QQ Assignment: Description and Analysis of Quantitative Data. Geography 330

QQ Assignment: Description and Analysis of Quantitative Data
This assignment entails using secondary data and GeoWeb mapping tools to describe and
analyze quantitative geographic data. The specific topic of the analysis is the connection
between greenhouse gas emissions and development-related variables across countries.
However, the assignment is not intended to “test” your knowledge about climate change
and development, but rather to provide an opportunity to engage in formulation,
evaluation, and communication of conclusions and inferences from quantitative
information. This is accomplished through an assessment of secondary data sources,
description of mapped variables, and identification of possible causal relationships
between variables.
The assignment will contribute to completion of the QQ/QFRq goal in the SAS Core
Curriculum: Formulate, evaluate, and communicate conclusions and inferences from
quantitative information.
In this assignment you will use data from the World Bank website to address the
following research questions:
1. What are national patterns of carbon emissions? How to do these patterns vary
across space and do these patterns differ based on how carbon emissions are
defined and measured?
2. How do spatial patterns of carbon emissions per capita compare to patterns for
other social and economic variables?
3. If you were to develop a statistical model to identify variables that are causal
predictors of carbon emissions per capita, which variables would you include in
your model and why?
INSTRUCTIONS
Instructions for Completing Assignment 1:
This assignment will use GeoWeb features on the World Bank website
(http://data(dot)worldbank(dot)org/indicator) to create maps of variables that illustrate carbon
emissions and development levels. [Note when you identify a variable and click “map”
and “shaded”, you may need to refresh the page in some browsers to get the map to
appear.]
2
For Question 1, use several different variables that measure CO2 emissions, such as total
CO2 emissions (kt), CO2 emissions (metric tons per capita), etc. Create maps of these
variables, consider their spatial patterns, and how and why these patterns vary depending
on metric used.
For Question 2, select a number of other variables from the World Bank website which
you think might be useful to compare to the map of carbon emissions (metric tons per
capita). Spend some time looking at a number of different variables to get an ‘eyeball’
sense of what might be spatially correlated with the carbon emissions map. I would
recommend looking at maps for many different variables and before you settle on at least
5 that you think are related to carbon emissions.
For Question 3, select 5 variables that you think are causally related to carbon emissions
per capita. Select these variables based on the variables you examined in question 2.
These are the variables that you will include in your proposed statistical model. Discuss
how and why you think each of the variables you selected is expected to influence carbon
emissions.
WHAT TO TURN IN
Write up for Assignment 1 - to be posted on Sakai (under Assignment 1):
The write up for this assignment should be approximately four double-spaced pages
long (roughly 1000 words). The format for the write-up will include:
I. An introduction to the research issue (I recommend doing a little bit of
background research on greenhouse gas emissions and national differences in
emissions patterns) (length: about 1 paragraph)
II. A description of your data source (Discuss where the data in the world bank
website comes from and why you trust these data to be valid). (length: about 1
paragraph)
III. Answers to the three main research questions. (length: about 3 paragraphs)
IV. Conclusions. What broader conclusions about global patterns of carbon
emissions can we draw from this type of analysis? What additional research steps
could be taken to expand on this work? (length: about 1 paragraph)

Research Methods Geography 330 QQ Assignment 

  QQ Assignment: Description and Analysis of Quantitative Data  This assignment entails using secondary data and GeoWeb mapping tools to describe and analyze quantitative geographic data. The specific topic of the analysis is the connection between greenhouse gas emissions and development-related variables across countries. However, the assignment is not intended to “test” your knowledge about climate change and development, but rather to provide an opportunity to engage in formulation, evaluation, and communication of conclusions and inferences from quantitative information. This is accomplished through an assessment of secondary data sources, description of mapped variables, and identification of possible causal relationships between variables.  The assignment will contribute to completion of the QQ/QFRq goal in the SAS Core Curriculum: Formulate, evaluate, and communicate conclusions and inferences from quantitative information.  In this assignment you will use data from the World Bank website to address the following research questions:    1. What are national patterns of carbon emissions? How to do these patterns vary across space and do these patterns differ based on how carbon emissions are defined and measured?   2. How do spatial patterns of carbon emissions per capita compare to patterns for other social and economic variables?   3. If you were to develop a statistical model to identify variables that are causal predictors of carbon emissions per capita, which variables would you include in your model and why?     INSTRUCTIONS  Instructions for Completing Assignment 1:  This assignment will use GeoWeb features on the World Bank website (http://data(dot)worldbank(dot)org/indicator) to create maps of variables that illustrate carbon emissions and development levels. [Note when you identify a variable and click “map” and “shaded”, you may need to refresh the page in some browsers to get the map to appear.]  2  For Question 1, use several different variables that measure CO2 emissions, such as total CO2 emissions (kt), CO2 emissions (metric tons per capita), etc. Create maps of these variables, consider their spatial patterns, and how and why these patterns vary depending on metric used.   For Question 2, select a number of other variables from the World Bank website which you think might be useful to compare to the map of carbon emissions (metric tons per capita). Spend some time looking at a number of different variables to get an ‘eyeball’ sense of what might be spatially correlated with the carbon emissions map. I would recommend looking at maps for many different variables and before you settle on at least 5 that you think are related to carbon emissions.   For Question 3, select 5 variables that you think are causally related to carbon emissions per capita. Select these variables based on the variables you examined in question 2. These are the variables that you will include in your proposed statistical model. Discuss how and why you think each of the variables you selected is expected to influence carbon emissions.     WHAT TO TURN IN   Write up for Assignment 1 - to be posted on Sakai (under Assignment 1):  The write up for this assignment should be approximately four double-spaced pages long (roughly 1000 words). The format for the write-up will include:   I. An introduction to the research issue (I recommend doing a little bit of background research on greenhouse gas emissions and national differences in emissions patterns) (length: about 1 paragraph)  II. A description of your data source (Discuss where the data in the world bank website comes from and why you trust these data to be valid). (length: about 1 paragraph)  III. Answers to the three main research questions. (length: about 3 paragraphs)  IV.  Conclusions. What broader conclusions about global patterns of carbon emissions can we draw from this type of analysis? What additional research steps could be taken to expand on this work? (length: about 1 paragraph)