Project 3

Due: Friday, March 11
Objectives: Data Collection, Visualization & Analysis
Grade: 10% of final

This assignment requires you to use practice processing, visualizing, and analyzing data. Follow these steps:

  1. Familiarize yourself with Solving the E-Waste Problem (STEP) by looking at their website and exploring some of their publications. Pick publications that provide enough “raw data” for further analysis. Likely you will find more data than you can reasonably process for this assignment. That means you will have to be selective about what you collect. You can explore information that interests to you, but I recommend you keep track of geographical information (for reasons that will become clear when we get into Fusion).
  2. Collect the information by entering it into a spreadsheet. I recommend you use a Google spreadsheet then import it into Google Fusion, but you can start from scratch in Fusion if you prefer. Or you can upload .csv, .tsv, .txt, or .kml files into Fusion if you prefer to work on a local drive rather than online.
  3. Once you have a data set organized into a document, you can move it into Google Fusion. Start playing around with different visualizations, from basic infographics to more complex map overlays. Not all graphs, tables, or maps will work with all data sets, and you may have to filter or aggregate specific subsets of data to get useful results. Keep trying different options until you find results that help you better understand the data. Revise the spreadsheet if need be. Save three to four visualizations that you find useful.
  4. Using your visualizations as a starting point, begin to write up an analysis of the data that answers at least three questions that the tables illustrate. What do your visualizations tell you that the original reports did not? What do they tell you that you didn’t know in advance? How do they begin to make sense of the e-waste problem that they document? Finally, brainstorm at least one question that the data visualizations cannot answer. Draw a hand-rendered visualization that helps answer, or illustrates, that question. Scan or photograph your drawing for use in the final report.
  5. Use the Data Analysis Template (see below) to create a report that includes your analysis, visualizations, and hand-rendered infographic. Use the hand-rendered graphic as a cover page, but feel free to reproduce a smaller version in the body of the report. You can compose the report in Microsoft Word, but should save the final draft as a PDF. Title the file using this form: LastnameFirstinitial_Data.pdf.

 


 

Data Analysis Template

I. Introduction

  1. The introduction should provide an overview of topic and data. Be sure to include relevant context and background to explain the data set in relation to STEP publications. How did you collect this data? How many reports did you use? How did you limit it to the just the items included? What’s the date and/or geographical range?
  2. Summarize your major conclusion. What is the “big answer” that you data analysis provides about this collection?

II. Body
The subsections in this portion of the report can be arranged as you see fit for your materials. However, it must include the following elements.

  1. Description of methods for collecting data.
  2. Full data summary. Don’t reproduce the spreadsheet. Instead, explain different data fields you designated as relevant. Then give readers a “big picture” summary of trends, patterns, or interesting gaps in the data collected. It may be useful to narrate to how STEP produced this data, if the reports provide that information.
  3. Statement of question answered. Tell readers what questions this data answers. Naturally, provide the relevant answers.
  4. Data analysis. Explain how you came to those answers. (Data visualizations go here.)
  5. Statement of limitations. Explain at least one question your data set cannot answer, being sure to say why and whether or not it is an important question for your purposes. (Hand-rendered infographic goes here.)

III. Conclusions

  1. Restate major conclusion, this time providing additional elaboration based on data analysis.
  2. Synthesize your findings and point toward new areas for investigation. You might also explain potential applications of your new knowledge.

 

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