Monday 6 April 2015

Transforming and Synthesizing the Data

By: Andrew Leaper, Cory Dean, Demi-Jenna Aodan, and Lindsay Griffiths

In this Blog it is our goal to use the Dashboard tool to develop insights into Key Performance Indicators (KPI’s) that are outlined in a list of 5 specific KPI’s. The data source used consisted of over 200,000 records and millions of combinations. Using a Business Intelligence modeling solution from MicroStrategy called Analytics Express we are able to drill down into the data to deliver the core information to identify the KPI’s through a simple graphical representation of the raw data. An environmental nonprofit organization may be interested in this data and insights in order to recognize where in the world and which specific countries they should be focusing their efforts. The following are our Questions or KPI’s.

1)What region in 1990 began with the highest carbon footprint? Which region had the lowest carbon footprint in 1990?

Looking at the data in a graphical format, it is easy to see that Asia & Oceania had the overall highest carbon footprint across the seven regions in 1990. Using the same graph we can see that the Middle East had the lowest carbon foot print in 1990.



A bar graph was used for this question because it shows a significant difference between the regions and separates the carbon footprint contributions into years. The metrics needed were from coal, petroleum, natural gas, and total carbon footprint. The attributes used were years, and region.

An environmental organization would be interested in this data and visual because it would reveal to them which regions around the world are in most need of their aid.

2)Does the population growth rate of the regions match the growth rate of their carbon footprint?

It would appear that there is a direct correlation between population growth and the carbon footprint growth. On the highest end of the scale, we can certainly see strong population growth in Asia & Oceania and a strong paralleling growth curve in carbon footprint. Even when we see population growth in Eurasia drop over the data period, we also see a drop in carbon footprint.
While population growth is a key factor to overall carbon footprint, we can see that it is not the only factor. We can see that Asia and Oceania are the world’s biggest users of Coal and Petroleum that generate a larger proportion of carbon, compared to regions like North America that are the largest consumers of cleaner fuels that produce a smaller carbon footprint like Natural Gas.



To answer this question we chose to use a line graphs because they clearly shows an increase in both population and carbon footprint over the 20 year time period. The Metrics used in these graphs were population, from coal, from petroleum, from natural gas, which all made up total carbon footprint. The Attributes used in the graph are the years from 1990-2010 and the regions.

An environmental organization would need to see these graphs and data because it would tell them if a regions's carbon footprint was growing naturally with their population or because of another threat.

 3)In 2010 which region contributed the most coal, natural gas, and petroleum? Which region was the biggest overall contributor?

In 2010 the region with the largest overall footprint was Asia & Oceania by a large margin. This can easily been seen in pie chart below. It also shows that Asia & Oceania have the largest carbon footprint of any of the regions. It accounts for 44.44% of the total carbon footprint of all regions.

Examining the 2010 data we can observe carbon footprint contributions by type of fosil fuel and which region has the largest overall carbon footprint. The breakdown shows which region is the largets contributor by fuel type. Our findings revealed:

·         Coal:    Asia& Oceania with 67.78% of the total of all regions

·         Petroleum: Asia & Oceania with 30.28% of the total of all regions

·         Natural Gas: North America with 24.44% of the total of all regions
 



To answer this question we thought a pie graph would illustrate the data best. Each pie graph shows the total amount of pollution from each fuel and what percentage was contributed from each region. The metrics used in this graph were from coal, from petroleum, from natural gas, and total carbon footprint. The Attributes used were the year 2010, and each region.

An environmental organization would find this data useful because it would inform them of which region currently needs their help the most. Furthermore, when compared to the graph for question number one, the organization would be able to recognize possible patterns.

4) Why is Eurasia the only region to have a decreasing carbon footprint?
One of the contributing factors to a country’s carbon footprint size is its population. Eurasia was the only country that saw its overall population decrease during the period of 1990 to 2010. We also see that the carbon footprint per capita went down in Eurasia at the same time as the overall population decreased in that region.   
It is also clear that between 1990 and 1998 there was a dramatic decrease in the total carbon foot print in that region. When we look at the use of carbon emitting resources, it is clear that Eurasia moves away from the consumption of Coal over the studied period. At the same time we can see an increase in usage of Natural Gas and Petroleum which lowers it overall carbon footprint. 
In the case of Eurasia the two contributing factors to why they are the only region with a decreasing carbon footprint is because their population is decreasing and they are using more efficient resources like Natural gas and Petroleum. Furthermore, they are moving away from Coal which is a heavy contributor to carbon footprint size.



 To analyze the data that was relevant to this question we thought that a line graph would be fitting. Since we were analyzing data over a 20-year period and looking for increases and decreases the line graph showed this well. The metrics used in the graph were from coal, from petroleum, from natural gas, and population. The attributes needed in these graphs were years, and region.

This information would be useful to a nonprofit environmental organization because part of their purpose is to reduce carbon footprints, and to do this they must understand how other country's and regions are doing it successfully.

5) Why does Africa have a steep increasing carbon footprint?

Africa has one of the fastest growing carbon footprints of the regions. When we review the two main factors that contribute to carbon footprint, population and per capita carbon footprint, we notice that the per capita carbon foot print remains almost flat during the 20 years reported in the data. This means that that the amount of carbon footprint per person in Africa remains consistent. What we see is a dramatic increase in population in Africa during the period. Therefore it is clear that there is a massive increase in population and the carbon foot print per person is constant. More people producing approximately the same carbon footprint generated a massive increase in overall carbon footprint within Africa.




The Metrics used in this graph are the per capita total, from coal, from petroleum, from natural gas, used to make a metric of total carbon footprint. The attributes needed are region, and years. A line graph was used because it illustrates a steady and continuous relationship between these metrics. 

This data would be useful to an environmental organization because just as they need to understand how countries are successfully reducing their carbon footprint, they need to understand why some countries have a sharply increasing carbon footprint size. When an organization understands these two issues they will be able to more efficiently complete their missions. 

Dashboards and Data Sets

By: Andrew Leaper, Cory Dean, Demi-Jenna Aodan, and Lindsay Griffiths

A dashboard is a tool organizations use to graphically display important up-to-date information. The purpose of the interface is to allow people to see all relevant data for a business in a single glance, so it is therefore no larger than a single screen. Many companies incorporate business intelligence dashboards because they play an important role in planning, budgeting and forecasting and are used to monitor and analyze information.

Dashboards are used to display, compare and analyze historical figures with budgets, forecasts and sales targets. Metrics, such as revenues and EBITDA and key performance indicators used to measure how successfully a company is achieving its goals are displayed on a dashboard. Furthermore, a dashboard may be for a single department in a company and therefore other key data points relevant to a specific process or enterprise may be displayed.

Dashboards help with big data because they are able to transform complex datasets into simple and comprehensible visuals. They contain charts and graphs to quickly summarize important data and information. This visual representation of data helps people to recognize links, patterns, trends, and anomalies in order to help make informed decisions and understand the present performance of a company. They also allow you to identify and correct negative trends, improve efficiency, improve upon performed analysis and help businesses determine goals and strategies as a whole (Andersen, 2013). It also saves a lot of time, as real-time data is displayed without generating multiple reports. Overall, a dashboard is a snapshot of a company’s current performance.

Our dataset shows the carbon footprint of many different countries from seven regions around the world. The data shown is from the years 1990 to 2010. It divides their footprint into total amounts contributed by Natural Gas, Coal, and Petroleum then per capita amounts for each. An environmental nonprofit organization could be interested in transforming and synthesizing this dataset. Understanding this information would help a nonprofit organization know where to focus their work across the world and the reasons a region possibly has an increasing or decreasing carbon footprint. Our goal in analyzing this dataset is to determine whether or not there is a correlation between the size of a country’s carbon footprint and their population. To do this we will ask these 5 questions:

Questions
Metrics and Attributes
1.      Which region in 1990 began with the highest carbon footprint? Which region had the lowest carbon footprint in 1990?
Metrics: From Natural Gas, Petroleum, and Coal
Attributes: Region, Years
2.      Does the population growth rate of the regions above match the growth rate of their carbon footprints?
Metrics: From Natural Gas, Petroleum, and Coal
Attributes: Region, Years
3.      In 2010 which region contributed the most coal, natural gas, and petroleum?
Which region is the biggest overall contributor?
Metrics: Per Capita from Natural Gas, Petroleum and Coal
Attributes: Regions, Years
4.      Why is Eurasia the only region to have a decreasing carbon footprint?

Metrics: From Natural Gas, Petroleum, Coal and their Per Capita Totals
Attributes: Region, Years
5.      Why does Africa have a steep increasing carbon footprint?

Metrics: From Natural Gas, Petroleum, Coal and their Per Capita Totals
Attributes: Region, Years

Bibliography
Andersen, K. (2013, December 31). The Importance of the Business Intelligence Dashboard. Retrieved from mas software solutions: http://www.mas-ss.com/blog/bid/155733/The-Importance-of-the-Business-Intelligence-Dashboard

Chiang, A. (2011, November 28). What is a Dashboard? Retrieved from Dashboard Insight: http://www.dashboardinsight.com/articles/digital-dashboards/fundamentals/what-is-a-dashboard.aspx

Klipfolio. (2014). What Is A Business Dashboard? Retrieved from Klipfolio: http://www.klipfolio.com/guide-to-business-dashboards

Rouse, M. (2010, November). Business Intelligence Dashboard. Retrieved from Search Business Analytics: http://searchbusinessanalytics.techtarget.com/definition/business-intelligence-dashboard