Visualising Information: Uses and Abuses of Data

"A picture is worth a thousand words," but only if you know how to read it. Digital technology has made charts, maps, and data visualisations easier to create and share than ever. Whether we consider climate change graphs, invasion maps, or visualisations of artificial intelligence, well-designed charts and maps can be enlightening, but they can also be ambiguous or misleading. Indeed, we are often ill-equipped to approach visualised information critically.

In this course, you will learn to engage with information visually. You will learn to recognise and critique oversimplifying, biased, or misleading forms of visual representation and to create your own visualisations to explore and communicate data that matters to you. Through examples from a wide range of academic disciplines - including such fields as economics, literature, meteorology, history, urban design, and computer science - you will discover key principles of exploratory and public-facing data visualisation and learn how to create your own charts and maps.

Historically, data visualisation has often been used to discriminate, control, and police. In this course, you will also explore interventions by critical data scientists, scholars, and activists who visualise data to expose injustice, challenge unfair classification systems, and speak truth to power.

The course allows you to formulate your own questions and answer them using a suite of digital tools that allow you to develop and present your argument through visualisation and narrative.

Note that this course does not involve any coding and does not require any previous technical knowledge.

Explore uses (and abuses) of visualised information in different domains of knowledge, from infection charts to invasion maps.

Allow you to master to some of the most important data visualisation tools used across disciplines to create interactive visualisations, maps, and network graphs

Help you, regardless of your discipline or background, to become more vigilant and reflective users of visual information

Enhance your employability by allowing you to develop technical, critical, and creative skills needed to thrive in sectors that work with data and visual information

Identify the opportunities and limitations of data visualisation

Decide when a visualisation tool can be useful for specific questions in your subject area

Critically reflect on how data modelling and visualisation choices influence the interpretation of the data
Evaluate different types of projects undertaken in text mining, network analysis, and digital mapping

Use digital tools to collect, analyse, and explore different types of data, and create high quality maps and charts

Present information and arguments orally, verbally and visually with due regard to the target audience

Why We Visualise

Visual Variables

Thinking with Charts

Visualising Space: Maps

Deceptive Visualisations

The unit includes contributions from leading researchers from a broad range of disciplines, including history, computer science, sociology, and meteorology and others.

The unit is made up of 5/10 online modules (released at intervals) and 5/10 face-to-face seminars that include practical tutorials and discussions in the Digital Humanities Lab, which is equipped with computers and large screens.

The unit is interactive and uses a variety of learning materials, including historical and contemporary visualisations from a broad range of disciplines.

Develop an awareness of the opportunities and limitations of digital tools and visual information

Evaluate different types of projects undertaken in text mining, network analysis, and digital mapping

Recognise how charts and maps can both enhance and limit our understanding of a wide range of phenomena 

Read, critically evaluate, and apply literature on distant reading, network analysis, mapping, and data visualisation

Decide when a digital tool can be useful for specific questions in your subject area

Critically reflect on how data modelling and visualisation choices influence the interpretation of the data

Remain vigilant for bullshit contaminating your information diet

Develop a critical perspective on maps and charts and learn to identify misuse, oversimplification, or misleading use of colour, symbology, or scale and act on your criticism by creating visualisations of your own 

Learn to use some of the most important tools currently employed in the humanities and social sciences and develop a deeper proficiency in at least one technology of your choosing

Identify a question amenable to digital analysis and explore the use of geospatial, network, distant reading techniques and concepts to answer it

Use digital tools to collect, analyse, and explore different types of data  

Create high quality maps and charts  

See a digital research project from inception to completion  

Acquire practical skills using a range of different digital applications

Present information and arguments orally, verbally and visually with due regard to the target audience

Think creatively how to develop and communicate your work

Ongoing assessments (10%)

500-word critical discussion of a self-selected visualisation (35%)

1500-word essay with a self-designed visualisation (55%)

Feedback on assignments

Office hours

Battle-Baptiste, Whitney, and Britt Rusert, eds. W. E. B. Du Bois’s Data Portraits: Visualizing Black America (Princeton Architectural Press: 2018).

Cairo, Alberto. How Charts Lie: Getting Smarter about Visual Information (Norton: 2019).

Drucker, Johanna. Graphesis: Visual Forms of Knowledge Production Cambridge, Massachusetts: Harvard University Press, 2014.

Healy, Kieran. Data Visualization: A Practical Introduction (Princeton University Press, 2018).

Ware, Colin. Information Visualization: Perception for Design (Morgan Kaufmann, 2019).

Yau, Nathan, Data Points: Visualization That Means Something (Wiley & Sons: 2013).