Group02 Proposal

PROPOSAL

POSTER

APPLICATION

RESEARCH PAPER

ALL PROJECTS

Overview

Empires rise and fall because of it; its ways are looked upon as the lifelines for nations who fought and died for it. Yet, it is something that you engage in as part of your daily life. 'It' refers to trade, which is the exchange of goods and services, often with compensation paid by the buyer to the seller.

From the simplest forms of bartering that has been interwoven into the earliest fabric of human history, to the modern-day commodity derivatives traded on virtual exchanges, trade has always been an essential economic activity to mankind. Over the years, globalization has created complex inter-dependencies between countries.

Recent US’s announcement of the imposition of hefty tariffs on steel and aluminium on most countries as part of their economic policy has shattered the delicate balance of world trade. The breakout of a full-blown global trade-war seems to loom ahead, and amongst many questions that arise out of this possibility, we seek to apply visual analytics method to facilitate and share understanding on the “Casualties-of-War”.

Motivations and Objectives

There is motivation to provide novel visual insights on a current and complex matter that affects the entire world. Using Global Commodity Trade Statistics published by the United Nations Statistics Division, we have identified investable and tradable commodities which fall into Metals, Energy, Livestock & Meat and Agricultural [1]. Integrating the Commodity Trade with the current GDP data from the World Bank, we plan to identify trends, patterns and dependencies in commodity trade at geographic, regional and economic communities; and identify economies that are sensitive to trade, along with the particular commodities that give rise to this sensitivity. We use a funnel methodology by generalizing our data visualizations by geographic and financial trade commodity groupings before providing a drill-drown facility in an interactive dashboard to help policymakers have a better understanding their economies and trade given the looming trade-war.

Through out analysis, we hope to address the following:

1) Commodity Trade Overview: Exploratory Analysis of Commodity Trade by Trading Parameters
We want to explore the hierarchical relationships between trade balance, quantity and volume by trading parameters such as commodity type, trade flow and regions to identify interesting patters between 2007 to 2016.
Choropleth Map: Filter by trade flow, commodity type and year to analyze trade balance (in USD Millions) between 2007 to 2016.
Sankey Diagram: Filter by year to view flow of commodity trade (in USD Millions) from the different commodity types to regions between 2007 to 2016.
Treemap: Filter by year to view import and export commodity trade quantity and volume by regions between 2007 to 2016.
Sunburst: Filter by year to view hierarchy of quantities and volume of commodity trade between 2007 to 2016.

2) Commodity Trade Dependencies: Analysis of Trade dependencies by Regions
We would like to analyze trends and trade dependencies among geographical and economic groups such as Organization for Economic and Development (OECD), European Union (EU) and Southeast Asia (SEA) by the different commodity types and trade flow.
Time-series with geographic panels: Filter by commodity flow and type to analyze trends and trade dependencies among OECD, EU and SEA between 2007 to 2016.

3) Commodity Trade Position Over Time: Analysis of Export-to-Import relationship over time
We would like to analyze commodity trade position by assessing export-to-import relationship among regions over time from 2007 to 2016.
Bubble Plot: Filter by commodity type and regions to assess Export-to-Import relationship among countries between 2007 to 2016.

4) Commodity Trade Openness to GDP
We would like to identify economies that are sensitive to trade, along with the particular commodities that give rise to this sensitivity.
Trellis Scatter Plot: To display the strong R-squared relationship between commodity trade balance to GDP to assess the level of sensitiveness among countries.

Data Sources

The Global Commodity Trade Statistics data [2] is retrieved from United Nations Statistics Division of the UNData website. The retrieved 2.94 million rows of data between 2007 to 2016 comprises the following information:

Dataset

Variable Description

Global Commodity Trade Statistics

Country name of record Year in which the trade occured Harmonized Commodity Description and Coding System (HS) by World Customs Organization comprising about 5000 commodity groups; each identified by a six digit code Commodity category Description of a particular commodity code Flow of Trade (Import, Export etc.) Value of Trade in USD Bn Quantity count of a given item based on the Quantity Name Quantity measurement type based on the commodity type Description of the quantity measurement type

Data Approach

Dataset	Description
1. Trade Commodities	·       From the UN Statistics Division, this forms the core data set. We use Countrycode package from R cran to obtain continent information for each country. ·       Exploratory analysis is carried out on this main dataset.
2. Commodity Types	·       Trade data from main dataset is grouped accordingly into the commonly traded commodity types of Metals, Agriculture, Livestock/meat, and Energy.
2. Country GDP	·       From the World Bank, this dataset contains the GDP figures of the world’s countries all the way back till the year 1960 up to 2017. ·       This is joined against the main dataset to enrich for further insights.

Tools & Packages

Dashboard and Data Manipulation

• shinydashboard
• DT
• data.table
• dplyr
• tidyverse

Visualizations

• sunburstR
• treemap
• networkD3
• geofacet
• ggplot2

References

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[1] Commodities Trading: Overview. https://www.investopedia.com/investing/commodities-trading-overview
[2] Global Commodity Trade Statistics Data: United Nations Statistics Division. http://data.un.org/Explorer.aspx
[3] World Bank GDP Data: The World Bank. https://data.worldbank.org/indicator/ny.gdp.mktp.cd