Group12 Report
Have the Nations really progressed ?
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Report
Contents
Introduction
World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available including national, regional and global estimates. It covers more than 7 million data points collected over the span of 58 years. This statistical reference includes over 1500 indicators covering more than 200 economies. The annual publication is released in April of each year.
The massive amount of world development data has by far exceeds the ability for students, policymakers, analysts and officials to transform the data into proper visualization for analysing and gaining insight of the global developmental landscape. Thus, creating an adverse impact on the financial and technical assistance World Bank is providing to the developing countries around the world.
Through our visualizations, we seek to utilize existing data to derive meaningful insights over how various socioeconomic factors have had an impact on development of different nations and to tell their story of growth and downfall across years. This dashboard also helps decide on various areas the countries need help on and has the aid provided earlier has any effect or not. The Key objective is to deep dive in a countries development across 9 parameters.
Objective and Motivation
We use a lot visual metrics to formulate the trend of each indicator that has been chosen for the analysis. Below we have given some sample visuals of the time series trend taking for example, Afghanistan and Singapore countries as sample.
- In this visualization, we have plotted the time series trend of Adjusted Savings from Carbon dioxide damage in USD in Afghanistan. We can clearly see, from 1960-1965 there was no readings of this measure in the nation, after 1965 there has been a uniform trend over the years until 2006, after 2007, there was an uptrend of the savings value. It is evident that the value is the maximum in 2017, making the value for the year 2018 still higher.
- In this visualization, we have plotted the time series trend of Education Expenditure for Afghanistan in % GNI. We can clearly see, from 1960-1965 there was no readings of this measure in the nation, after 1965 till 1970 there has been fluctuating expenditure on the education, but after 1970 its a perfect uniform trend over the years until 2010, At 2011, there had been a huge downfall in the trend and then it again rose and fell.
- In this visualization, we have plotted the time series trend of Net Official Development Assistance received from India. We can clearly see, from 1960-2017 there has been irregular trend, no any repetitive trends present and the overall trend over the years has increased, the value of 500 Million USD in 1960 has rose to 2.5 Billion USD in 2017.
- In this visualization, we have plotted the time series trend of The Exports and Goods and services and Change in Inventories of Singapore in USD.The trend clearly shows that there has been a consistent rise in the vale of exports of goods from Singapore. The other graph tells us the changes has been so wide, regularly there has been eradication of goods stock in Singapore over the years.
Previous Works
Visualization
- We use different tools for the visualization of the data. These include R Markdown, R Shiny, Tableau for now.
- We use the R tool to model the entire project. In R Shiny, we use the xts package for data manipulation and beneficiary in plotting time series chart. We also use ggplot2, plotly, lubridate for plotting visuals in R and manipulating the date structure. Following it, we use dynlm/ardl a tool for time series regression analysis. We have also planned to forecast the future values of the indicators for a country using the R package forecast.
- We also use the Tableau for the quick visualization of any data. The Tableau has more packages for making the visuals with much less effort. In Tableau there is a restriction to use a limited number of rows for visualization, hence we use only a certain specific rows for the visuals in Tableau.
Data Cleaning
- We have a lot of data to be cleaned for the proper data set for the analysis. The data cleaning is done using the SAS-JMP tool, removing the necessary rows and recoding the missing values. Besides, we also use Microsoft Excel 365 for small and quick cleaning.
