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IM949 - Data Visualisation in Science, Culture and Public Policy

Module Outline

In this module students will learn about the opportunities and challenges opened up by the growing role of data visualisation in contemporary science, culture and public policy. It will introduce key concepts for understanding the importance of data visualisation as a form of knowledge, communication, persuasion and engagement as well as state-of-the-art approaches to using data visualisation as a way of knowing and intervening in the world and in society - such as ethnography, design research and inventive methods. Student learning will be supported through the exploration of real-world examples: by reviewing paradigmatic cases, such as the Blue Marble (images of planet earth as seen from space), topographic maps of disputed territories, and visualisations of air pollution, students will learn to reflect on the wider impact of data visualisation on public understanding, cultural awareness, policy and decision-making and societal change.

The module consists of two parts, with the first part offering lectures and seminars introducing guiding ideas from social, cultural and political theory, science and technology studies, as well as digital and environmental humanities on the key importance of visualisation in science, culture and democracy. The second part will focus on the introduction of specific methodological approaches to the use of data visualisation in social, creative, participatory and policy research. Throughout the module, and linking the two parts, students will be engaging with cultural, social and public issues through real-world examples of data visualisation. The module then combines lectures, seminars and assignment-based work, which together will equip students to recognise, analyse and appreciate the wider affordances of data visualisation for knowledge, intervention and change.

To sum up, the module will introduce concepts, methods and empirical cases key to understanding the affordances, power and limitations of data visualisation in science, culture, and public policy, with student engagement with the empirical cases providing continuity throughout the module: supported by group assignments, students will be exploring both concepts and methods by applying and thinking these through in relation to the cases. This will form the basis of the group presentations in the last week.

Module Convenor

Nerea Calvillo Gonzalez


For 20 CATS
  • 60% Essay, 2,500 words;
  • 40% Project Presentation Report, 1,500 words.
For 30 CATS
  • 70% Essay, 4,500 words;
  • 30% Project Presentation Report, 1,500 words.

Indicative Syllabus

Week 1: Introduction: Data, Visualisation & Culture Now

Week 2: Vision as Knowledge, Culture and Democracy

Week 3: Social Studies of Visualisation: Science, Power, Intervention

Week 4: Visualisation across Culture/Nature

Week 5: Data Visualisations in Counter-Publics and Political Resistance


Week 7: Data Visualisation As Public Engagement

Week 8: Data Visualisation in Public Policy

Week 9: Data Visualisation and Ethnography

Week 10: Module Review and Final Presentations

Indicative Reading List

Arnheim, R. (1980). A plea for visual thinking. Critical Inquiry, 6(3), 489-497.

Calvillo, N. (2019). Digital Visualizations for Thinking with the Environment. Digital STS: A Field Guide for Science & Technology Studies, 61.

Dávila, P. (2019) Diagrams of Power, Eindhoven: Onomatopee 168,

Drucker, J. (2020) Visualization and Interpretation: Humanistic Approaches to Display, Cambridge: MIT Press.

Engebretsen, M. and Kennedy, H. (2020) Data Visualization in Society, Amsterdam: Amsterdam University Press

Ezrahi, Y. (2012). Imagined democracies: Necessary political fictions. Cambridge University Press.

Guggenheim, M. (2015). The media of sociology: tight or loose translations? The British journal of sociology, 66(2), 345-372.

Hall, P. A. (2014). Counter-mapping and globalism. Design in the borderlands. Abingdon: Routledge, 132-50.

Haraway, D. (1984). Teddy bear patriarchy: Taxidermy in the garden of Eden, New York City, 1908-1936. Social Text,

(11), 20-64. Healy, K. (2018). Data visualization: a practical introduction. Princeton University Press.

d’Ignazio, C., & Klein, L. F. (2016). Feminist data visualization. Workshop on Visualization for the Digital Humanities (VIS4DH), Baltimore. IEEE..

Jay, M., & Ramaswamy, S. (Eds.). (2014). Empires of vision: A reader. Duke University Press.

Kennedy, H., & Allen, W. (2016). Data visualisation as an emerging tool for online research. The Sage handbook of online research methods, 307-326.

Kennedy, H and Hill, RL (2018) The Feeling of Numbers: emotions in everyday engagements with data and their visualisation. Sociology, 52 (4). pp. 830-848.

Kimbell, L., & Bailey, J. (2017). Prototyping and the new spirit of policy-making. CoDesign, 13(3), 214-226.

Kimbell, L. (2015). Applying design approaches to policy making: discovering policy lab. Discussion Paper. University of Brighton,

Toscano, A., & Kinkle, J. (2015). Cartographies of the Absolute. John Hunt Publishing.

Kurgan, L. (2013) Close up at a distance, New York: Zone books

Latour, B. (1986) Visualisation and Cognition: Drawing Things Together» in H. Kuklick (editor) Knowledge and Society Studies in the Sociology of Culture Past and Present, Jai Press vol. 6, pp. 1-40

Marres, N., & de Rijcke, S. (2020). From indicators to indicating interdisciplinarity: A participatory mapping methodology for research communities in-the-making. Quantitative Science Studies, 1(3), 1041-1055.

Masud, L., Valsecchi, F., Ciuccarelli, P., Ricci, D., & Caviglia, G. (2010, July). From data to knowledge-visualizations as transformation processes within the data-information-knowledge continuum. In 2010 14th international conference information visualisation (pp. 445-449). IEEE.

Mitchell, W. T. (1995). Picture theory: Essays on verbal and visual representation. Chicago: University of Chicago Press.

Mitchell, C., De Lange, N., & Moletsane, R. (2017). Participatory visual methodologies: Social change, community and policy. London and New York: Sage.

Moats, D and J. Perriam (2017). ‘How Does it Feel to be Visualized: Redistributing Ethics’ in Internet Research Ethics for the Social Age: New Cases and Challenges. Zimmer, M and Kinder-Kurlanda, K eds, New York: Peter Lang

Niederer, S. and G. Colombo (2019):. "Visual methodologies for networked images: Designing visualizations for collaborative research, cross-platform analysis, and public participation." 40-67.

Pink, S. (2021). Doing visual ethnography. New York and London: Sage.

Pitkin, H. F. (1967). The concept of representation (Vol. 75). Univ of California Press.

Ricci, D. (2010). Seeing what they are saying: Diagrams for socio-technical controversies.

Rorty, R. (1979). Philosophy and the Mirror of Nature. Princeton: Princeton university press.

Rose, G. (2016). Visual methodologies: An introduction to researching with visual materials. New York and London:Sage.

Scott, J. C. (2020). Seeing like a state: How certain schemes to improve the human condition have failed. New Haven:Yale University Press.

Wolin, S. S. (2016). Politics and Vision: Continuity and Innovation in Western Political Thought-Expanded Edition. Princeton: Princeton University Press.

Learning Outcomes

  • Demonstrate a conceptual understanding of the importance of data visualisation as an instrument of inquiry, advocacy, decision-making, and engagement in contemporary science, culture and public policy.
  • Evaluate the usefulness of different methodologies for using data visualisation in the creation of knowledge, facilitate participation, decision-making and social and cultural change.
  • Demonstrate an empirical understanding of how data visualisation has enabled new knowledge, engagement, awareness and change through the discussion of real-world examples.
  • Provide an account of the role of data visualisation in the development of innovative forms of inquiry, policymaking, intervention and engagement, and of the potential of data visualisation to transform the relationship between science, culture and democracy.

Important Registration Information:

CIM Students

  • You will need to make your optional module choices using the degree specific CIM module webform available in the CIM welcome page. All further instructions will be available to you on the webform.

  • The webform opens on Monday 12th September at 12:00 noon BST and closes on Monday 19th September at 12:00 noon BST

  • Gheerdhardhini (CIM PG Coordinator) will register you for your chosen modules, confirming your place in the module by 30th September, Friday.

  • If there are any queries, please get in touch with Gheerdhardhini via 

External Students

  • Computer Science – Please register your interest in the CIM module with the PG Administrator in your home department - 

  • Psychology - Your PG Administrator will be in touch before Term 1 about registering interest for CIM modules

  • All other external students - Please contact the CIM PG Coordinator (Gheerdhardhini) via email (, to request your optional module choice at the latest by Week 1 : Wednesday, 5th October, 17.00 BST.


  • Please be advised that you may be expected to have access to a laptop for some of these courses due to software requirements; the Centre is unable to provide a laptop for external students.

  • Please be advised that some modules may have restricted numbers and places are allocated according to availability and inter-departmental arrangements.

  • Please note that a request does NOT guarantee a place on the module and is subject to availability.

  • Gaining permission of a member of CIM teaching staff or a member of staff from your home department or filling in the eVision Module Registration (eMR) system with the desired module does NOT guarantee a place on that module.

  • Requests after the specified deadline will not be considered.

  • For external students - Only after confirmation of a place from CIM PG Coordinator can students’ or their home departments confirm their registration on eVision/MRM. Registrations by students who have not received confirmation of a place from CIM will be rejected via the system.

NOTE – The above-mentioned registration deadline also applies to the CIM optional modules running in Term 2. We will consider registrations again in the first week of Term 2, but only in relation to modules where there is availability.

We are normally unable to allow students (registered or auditing) to join/leave the module after the second week of it commencing.