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日期:2021-03-20 11:22

Assignment 1 — Information visualization

Deadline 11 a.m. on Tuesday 30/03/2021

By submitting this coursework, electronically and/or hardcopy, you state that

you fully understand and are complying with the university’s policy on Academic

Integrity and Academic Misconduct. The policy can be found at https://myuni.

swansea.ac.uk/academic-life/academic-misconduct/.

Credits for CSC337/CSCM37 25% of module

Motivation

Last night Indiana Jones was on the TV and you watched it. You thought

maybe being an archaeologist sounds like a fun career. Then you realized how

often Indiana Jones was in a life-threatening situation and decided that maybe

the leather jacket/adventuring archaeologist isn’t for you. However, you realize

you’re taking this amazing visualization course and maybe you can leverage

some of those skills to inspect certain historical places. So you decide to look at

the Pleiades dataset and see if you can find some interesting relationships.

You decide to download the locations dataset from that resource at http://atla

ntides.org/downloads/pleiades/dumps/. You have a look at the dataset and it

is a lot of complex data. Maybe you can use visualization to help make some

sense of the complex data. There is also a README file for the dataset at

http://atlantides.org/downloads/pleiades/dumps/README.txt which describes

the fields of the CSV file. There are other datasets available on that site which

you can use.

Understanding the data

As with real-world projects, acronyms and special terminology are prevalent.

Thus, you may need to conduct some research, like reading the data description

online or googling terms you’re unsure of. You may also ask questions on Discord

or http://VisGuides.org if you need help.

1

Goals of the analysis

The goal is to create visualizations that maximize our understanding of the data.

In addition to some obvious factual information, such as:

1. What is the oldest place in each location?

2. What is the distribution of structure types around the world?

3. Can you make a map of an ancient city?

4. How do newer settlements in a civilization absorb older ones?

There are also some less obvious questions, such as:

1. Are there any interesting patterns or trends in the data?

2. What are the outliers?

3. What insight can we gain from multi-variate visualizations of the data?

4. Are there any relationships between the different time ages?

Your boss shows you a bar chart they made in excel and asks for, “something like

this.” However, you know from your insightful vis lectures that single graphics of

old-fashioned pie charts, bar charts, bubble charts, and line graphs might not be

very insightful or aesthetically impressive. We resolve to do better (and maybe

get a promotion)!

Coursework: A Real-World Challenge (maximum

marks 50)

Your task is to use literate visualization to produce five different visual designs

which can convey some meaningful and hopefully interesting insight about the

data. You are required to supply five unique observations about the data (and

not just repeat the same observation over and over). For each design, fill out a

literate visualization schema as discussed in https://github.com/gicentre/litvis

/blob/main/documents/tutorials/introduction/intro2.md.

Each visualization design you create should be in a separate markdown file.

Thus you will have 5 files at the end. You don’t have to use elm-vegalite

for this coursework. You can use another tool, like Altair if you want. However,

you must include images and code in the markdown files to create your

visualizations in whatever tool you used. There is a prepared set of files at

https://github.com/SwanseaU-TTW/csc337_coursework1.

You are allowed and encouraged to further abstract the data, e.g., aggregating

data records into groups, combining some attributes together, or add new data,

e.g., cities, geographic regions. In fact, you can do anything you like to the

data and you are expected to make changes, e.g., perhaps combining some

spreadsheets together, creating new spreadsheets, or gathering data from other

sources.

2

Description Template

Provide the following information for each visual design you create. In parenthesis

are the literate visualization labels to use to identify these in the source files:

Aim (aim): Things we can learn from the visualization, e.g., from this visualization

we can see this pattern. . .

Visual Design Type (vistype): The name/type of the visual design

Image: The visualization itself as an image

Visual Mappings (vismapping): Each of the visual design mappings. Include

the data mapping information about color, shape, size, position (x,y axes),

and any other visual mappings.

Data Preparation (dataprep): Any modifications to the original data that

had to be performed to generate your beautiful image.

Improvements (improvements): What are the limitations of your visualization?

What improvements could you make to it?

A good observation requires interpretation of the resulting image that you

generate.

What is better?

Create visualizations that:

? convey information and knowledge

? aid discovery of: patterns, deviation, hierarchy, relationships and association

? identify relationships between data attributes

? depict data at different scales

? separate noise from the signal

? are intuitive to laypersons and easy to learn

? are aesthetically pleasing

Examples: Simply creating more standard pie charts, line charts, and

bar charts is not good enough. Some examples of more advanced visual

designs are treemaps, parallel coordinates, matrix charts, scatter

plots etc. You will see many examples in the lectures. If you’d like to

get a head start, you may watch the course lectures from last year on

YouTube (https://www.youtube.com/playlist?list=PLZo40sVmw_4No1_KXllf04O977PYngq1),

look at the slides on Blackboard, or read the recommended

text book[@ward:2015]. You are also encouraged to ask questions in lectures.

Describe the insight that your visualizations provide. What can we learn from

your visualizations? How are they better than a standard line, pie, or bar

chart? Also, a common mistake that students make is using a treemap to

show non-hierarchical data. Treemaps are a hierarchical data visualization

technique. Without a hierarchy, they may be even less effective than a bar chart.

However, you can create a hierarchy from your data to visualize. See the example

submission and Geng et al.[@geng:2011a] for examples.

3

Digital Maps: You are encouraged to explore the use of digital maps in your

visualizations. For a good source of digital maps, please visit the following URL:

http://commons.wikimedia.org/. You can then do a search for the relevant

maps.

Hands-On Tutorials: For extra help, there are extensive tutorials at https:

//github.com/gicentre/litvis/tree/main/documents/tutorials. There’s also a

very nice tutorial on youtube: https://www.youtube.com/watch?v=pMHmQX3

TZ8A.

You may certainly use the digital, geo-spatial maps from these tutorials in your

submission. However, you will not receive credit for the bar charts and line

charts demonstrated in the tutorial. Also, a paper called, Liquid Diagrams:

Information Visualisation Gadgets[@andrews:2010a] discusses the use of digital

maps for visualization.

Submission

Below is a list of possible tools that you can use. If you want to use a tool or

technology not on this list then please clear it with me first. If not, you will

receive a 0 for the assignment.

You are required to submit a document, which contains your name, student

number, and five narrative examples with complete template information as

in the example submission. Make sure each of your visual design types is distinct.

In other words, two Treemap layouts are two instances of one visual design.

Create a directory with your student number. Then put the final pdf, all

markdown, and all data files used. Make sure it’s laid out as:

<student number>

|- coursework.md

|- followup1.md

|- followup2.md

|- followup3.md

|- final.md

|- <studentnumber>.pdf

|- <datafile 1>

|- <datafile 2>

|- ...

Then, zip this directory. Please create a .zip file and not a tar, gzip, or any

other format. Make sure extracting this zip file properly recreates this directory.

You are required to upload the zip file to Canvas. If you only submit the

code or the pdf then you will receive a mark of 0

4

Creating a pdf from VS Code preview

The easiest way I’ve found to create a pdf from the preview is:

1) Right click on the preview window

2) Select “Open in Browser” from the menu. Your web browser should open

3) Use the print to pdf functionality of your browser

4) Once you have all 5 of these, combine the pdfs together.

Useful Links to Visualization Tools

Here is a list of visualization tools that you may use for this assignment. You

can potentially use something else but you must clear it with me first.

? elm-vegalite (Elm): https://github.com/gicentre/elm-vegalite

? Data Driven Documents (JS): http://d3js.org/

? Vega (JS): https://vega.github.io/vega/

? Altair (Python): https://altair-viz.github.io/

? VegaLite.jl (Julia): https://github.com/queryverse/VegaLite.jl

? vegalite (R): https://cran.r-project.org/web/packages/vegalite/index.html

I’ve deliberately not included packages like ggplot and matplotlib in this list

because it is not easy to produce interactive tools using them.

Tableau and Lyra are not included as I want you to have more experience with

using code to generate visualizations. These are excellent tools for prototyping

though!

To help you get started with litvis, there are a number of tutorials at https:

//github.com/gicentre/litvis/tree/main/documents/tutorials.

Both carefully prepared tutorials start from the very beginning with a description

of a sample data set and proceed step-by-step creating a selection of visual designs.

A paper called, Visual Analysis of Document Triage Data[@geng:2011a], provides

a nice overview of free information visualization tools and their features. If you

have any questions or concerns, please do not hesitate to contact the module

lecturer or the module teaching assistant.

References

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