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 had 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. With the progression of time, the vital role of trade has not changed. Rapid industrialisation necessitated the embracing of division of labour, and entrance into the 21st century heralded various technological advancements that removed geographic boundaries that created a global market. Comparative advantages and trade dependencies have thus developed over the years, entrenching trade as a foundation for national economies and our global society at large. Voluminous commodities now flow in and out of countries, and data about such movements have proliferated over the years with the application of technology into commodity trading processes and workflows.

The importance of trade is unquestionable, and our group hopes to apply visual analytics methods to facilitate and share the understanding of trade in the form of an interactive dashboard, to empower users to explore commodity trades in all its complex interdependencies, and changes through time.

Motivations and Objectives

Based on the Global Commodity Trade Statistics published by the United Nations Statistics Division, we have identified investable and tradable commodities which fall into Metals (such as gold, silver, platinum and copper), Energy (such as crude oil, heating oil, natural gas and gasoline), Livestock and Meat (including lean hogs, pork bellies, live cattle and feeder cattle) and Agricultural (including corn, soybeans, wheat, rice, cocoa, coffee, cotton and sugar). [1]

Our project aims to identify trends and patterns in international trade at geographic, regional and economic communities and to explore the trade dependencies among these countries. We also aim to explore the major importers and exporters of trade communities and to find out the relationships in Amount, Quantity and Volume of Trade. Our team is motivated to design a dynamic and interactive dashboard to provide policymakers a better understanding and holistic view of the international trade.

Through out analysis, we hope to address the following:

1) To explore Quantity and Volume of International Trade by Commodity Type and Trade Flow
We want to explore the hierarchical relationship between trade quantity and volume by commodity type, trade flow and regions to identify interesting patterns in international trade over the years.
Visualizations: Sunburst and Treemap

2) To analyze trade dependencies in OECD, EU and SEA
We would like to analyze the trends and trade dependencies among geographical and economic groups such as Organization for Economic Cooperation and Development (OECD), European Union (EU) and Southeast Asia by commodity type and trade flow.
Visualizations: Time-series with geographic panels

Data Sources

The Global Commodity Trade Statistics data 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
Single-Linkage (single)	single - Nearest Neighbour clustering
Complete-Linkage (complete)	complete - Furthest Neighbour Sorting
Average Agglomerative Clustering	average - Unweighted Arithmetic Average Clustering (UPGMA) mcquitty - Weighted Pair Group Method with Arithmetric Mean (WPGMA) centroid - Unweighted Centroid Clustering (UPGMC) method - Weighted Centroid Clustering (WPGMC)
Ward’s Minimum Variance	ward.D – Does not implement Ward’s (1963) clustering criterion ward.D2 – Implements that criterion (Murtagh and Legendre 2014)

Tools & Packages

• shiny
• shinydashboard
• tidyverse
• dplyr
• countrycode
• rworldmap
• sunburstR
• treemap
• geofacet
• networkD3
• ggpplot2

References

Banner: https://www.drugdetectingdogs.com/detecting-dogs/miami/sweeper-florida/shipping/sniffing
[1] Commodities Trading: Overview. https://www.investopedia.com/investing/commodities-trading-overview/