Difference between revisions of "Group02 Proposal"
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<br> | <br> | ||
=Overview= | =Overview= | ||
| − | + | 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 existed in one form or another as an important economic activity to mankind. | |
| − | + | ||
| − | From the simplest forms of bartering that | + | With globalisation as a catalyst, trade has become entrenched as an integral part of the global economy. Complex webs of country-to-country inter-dependencies have formed, and up to a quarter of global production is being exported (Ortiz-Ospina, Beltekian, & Roser, 2018). |
| − | + | ||
| − | + | As with other international activities, countries seek to preserve and further national interests when engaging in trade. Thus, trade flows are controlled and regulated by political instruments such as tariffs and sanctions. These tools are often used to impose penalties on antagonistic countries, or to preserve national interests. | |
| − | + | Following the US President's recent signing of a protectionist memorandum detailing the imposition of a 25% tariff on $50 billion of imports from China (White House, 2018), and the subsequent announcement of the imposition of hefty tariffs on steel and aluminium imports from several countries (BBC, 2018), the delicate balance of world trade has been shattered. 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 provide exploratory insights on international commodity trading, and to facilitate the analysis of the casualties of this potential trade war. | |
| − | |||
| − | + | =Project Motivation 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 Agriculture (Lioudis, 2018). 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: <br> | Through out analysis, we hope to address the following: <br> | ||
| − | '''1) | + | '''1) Commodity Trade Overview: Exploratory Analysis of Commodity Trade by Trading Parameters''' |
| − | We want to explore the hierarchical | + | <br>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. |
| − | + | <br>Choropleth Map: Filter by trade flow, commodity type and year to analyze trade balance (in USD Millions) between 2007 to 2016. | |
| + | <br>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. | ||
| + | <br>Treemap: Filter by year to view import and export commodity trade quantity and volume by regions between 2007 to 2016. | ||
| + | <br>Sunburst: Filter by year to view hierarchy of quantities and volume of commodity trade between 2007 to 2016. | ||
| − | '''2) | + | '''2) Commodity Trade Dependencies: Analysis of Trade dependencies by Regions ''' |
| − | We would like to analyze | + | <br>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. |
| − | + | <br>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 ''' | |
| − | + | <br>We would like to analyze commodity trade position by assessing export-to-import relationship among regions over time from 2007 to 2016. | |
| − | + | <br>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''' | |
| + | <br> We would like to identify economies that are sensitive to trade, along with the particular commodities that give rise to this sensitivity. | ||
| + | <br>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= | =Data Sources= | ||
| − | The Global Commodity Trade Statistics data | + | We would be consolidating Global Commodity Trade Statistics with World Bank GDP data by their respective countries. The Global Commodity Trade Statistics[2] and GDP data[3] are retrieved from United Nations Statistics Division and World Bank respectively between 2007 to 2016. The consolidated dataset comprises of investable and tradable import/export commodities from the four (4) main categories: Metals, Energy, Livestock & Meat and Agriculture. |
| − | * Country | + | |
| − | * Year in which the trade | + | {| class="wikitable" |
| + | |- | ||
| + | ! 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 | * Harmonized Commodity Description and Coding System (HS) by World Customs Organization comprising about 5000 commodity groups; each identified by a six digit code | ||
| − | * Description of a particular commodity code | + | * Commodity category |
| + | * Description of a particular commodity code | ||
* Flow of Trade (Import, Export) | * Flow of Trade (Import, Export) | ||
| − | * Value of Trade in USD | + | * Value of Trade in USD Bn |
| − | |||
| − | |||
* Quantity count of a given item based on the Quantity Name | * 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 | |
| − | + | |- | |
| + | | World Bank GDP Data|| | ||
| + | * Country Name | ||
| + | * Year in which the record was taken | ||
| + | * Current GDP (in US$ Trillion) | ||
| + | |} | ||
=Tools & Packages= | =Tools & Packages= | ||
| − | + | Dashboard and Data Manipulation: | |
* shinydashboard | * shinydashboard | ||
| + | * DT | ||
| + | * data.table | ||
| + | * dplyr | ||
* tidyverse | * tidyverse | ||
| − | * | + | * stringr |
* countrycode | * countrycode | ||
| − | + | Visualizations: | |
* sunburstR | * sunburstR | ||
* treemap | * treemap | ||
| + | * networkD3 | ||
* geofacet | * geofacet | ||
| − | * | + | * ggplot2 |
| − | * | + | * plotly |
| + | |||
| + | =Credits= | ||
| + | [1] Banner: https://www.drugdetectingdogs.com/detecting-dogs/miami/sweeper-florida/shipping/sniffing | ||
| + | <br>[2] Global Commodity Trade Statistics Data: United Nations Statistics Division. http://data.un.org/Explorer.aspx | ||
| + | <br>[3] World Bank GDP Data: The World Bank. https://data.worldbank.org/indicator/ny.gdp.mktp.cd | ||
=References= | =References= | ||
| − | + | * BBC. (2018, May 31). ''US tariffs: Steel and aluminum levies slapped on key allies''. Retrieved from: https://www.bbc.com/news/world-us-canada-44320221 | |
| − | + | * Lioudis, N. K. (2018, February 27). ''Commodities trading: An overview''. Retrieved from: https://www.investopedia.com/investing/commodities-trading-overview | |
| − | + | * White House. (2018, May 29). ''Statement on Steps to Protect Domestic Technology and Intellectual Property from China’s Discriminatory and Burdensome Trade Practices''. Retrieved from: https://www.whitehouse.gov/briefings-statements/statement-steps-protect-domestic-technology-intellectual-property-chinas-discriminatory-burdensome-trade-practices/ | |
Latest revision as of 11:58, 5 December 2018
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Contents
Overview
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 existed in one form or another as an important economic activity to mankind.
With globalisation as a catalyst, trade has become entrenched as an integral part of the global economy. Complex webs of country-to-country inter-dependencies have formed, and up to a quarter of global production is being exported (Ortiz-Ospina, Beltekian, & Roser, 2018).
As with other international activities, countries seek to preserve and further national interests when engaging in trade. Thus, trade flows are controlled and regulated by political instruments such as tariffs and sanctions. These tools are often used to impose penalties on antagonistic countries, or to preserve national interests.
Following the US President's recent signing of a protectionist memorandum detailing the imposition of a 25% tariff on $50 billion of imports from China (White House, 2018), and the subsequent announcement of the imposition of hefty tariffs on steel and aluminium imports from several countries (BBC, 2018), the delicate balance of world trade has been shattered. 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 provide exploratory insights on international commodity trading, and to facilitate the analysis of the casualties of this potential trade war.
Project Motivation 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 Agriculture (Lioudis, 2018). 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
We would be consolidating Global Commodity Trade Statistics with World Bank GDP data by their respective countries. The Global Commodity Trade Statistics[2] and GDP data[3] are retrieved from United Nations Statistics Division and World Bank respectively between 2007 to 2016. The consolidated dataset comprises of investable and tradable import/export commodities from the four (4) main categories: Metals, Energy, Livestock & Meat and Agriculture.
| Dataset | Variable Description |
|---|---|
| Global Commodity Trade Statistics |
|
| World Bank GDP Data |
|
Tools & Packages
Dashboard and Data Manipulation:
- shinydashboard
- DT
- data.table
- dplyr
- tidyverse
- stringr
- countrycode
Visualizations:
- sunburstR
- treemap
- networkD3
- geofacet
- ggplot2
- plotly
Credits
[1] Banner: https://www.drugdetectingdogs.com/detecting-dogs/miami/sweeper-florida/shipping/sniffing
[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
References
- BBC. (2018, May 31). US tariffs: Steel and aluminum levies slapped on key allies. Retrieved from: https://www.bbc.com/news/world-us-canada-44320221
- Lioudis, N. K. (2018, February 27). Commodities trading: An overview. Retrieved from: https://www.investopedia.com/investing/commodities-trading-overview
- White House. (2018, May 29). Statement on Steps to Protect Domestic Technology and Intellectual Property from China’s Discriminatory and Burdensome Trade Practices. Retrieved from: https://www.whitehouse.gov/briefings-statements/statement-steps-protect-domestic-technology-intellectual-property-chinas-discriminatory-burdensome-trade-practices/
