Featured image by liuzishan
Finding connections of power invisible to the eye, when unstructured, is something that can be achieved with the so-called ‘power mapping’. Data mapping cannot only visualise relations we would not see usually, but it can be an innovative approach to research methods (Feigenbaum 2020). The use of maps and visual imageries emerged during COVID-19 to provide an aid for a better understanding of the viruses spread (Kennedy 2020). However, maps can be put to use, not only to visualise the spread of COVID-19 but also to outline companies and individuals who profit from the pandemic and their connections with each other.
Power maps are a form of network graphs, which can be seen in television depictions of police investigations (Feigenbaum and Alamalhodaei 2020). The analysis of networks is often connected to the visualisation of shareholders in a map (Sova et al. 2015). For example, network analysis can be applied within social networks to analyse individual relationships (Sova et al. 2015) and is popular amongst NGOs (Feigenbaum and Alamalhodaei 2020).
Police investigation board. Photograph by Popov (2022).
I first came to experience this method of data structuring during a workshop at Bournemouth University held by professor Anna Feigenbaum, where we learned to search but also visualise connections among different companies and employees. We focussed on companies and people who benefit economically from COVID-19. For example, in the U.S. many people lost their jobs or business due to COVID-19, which highlights the failed economic system (Oxfam 2020). The problem presented here is, while poverty is growing and small businesses are being ruined, some of the biggest U.S. corporations, in technology or the pharmaceutical industry, are gaining more profit (Oxfam 2020). The example outlined, portrays the structures of power and who is at the receiving end of it and who isn’t.
After we discussed in our workshop the previously mentioned social and economical issues, we examined a few of the pandemic profiteering companies such as Facebook, Google and Cisco. In line with current issues, we focussed on relations with the pharmaceutical companies, technological companies or other companies benefitting from the pandemic.
Search for connections.
As part of the steps, we researched the companies and connected shareholders but also searched for connections between the sectors. Once connections were found and influential employees were discovered we inserted our data into a Google Sheet, where we separated between nodes and edges.
Inserting of data into edges and nodes.
The sheet was necessary to populate our map of links and connections in Graph Commons (website for creating power maps). Cleaning data sets can be challenging (Feigenbaum and Alamalhodaei 2020), which definitely applied for this exercise. Additionally, I wanted to see how Apple is connected to other companies who are benefiting from the pandemic since I use their products daily. Apple counts to the top 25 American companies regarding earnings during COVID-19 (Oxfam 2020). Therefore, I entered all found connections into my spreadsheet and updated it in Graph Commons.
Finished power map and outliers. Link to map
To understand the final connections better, I used different tones of colours for the edges and a different colour scheme for the nodes, compared to the edges.The map created shows links between technological and pharmaceutical companies, as well as financial ones. The narrative depicted here is rich white men getting more profit through influence in the technological, as well as, pharmaceutical sector.
References:
Feigenbaum, A. and Alamalhodaei, A., 2020. The Data Storytelling Workbook [online]. London; New York: Routledge.
Feigenbaum, A., 2020. Case Study: Anna Feigenbaum explains ‘how not to make an online map’. In: Feigenbaum, A. and Alamalhodaei, A., eds. The Data Storytelling Workbook [online]. London; New York: Routledge, 216-218.
Kennedy, H., 2020. Simple data visualisations have become key to communicating about the COVID-19 pandemic, but we know little about their impact. LSE [online]. 4 May 2020. Available from: https://blogs.lse.ac.uk/impactofsocialsciences/2020/05/04/simple-data-visualisations-have-become-key-to-communicating-about-the-covid-19-pandemic-but-we-know-little-about-their-impact/ [Accessed 5 January 2022].
Liuzishan, 2022. 3d earth graphic symbolizing global trade illustration. Free Vector [image]. Malaga: Freepik. Available from: https://www.freepik.com/free-vector/3d-earth-graphic-symbolizing-global-trade-illustration_14803715.htm#query=world%20map&from _query=polygonal%20world%20map&position=16&from_view=search [Accessed 10 January 2022].
Oxfam, 2020. Pandemic Profits Exposed: A COVID-19 Pandemic Profits Tax as one essential tool to reverse inequalities and rebuild better post-pandemic [online]. Boston: Oxfam America.
Popov, A., 2022. Police Investigation Board [photograph]. Adobe Stock. Available from: https://stock.adobe.com/uk/images/police-investigation-board/428469231 [Accessed 13 January 2022].
Sova, C.A., Helfgott, A., Chaudhury, A.S., Matthews, D., Thornton, T.F. and Vermeulen, S.J., 2015. Multi-level Stakeholder Influence Mapping: Visualizing Power Relations Across Actor Levels in Nepal’s Agricultural Climate Change Adaptation Regime. Systematic Practice and Action Research [online], 28 (4), 383-409. DOI: 10.1007/s11213-014-9335-y.