2024-2025 InfoVis project

Goal

Produce an interactive visualization that allows the exploration of the data set or that conveys interesting information about the dataset.

Instructions

You are expected to:

• formulate questions that should be answerable using information present in the data set (see below) and
• design interactive visualizations that can help answer those questions
• form groups and converge on questions and visualisations for the group
• implement some of those visualizations (using d3.js or anything else that you code)

Tentative Schedule

Each bold entry is an assignment, and as such should be send by email at blanch@imag.fr.

Dataset

This year dataset is about characters of the Game of Thrones television series.

The data set is available at https://gricad-gitlab.univ-grenoble-alpes.fr/blanch-ens/2024-got. The README.md file gives some information about the structure of the data tables.

You can download an archive of the data that includes some sample of visualizations with d3.

Questions

The main interests of the dataset are the relations between characters (family relations, allegiances, who killed who, etc.) and the temporal attributes (given by the years and the season/episode/scene structure).

Interrogations about the data set can belong to different main categories. Below are some sample questions, you will have to expand this list with your own questions:

• getting global insights, e.g.:
  • What is the distribution of the appearance time accros the characters?
  • …
• focusing on subsets
  • How is composed the Stark family?
  • …
• comparing subsets
  • What are the relative times of appareance of male/female characters?
  • …

Visualisation design

Choose 2 questions, one about global insights, one about comparing subsets, that involve time and/or relations between characters. Propose visualisations to answer those questions. If the questions are linked, that can be a single visualisation, provided it allows to first concentrate on global insights, then onto comparison.

You will have to provide explicitly, for each visualisation:

• the visual mapping, e.g. using a table (à la Card & Mackinlay)
• the design rational that led to this visual mapping
• a precise description of the interaction (if the user does this, then …)
• sketches of the visualisations to explain the parts of the visualisation that can not be captured by the visual mapping (interaction, and mapping details)

Do not limit yourselves to what you would be able to implement. For this part, consider you will have a staff of engineers working for you, and you just have to give a specification of your visualisations.

Implementation

Form groups of 2-3.

Select one question per main category. Implement visualisation(s) to answer those questions. It should allow to go from an overview of the dataset to the details that allow to gain insights on the question. It can be a single visualisation, where interaction allows to go from the overview to the details. It can consist in multiple more simple visualisations if needed.

You have to code your visualisations. It means that given the dataset and your code, I should be able to run your visualisation on my computer. You can use javascript/d3 (+ other libraries), python (+dependencies if they are available in pypi), R … You can use jupyter notebooks, RStudio. If the tool you are planning to use is not explicitly in this list, you have to ask if you can use it.

You are expected to deliver your work by email at blanch@imag.fr with:

• an archive of your code and a readme that lists the dependencies of your code and gives the steps to run your visualisation (zip, tgz, no rar)
• a report that explains how the visualisations are answering the questions and if, how and why they differ from your initial designs (pdf, md, html, no doc).

Defenses

Expectations

You are expected to present your visualizations, and to explain how they help answer the questions you choose to investigate. You are expected to justify the design choices for the visualizations, i.e., why is the visual mapping you choose pertinent given the data at hand and the insights you are looking to find. You are expected to send by email at blanch@imag.fr the material used for the defense.

Defenses schedule

Past projects

• 2022-2023 CO2 Greenhouse Gas Emissions 2022
• 2021-2022 Global Gender Gap indicies visualized
• 2020-2021 Carbon emmisions by work travels for a research lab.
• 2019-2020 World Inequality Database: income inequalities visualized
• 2018-2019 OpenFoodFacts visualized
• 2017-2018 approval voting and evaluative voting during the 2017 french presidential election
• 2016-2017 BBC's 100 greatest films of the 21st Century