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COMP4037 Research Methods Spring

Coursework 2: Data Visualization

Number of Credits: 25% of module

Recommended hours: 20-25 hours (for distinction level)

Reassessment Deadline: 21 Aug by 17:00

Late submissions will incur a penalty of 10% per day including weekends and bank holidays. Submit a PDF file on Moodle-a link to a submission form will be provided a few days before the deadline.

The aim of this coursework is to:

● Help you to build an understanding of the research methods in your area of interest

● Develop and assess your ability to present research in a concise visual form

● Give you practice in the use of data as evidence to support your work

This coursework supports the following Learning Objectives of the module:

● To increase understanding of the research process and the application of appropriate research approaches to given problems.

● To develop the ability to critically appraise and choose research methods, and justify their application to appropriate research problems.

● To enhance skills to present results as part of implementing a project.

Task Description and Context

Professor C was approached by a team of researchers at the University of California, Berkeley (UCB),and the Max Planck Institute for Demographic Research (MPIDR) working on a project called The Human Mortality Database (HMD). They need your help with a serious challenge. They have been collecting a massive amount of data about global mortality for many years now. The data contains information about mortality rates by age group throughout the world over the past 100-200 years (approximately).

Each data set from the project contains the following information:

1. Country Name

2. The time span for which the data is collected

3. Age range/intervals

4. Number of female deaths

5. Number of male deaths

It is a lot of temporal data. In fact, it feels like data chaos. The researchers would like to know if visualization can be used to gain insight into their data.

Download the Data: The data can be downloaded from:

https://www.mortality.org/Home/Index

You will need to register with the site.

Understanding the Data: As with real-world projects, acronyms and special terminology are used sometimes. Thus, you may need to conduct some research, like Googling various terms, in order to gain an understanding of the data and its associated terminology.

Better Than Professor C: Professor C would like to obtain and convey the insight contained in the data visually. The goal is to create visualizations that maximize our understanding of the data. In addition to some obvious factual information, such as:

● What does the distribution of a given country’s mortality age distribution look like?

● Which countries have the highest mortality?

Professor C would also like to find out some less obvious information, such as:

● Are there any interesting patterns or trends in the data?

● Are there any outlier countries or time-periods?

● What insight can we gain from multivariate visualizations of the data?

● Can we compare the mortality rates of two or more countries over time?

Professor C made some attempts to visualize the data using old-fashioned pie charts, bar charts, bubble charts, and line graphs. He was disappointed by the results, which are not

very insightful, not visually uninformative, nor aesthetically impressive. Can you do better than Professor C?

A Real-World Challenge

Selecting a tool: You are required to select an appropriate visualization tool for depicting the dataset concerned. You may have a look at the visualization resources paper for a description of available tools. See the Section called “Free, Off-the-Shelf Software Collections for Data Visualization Practitioners” in the survey paper by Liu et al (2022) in the References.

Process the data: You are allowed and encouraged to further abstract the data, e.g., aggregate data records into groups, combine some attributes together, or add new data or meta-data. 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 or creating new spreadsheets.

Your task is to produce an advanced visual design which can convey some meaningful and hopefully interesting insight into the data. You are required to supply a unique observation about the data. For your final image, fill out a description template as below. See Figure 1 for an example.

Description Template:

Provide the following information for the image you create:

● Image: The visualization itself as an image

● Visual Design Type: The name/type of the visual design

● Name of Tool: The tool that was used to generate the image

● Country: Name of country (or countries) data shown

● Years: the year(s) or time-span of data shown

● Visual Mappings: Each of the visual design mappings. Include the data mapping information about color, position (x,y axes), shape, size, hierarchy, and any other visual mappings.

● Unique Observation: Things we can learn from the visualization, e.g, from this visualization we can see this pattern… Make sure you describe where and how in the imagery your unique observation can be seen. Is it shown in the accompanying video? If so, at what time in the video? Note that outliers are normally very interesting. They indicate that something unusual is happening.

● Data Preparation: Any modifications to the original data that had to be performed to generate your beautiful image.

● (Optional) URL to screen-capture demo showing any animation or user-interaction. You can include a link to a YouTube or Vimeo video to demonstrate any interaction or animation (similar to CW1). I recommend two minutes maximum time. Due to the large volume of submissions, we will not have time to look at and interact with Tableau public software web pages. Therefore, we require a video.

● (Optional) URL to source code: if you used a programming language like R or Python you can provide a link to GItLab or GitHub where your code is stored.

A good observation requires interpretation of the resulting image that you generate. Hint: you can compare mortality rates of 2020 with previous years as one of your insights or observations.

Also, if your visual design features interaction or animation, you may upload a screen-capture demonstration to YouTube and provide the link in the PDF you submit. Note that all user-interface components should be shown in English.

Better than Professor C: You are to do better than Professor C! That means you are required to use more advanced visual layouts than typical line graphs, bar charts, pie charts, and bubble charts. That means the following are not allowed: bar charts, pie charts, line charts, and bubble charts, however, you can use these simple charts to support more advanced visual designs, in other words, as complementary charts to support an interesting observation.

What is Better? Create a visualization that:

● conveys information and knowledge

● enables discovery of: patterns, deviation, hierarchy, relationships and association

● identifies relationships between data attributes

● depicts data at different scales

● separates noise from the signal

● is intuitive to laypersons and easy to learn

● is aesthetically pleasing

Describe the insight that your visualizations provide. What can we learn from your visualization? How is it better than a standard line, pie, or bar chart? You are not required to answer all of these questions. They merely serve as examples of what you could discuss when providing a unique observation about the data through the lens of your imagery.

Examples: Some examples of more advanced visual designs are treemaps, parallel coordinates, matrix charts, radar area charts, and maps.

Very Common Mistakes: A common mistake that students make is using a treemap to show nonhierarchical 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.

Digital Maps: You are encouraged to explore the use of digital maps in your visualizations. Using digital maps will yield bonus points. For a good resource on finding digital maps,

please visit refer to the survey paper on visualization resources by Liu et al 2022 in the References. You may certainly use the digital, geo-spatial maps from the Tableau tutorials in your submission. However, you will not receive credit for the bar charts and line charts demonstrated in the tutorial.

Extra Help

● Supplementary Questions and Answers:Research Methods: Questions and Answers on Courseworks 2 & 3

● More Questions and Answers on Courseworks 2 & 3 https://youtu.be/6DJYgh8B1uE

● Supplementary Lectures: You will see many examples in the supplementary data visualization lectures. If you’d like to get extra help, you may watch the course lectures on YouTube: Data Visualization Classes 2019 - YouTube

● Hands-On Tutorials: For extra help, you may also watch either one of the following live, hands-on, video tutorials of how to create a visual design including a geo-spatial map:

● Visualization in Practice: A Live Introduction and Hands-on Demonstration of Tableau

Tableau for Absolute Beginners: A Live Introduction and Hands-on Demonstration

● Applied Visualization using Tableau: A Hands-On Tutorial and Demonstration

Applied Visualization using Tableau: A Hands-On Tutorial and Demonstration

● Help from Visualization Experts: You may also ask any visualization questions on the Vis Guides Website: http://VisGuides.org

● Help from Mortality Data Experts: Or you may contact any member of the HMD project’s Research Team: https://www.mortality.org/People/ResearchTeam

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.

Assessment Criteria

See the detailed assessment criteria in the appendix after the references.

Academic Integrity: This is an individual assessment that should consist of your own unaided work. You are permitted to use any material (e.g., diagrams and quotations) from the research paper that your video is based on, but you must make it clear when you are quoting from the paper. The University has detailed advice about academic integrity and submissions that demonstrate a lack of that integrity will be treated under appropriate disciplinary procedures.

References

(Liu et al, 2022) Xiaoxiao Liu, Mohammed Alharbi, Jian Chen, Alexandra Diehl, Elif E Firat, Dylan Rees, Qiru Wang, and Robert S Laramee, Visualization Resources: A Survey, Information Visualization, Volume 22, Issue 1, pages 3-30 ( PDF file, web page, https://doi.org/10.1177/14738716221126992 )

Example of What We Are Looking For

Figure 1: Your task is to produce something like this. The dataset used for this example is different from the one used for this assignment.

Assessment

Grade 80-100. Exceptional insight into the data is provided, similar to that of a good PhD student. The quality of the image and the description provided are at a publishable level. The visual design is advanced. Every aspect of the template is identified and completed at a publishable level.

● The visual design is high quality and at a publishable level.

● The name of the tool and countries are provided

● The years/timespan conveyed in the image are provided

● The Visual Mappings are completely described where applicable: color legend with labels, axis labels, position, size, shape, hierarchy

● A Unique Observation is provided that is unique, non-trivial, and could not be simply guessed using prior knowledge or by looking at the raw data.

● The Data Preparation is described at a publishable level

Grade 70-79. Demonstrates a thorough understanding of the data and the image, explanations are very clear for all components in the image template. The visual design is advanced. Very good insight is provided. The writing has a very good structure and the work has been carried out using a professional standard.

● The visual design is high quality.

● The name of the tool and countries are provided

● The years/timespan used in the image are provided

● The Visual Mappings are completely described where applicable: color legend with labels, axis labels, position, size, shape, hierarchy

● A Unique Observation is provided that is non-trivial, and could not be simply guessed using prior knowledge or by looking at the raw data.

● The Data Preparation is described at a very good level

Grade 60-69. Demonstrates a decent understanding of most of the aspects of the data and the visual design, explanations are largely clear for most components of the template, the submission has a decent structure, and each part is completed, though perhaps falling short of a thorough, professional writing and image quality. The chosen visual design is advanced.

● The visual design is high quality.

● The name of the tool and countries are provided

● The years/timespan used in the image are provided

● The Visual Mappings are described where applicable: color legend with labels, axis labels, position, size, shape, hierarchy, although falling short of a thorough and professional level.

● An Observation is provided that is unique, non-trivial, and could not be simply guessed using prior knowledge or by looking at the raw data.

● The Data Preparation is described at a good level.

Grade 50-59. Demonstrates some understanding of the data and the visualization, the descriptions and explanations of each component of the template are generally okay but sometimes lack detail, are ambiguous, or contain errors. There is some attempt to structure the submission, and a decent (if perhaps) informal attempt has been made at the writing, image, and the presentation. The insight provided is rather obvious or trivial. The visual design is fairly advanced.

● The visual design is of reasonable quality.

● The name of the tool and countries are provided

● The years/timespan used in the image are provided

● The Visual Mappings are described where applicable: color legend with labels, axis labels, position, size, shape, hierarchy, at an adequate level.

● A Unique Observation is provided but is not unique, trivial, and could be simply guessed using prior knowledge or by looking at the raw data.

● The Data Preparation is described.

Grade 40-49. There is some understanding of the data and visualization, but mostly the descriptions are vague, lack a lot of detail, or have errors. The supplied image is very basic with no special insight given. There is a vague structure to the submission but it isn’t easy to follow throughout, some attempt at writing.

● The visual design is low quality.

● The name of the tool and countries are not provided.

● The years/timespan used in the image are not provided

● The Visual Mappings are described poorly.

● A Unique Observation is provided but is not unique, trivial, and could be simply guessed using prior knowledge or by looking at the raw data.

● The Data Preparation is poorly described.

Grade 30-39. A little bit of understanding and explanation of the data and visual design, but most of the components in the template description are not explained in much detail or contain significant errors. The submission doesn’t have much of a structure, there is some attempt at writing. No significant insight is conveyed by the image.

● The visual design is low quality.

● The name of the tool and countries are not provided.

● The years/timespan used in the image are not provided

● The Visual Mappings are described poorly or erroneously.

● A Unique Observation is provided but is not unique, trivial, and could be simply guessed using prior knowledge or by looking at the raw data.

● The Data Preparation is poorly or erroneously described.

Grade 20-29. A few aspects of the data and visualization have been understood, but overall the submission doesn’t show much understanding of the visual design nor the explanation of different components of the template description. The submission is poorly structured and contains little coherent attempt at writing. No effort is made to convey insight into the data.

The visual design is too basic.

● The visual design is low quality and too basic.

● The name of the tool and countries are not provided.

● The years/timespan used in the image are not provided

● The Visual Mappings are described poorly or erroneously or are not provided.

● No serious attempt at a Unique Observation

● No serious attempt describing the Data Preparation.

Grade 10-19. Shows some awareness of the data and the associated visualization, but doesn’t go much beyond mentioning a few keywords and doesn’t have any meaningful structure or attempt at writing. The supplied image is low quality or trivial.

● The visual design is low quality or contains errors.

● The name of the tool and countries are not provided or contain errors.

● The years/timespan used in the image are not provided or contain errors.

● The Visual Mappings are described poorly or erroneously or are not provided.

● No serious attempt at a Unique Observation

● No serious attempt describing the Data Preparation.

Grade 0-9. No or minimal attempt

Areas that can be improved

__ The quality of the image supplied could be improved.

__ The visual design could be more advanced.

__ The visual design type (the name of the visual design) is not correctly identified

__ The name of the tool used is not provided or incorrect