CS382:Unit-mashup

Visualization

Overview

The goal of this unit is to teach students to:

• Understand the goals of visualization.
• Know what the issues involved in visualization are.
• Be able to recognize and reason about the different types of visualization.
• Be introduced to a sampling of the tools used to visualize data.

Background Reading for Teachers and TAs

• web tool for non-programmers for making mashups
• chapter 1 of book on power of geo mashups
• Wikipedia page on Information Visualization
• Wikipedia page on Visualization
• "The Visual Display of Quantitative Information" by Edward Tufte
• "The Elements of Graphing Data" by William Cleveland

Reading Assignments for Students

• Needs to be created I think Agreed.

Lecture Notes

Lecture 1

Introduction

At this point students have already created/worked with a couple models and created basic graphs to visualize them. Talk about how even with just the simple models created so far, understanding the data is hard without having a visual representation of it.

Visualization is a graphical representation of data for the purpose of allowing humans to understand aspects of the data.

Show Gapminder and go through a good example. Talk about how this way of presenting the data makes information immediately obvious. Just having Introduce Tufte as one of the people to help formalize the notion of visualization.

Tufte's aspects of visualization, just a run through (From "The Visual Display of Quantitative Information"):

• Show the data.
• Induce the viewer to think about the substance rather than about the methodology, graphic design, the technology of graphic production, or something else.
• Avoid distorting what the data have to say.
• Present many numbers in a small space.
• Make large data sets coherent.
• Encourage the eye to compare different pieces of data.
• Reveal the data at several levels of detail, from broad overview to the fine structure.
• Serve a reasonable clear purpose: description, exploration, tabulation, or decoration.
• Be closely integrated with the statistical and verbal descriptions of a data set.

• Issues of visualization
  • Objective. There is always a goal or objective when visualizing by which one can judge effectiveness. In this class I don't think things like marketing should be mentioned but certainly the difference between using visualization to explore data and to explain data to others.
  • Data Selection. When given a set of data, often one wants to single in on a subset of that data to look at.
  • Psychology. Visualization is fundamentally about how humans perceive visual information so you have to think about the ways in which you want to take advantage of human psychology.
  • Systemization. While elaborate visualizations like the Napoleon one are very compelling, in Computer Science we are often more interested in visualizations that can be systematically generated.
• Go through a couple of examples of creating visualizations referring back to Tufte's list and the issues.
• Types of Visualizations (A sampling)
  • Tables
  • Graphs
  • Charts
  • Sparklines
  • Time Series
  • Data maps and mashups

Examples

Show some examples and ask what their pros and cons are and what insights they allow. Go through Tufte's list and identify which rules the visualizations follow.

• March of Napoleon

Lab

Learning spreadsheet visualization tools and Google Maps to gain, respectively, immediately practical and useful skills and an alternate way to think about data.

Highlevel outline: Students will be instructed to sample temperatures at several different points within some region of campus. They will pick their own points, recording each with a provided GPS unit. They will then enter the data into a spreadsheet and graph the results using different kinds of graphs. After that, each group will combine their data into one Google Map, putting pushpins in for each sample point and coloring the pin appropriately.

Process

1. With provided thermometer, go to your group's assigned region ( Region Map )
2. Pick ten points in your region and sample their temperatures. Record each point's coordinates. Be sure to pick points such that you will get a variety of temperatures (ie, pick tree and building shaded spots, sunny parking lots, points near steam tunnel exhaust grates)
3. Enter data into Open Office, with a column for coordinates and a column for temperature readings. Generate bar graphs based on this data.
4. Average your temperatures into one value and add it to the collaborative class Google Docs spreadsheet. Graph the averages.
5. Using your datapoints, add push pins to the collaborative class Google Docs map. Color the pins based on temperature ranges. Work with the other lab groups to come up with a sensible color scheme based on the range of temperatures you've found.

Write-up

• Which tool was most appropriate for visualizing this data and why?
• What was the most difficult part of the lab? Why?
• Explore alternative ways to get the same data (instead of getting it yourself). Are there other data sources that would provide granular enough temperature data (Hint: use google)?
• Describe the collaborative process with Google Spreadsheets and Google Maps. What was difficult? What was easier? Compare and contrast this experience with non-collaborative software. How would each group's data been collected into one document?

Software

• Web Browser
• Google account
• Open Office

Bill of Materials

• GPS
• Thermometer

Lab Notes

• It is impossible to create 3 axis graphs in open office. Perhaps add more detail on how to create a working bar graph in OO.
• GPS is mighty inaccurate. I am working on trying to get the differential working but perhaps it would be advantageous to have points taken from multiple areas.
• We need more thermometers.
• I didn't do the google docs part as I had no collaborators.
• The instructions need to be more detailed on how to add points to a map on google maps

Samuel Wein

Evaluation

CRS Questions

• Whats the best type of visualization for X set of data?
• XXX
• XXX

Quiz Questions

• XXX A question.

Visualization - Metadata

Scheduling

Should come before anything too complicated, but after basic modeling concepts.

General Education Alignment

Analytical Reasoning Requirement

Abstract Reasoning

From the [Catalog Description] Courses qualifying for credit in Abstract Reasoning typically share these characteristics:

• They focus substantially on properties of classes of abstract models and operations that apply to them.
  • None.
• They provide experience in generalizing from specific instances to appropriate classes of abstract models.
  • None.
• They provide experience in solving concrete problems by a process of abstraction and manipulation at the abstract level. Typically this experience is provided by word problems which require students to formalize real-world problems in abstract terms, to solve them with techniques that apply at that abstract level, and to convert the solutions back into concrete results.
  • None.

Quantitative Reasoning

From the [Catalog Description] General Education courses in Quantitative Reasoning foster students' abilities to generate, interpret and evaluate quantitative information. In particular, Quantitative Reasoning courses help students develop abilities in such areas as:

• Using and interpreting formulas, graphs and tables.
  • Complete. They will be doing many graphs and tables in this Unit.
• Representing mathematical ideas symbolically, graphically, numerically and verbally.
  • Partial. This unit definitely attempts to represent something graphically, but I don't think quite in the way that they mean.
• Using mathematical and statistical ideas to solve problems in a variety of contexts.
  • Partial. Looks at using statistical ideas to solve problems in the single context of Visualization.
• Using simple models such as linear dependence, exponential growth or decay, or normal distribution.
  • None.
• Understanding basic statistical ideas such as averages, variability and probability.
  • None.
• Making estimates and checking the reasonableness of answers.
  • None.
• Recognizing the limitations of mathematical and statistical methods.
  • Partial. Visualization does speak to the limitations of both visualization itself and the model a visualization represents.

Scientific Inquiry Requirement

From the [Catalog Description] Scientific inquiry:

• Develops students' understanding of the natural world.
  • None.
• Strengthens students' knowledge of the scientific way of knowing — the use of systematic observation and experimentation to develop theories and test hypotheses.
  • None.
• Emphasizes and provides first-hand experience with both theoretical analysis and the collection of empirical data.
  • Complete. Deals with collection of data from data sources and theoretical analysis of how to visualize it.

Scaffolded Learning

This unit asks students to take the types of considerations they used to build graphs not only in the previous couple units but during their entire academic history and extend them into a more general framework of visualization.

Inquiry Based Learning

At the moment there isn't a whole lot of this in the unit. In the lab the students will have an opportunity to explore what they can do with google maps and with graphs.

Visualization Mechanics

To Do

Consider doing something based on IBM's Many Eyes tool.

Comments

Fixed both. With a tool as sporty as Google Earth available to do geographic visualizations wouldn't it be nice to use that too in conjunction with the Census data?

Include a visualization with KML and Google Earth

Seriously consider OpenOffice

Authorship

Matthew Edlefsen