Final Duke University Case Study Stats 440 Project

Life expectancy, population, fertility, and mortality rates are factors that can tell a story about a country and the world. What if a user could view these factors in a customizable manner where they can choose which countries and how to visualize this data? Our high-level goal is to create an interactive spatio-temporal visualization regarding the changes in world population and other related factors (child mortality, life expectancy, children per woman). Our final product is a Shiny application that allows users to visualize these factors in various forms, including a spatio-temporal visualization for a world map or singular country, compare a related factor for multiple countries, and predict factors in the future using a time series regression model. To analyze our data we will be using four primary datasets that were all downloaded from the website Gapminder, at 'https://www.gapminder.org/'. Our primary inspiration for this data comes from a YouTube video watched called '200 countries in 200 years' by Hans Rosling as well as visualizations on the gapminder website. In this video Hans Rosling creates a spatio-temporal animation of population and life expectancy over years. Our group created a similar map, while adding other visualizations and customizability for users, using geospatial visualizations, predictions, and plots to compare countries. We decided to approach the visualizations by creating a shiny app that allows people to pick and select which countries to compare and use a slider to toggle through time at their own pace and see snapshots of each year, for a single country as well as for all countries. Everyone visualizes data in different ways and different people have different preferred methods of visualizing data so this was to maximize the user's input/customization in how they wanted the data to be presented. The four main visualization options we created are a world map visualization to create a big-picture idea about the state of the world in a given year for a factor (population, fertility, mortality, or life expectancy), a single-country geo-spacial visualization to see how a relevent factor changed in a country over time, a linear model to compare the factor as it relates to multiple countries, and a regression model to show a prediction for how a factor will change in the future for a selected country.

Life expectancy, population, fertility, and mortality rates are factors that can tell a story about a country and the world. What if a user could view these factors in a customizable manner where they can choose which countries and how to visualize this data? Our high-level goal is to create an interactive spatio-temporal visualization regarding the changes in world population and other related factors (child mortality, life expectancy, children per woman). Our final product is a Shiny application that allows users to visualize these factors in various forms, including a spatio-temporal visualization for a world map or singular country, compare a related factor for multiple countries, and predict factors in the future using a time series regression model. To analyze our data we will be using four primary datasets that were all downloaded from the website Gapminder, at 'https://www.gapminder.org/'. Our primary inspiration for this data comes from a YouTube video watched called '200 countries in 200 years' by Hans Rosling as well as visualizations on the gapminder website. In this video Hans Rosling creates a spatio-temporal animation of population and life expectancy over years. Our group created a similar map, while adding other visualizations and customizability for users, using geospatial visualizations, predictions, and plots to compare countries. We decided to approach the visualizations by creating a shiny app that allows people to pick and select which countries to compare and use a slider to toggle through time at their own pace and see snapshots of each year, for a single country as well as for all countries. Everyone visualizes data in different ways and different people have different preferred methods of visualizing data so this was to maximize the user's input/customization in how they wanted the data to be presented. The four main visualization options we created are a world map visualization to create a big-picture idea about the state of the world in a given year for a factor (population, fertility, mortality, or life expectancy), a single-country geo-spacial visualization to see how a relevent factor changed in a country over time, a linear model to compare the factor as it relates to multiple countries, and a regression model to show a prediction for how a factor will change in the future for a selected country.