Module 4 - Session 1 - Data Visualisation

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# Module 4 - Session 1 - Data Visualisation
## Working effectively with data
### CivicDataLab
### 2021/10/12 (updated: 2021-10-12)

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# The need to visualise data

1. To understand a concept visually

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2. To demystify what a data point represents

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3. For making decisions - Spatial data visualisations are good examples of how a map can help in simplyfying complex geographical patterns that further contributes to effective decision making.

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4. To explore an idea

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5. To engage with a diverse set of stakeholders

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6. **It's fun**

[SeriesHeat](https://vallandingham.me/seriesheat/#/) by [Jim Vallandingham](https://vallandingham.me/)

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# The Data Viz Quadrant

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![](../session-1/assets/data_viz_types.png)
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## Idea Illustration

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![](../session-1/assets/idea_illustration.png)
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## Idea Generation

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![](../session-1/assets/idea_generation.png)
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## Visual Discovery/Exploration

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![](../session-1/assets/data_exploration.png)
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## Every data viz

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![](../session-1/assets/data_viz.png)
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# What restricts us from visualising data

1. Data, that is well documented

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2. Missing context about the domain or subject matter expertise

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3. Familiarity with the tools to process and visualise data

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4. Asking the right questions

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5. Building convincing narratives tailored for specific stakeholders

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6. Unequal distribution of data literacy

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# What restricts us from visualising data

1. Data, that is well documented
2. Missing context about the domain or subject matter expertise
3. **Familiarity with the tools to process and visualise data**
4. Asking the right questions
5. Building convincing narratives tailored for specific stakeholders
6. Unequal distribution of data literacy

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# Telling stories using Data

**Stories** 
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| **Audience** 
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| **Medium**

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## Stories

1. Factoid Stories - _Why does this one data point stand out from the others?_

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2. Interaction Stories - _Why do these do aspects of the data change with each other?_

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3. Comparison Stories - _What is the meaningful difference between these parts?_

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4. Change Stories - _What made this part of the data change from this to that?_

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5. Personal Stories - _How does this data connect to the lives of the audience?_

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## Audience or Stakeholders

Knowledge about the intended user of a data visualisation. What can it change ?

1. **Presentation of information**

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2. **Content granularity**

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3. **Visualisation Narratives**

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4. **Intended Outcomes**

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## Medium to communicate

1. Static (Media articles, Infographics) vs Interactive (Web Applications, Websites, etc)

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2. Language to communicate

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3. Structuring the narratives

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4. Accessibility

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5. Stakeholder specific

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## Activity 1 - Building a Narrative

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[Convince Me](https://databasic.io/en/culture/convince-me) - _To practice making data-driven arguments that try to convince different people_
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1. Share a data viz with the group

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2. Questions to ask:
  1. Is there an intended audience ?
  2. Is there an argument being made ?
  3. Are they trying to get the audience to do something ?
  4. Who are the stakeholders in the system it describes ?

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3. Pick people from the group as identified stakeholders

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4. People from the audience use the data to make an argument to convince the stakeholders

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5. If the stakeholder gets convinced, they take a step forward

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6. Ask people about why they made an argument and why they thought it would convince a certain stakeholder

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7. Ask the stakeholders what convinced them and what didn't

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![](../session-1/assets/arguments.png)

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![](../session-1/assets/pitfalls.png)

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## Data Stories

1. [What's really warming the world ?](https://www.bloomberg.com/graphics/2015-whats-warming-the-world/) _Evaluating arguments_

2. [US Gun Deaths](https://guns.periscopic.com/) - _Gun Control vs Gun Rights_

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# Building accessible data visualisations

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[Vital Coronavirus Information Is Failing the Blind and Visually Impaired](https://www.vice.com/en/article/4ag9wb/vital-coronavirus-information-is-failing-the-blind-and-visually-impaired)
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Few things to consider while **designing data viz** solutions:

1. Use **alt text** to provide a one-sentence summary of the chart

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2. Provide **access to raw data**

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3. If you’re using color, think of how you can convey the same distinctions to people with **colorblindness**

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4. If you’re using visual media, consider how **blind people can consume your content**.

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5. If you’re using animation, factor in people with epilepsy, ADHD, or cognitive disabilities, and **give the user control of the animation**.

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6. If you’re using sound, such as **sonification**(use of non-speech audio to convey information or perceptualize data) or spoken aspects, **think of the deaf**.

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_Look at accessibility as an opportunity to hone your skills, to move beyond the ordinary, and to provide an intentional experience for your users._
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## Accessibility Resources

1. [Why Accessibility Is at the Heart of Data Visualization](https://medium.com/nightingale/accessibility-is-at-the-heart-of-data-visualization-64a38d6c505b)
2. [Accessible COVID-19 Pandemic Data](https://covid.ski.org/)
3. [Dataviz Accessibility Resources - Github](https://github.com/dataviza11y/resources)
4. [Accessibility - Fireside Chat - Data Visualization Society](https://www.youtube.com/watch?v=Aqx5PQwds80)

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## Activity 2 - Reading a data viz

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**Deconstructing a data viz** - _Build your visual skills by taking apart an example visualization_
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**Why** - _This activity helps you build the skills (data processing, design, storytelling) by taking apart work that others have done (the good and the bad) to build your critical eye._

**How** - _Discuss the visualisation_

1. The data it uses.

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2. The ways it shows that data.

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3. The narrative it is trying to tell.

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4. Evaluating whether it combines those to tell the data story well

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## Activity 2 - Examples

1. [Line of Succession](https://www.legallyindia.com/images/kianganz/vdyva201_d8d607d945b602eee0be79eb606d9c61.jpg)

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2. [IJR - 2021 - Vacancies- Pg 33](https://www.tatatrusts.org/Upload/pdf/ijr-2020-overall-report-january-26.pdf)

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# Data to Viz - Selecting charts

**Resource** - [From Data to Viz](https://www.data-to-viz.com/)

**Features**

1. Helps in selecting charts to build as per the type of datasets available

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2. Basic description of what works where

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3. Code to implement the charts across multiple platforms

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4. Lists common mistakes for all chart types

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5. Linked data stories to check how different charts were used for visualisations.

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6. Open Source - [Link](https://github.com/holtzy/data_to_viz)

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# Data Viz - Things to avoid

1. [Spotting visualisation lies](https://flowingdata.com/2017/02/09/how-to-spot-visualization-lies/)

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2. [Examples of quantitative info that is poorly designed for communication](http://www.perceptualedge.com/example19.php)

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# Collaborating with external consultants

A **process** to convey the requirements.

What should be shared:

1. Project Information - What is needed ?

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2. Call-to-Action - How will the output be used by different stakeholders ?

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3. Audience - Who are the stakoholders ?

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4. Use of Data Visualisation - How will the viz be used e.g. on website, printed reports, social media, etc

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5. How to make the viz more accessible to a diverse set of stakeholders

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6. Data
  1. Source
  2. Data Biography
  3. Metadata about the variables
  4. Assumptions

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7. Timelines

[Viz for Social Good - Justice and Peace project](https://www.vizforsocialgood.com/join-a-project/2019/8/1/justice-and-peace)

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# Queries and Feedback