regional categories are far too broad and with them there is often a dichotomy. Moving forward, I would like to explore regions using a more precise classification to more accurately observe trends.