This blog is based on Will’s recent article ‘The conventions and politics of migration data visualizations’ published in New Media & Society.
Data visualisation. Journalists do it. Policymakers do it. Charities, foundations, and UN organisations do it. Even academics (myself included!) do it. To paraphrase Andy Kirk, a leading data visualiser, visualisation is very appealing—even necessary—because it can convey meaning by helping audiences identify and understand the significance of patterns in datasets which otherwise would likely go unnoticed.
When it comes to migration and related issues, there are many sources of quantitative data available: whether talking about national censuses, administrative records, or bespoke as well as periodic surveys, researchers can access more and more data relatively efficiently. But most people will never directly access these sources by trawling through spreadsheets or online archives. Rather, the wider public tends to encounter data through visualizations like charts and graphs. These images, whether embedded in online news stories or attached to social media posts, have proliferated as a way of attracting attention while also telling stories with data.
But do they follow any rules? And, if they do, why does this matter—particularly when it comes to migration?
Fortunately, there are some shared practices (called ‘conventions’) when it comes to how data gets visualised. Previously, some colleagues and I identified some of these conventions in several popular visualisations. We argued they potentially shape how viewers perceive and make sense of the topics.
But this raised a new question: to what extent were these conventions present in other more political domains? If conventions really do have such power to shape what people think, then an important pre-requisite step for demonstrating that influence would involve identifying the prevalence of certain conventions.
To do this, a research assistant and I collected 277 charts about migration from Google Images, and then counted how often certain characteristics appeared in each chart. (We compared our results and made sure they were not different to a statistically significant degree). These characteristics came from what previous social scientific work suggests are key visualisation conventions.
There were several key findings:
These patterns, some of which confirm the smaller-scale and qualitative analysis my colleagues and I did earlier, lead to two main conclusions. First, they suggest that—at least when it comes to migration—visualisations try to appeal to objectivity and transparency. The white backgrounds give a sense of cleanliness, while the presence of a data citation signals credibility and good-faith honesty, even if most viewers won’t have the time or interest to check.
Second, migration visualisations tend to prioritise states’ perspectives—and mostly higher-income migrant destination ones at that. To an extent, this isn’t surprising, as much of the available data on migration tends to come from countries which have the resources, infrastructure, and interest in counting migrants. What is more, comparative research in developing countries has shown how most search results tend to point to US or Europe-based content rather than local content.
These patterns matter for migration politics because they potentially shape how people think about migrants and migration—not only in terms of how important the issue is, but also in terms of which aspects are more important and salient. This is especially important in the context of data visualization, where the messaging mode itself can instil a sense of trustworthiness and scientific ‘correctness’. As my colleagues Helen Kennedy and Rosemary Hill have argued, the ‘feeling of numbers’ conveyed through data visualisations that follow certain conventions can be just as important for attitude formation as the actual statistical content.
Moreover, these patterns highlight issues around categorising migrants and quantification as a tool of governance. The underlying datasets in these visualisations reinforce established categories of who counts as a migrant, which then manifest themselves in the images. Yet the realities of migration do not always neatly correspond with these typologies, meaning that visualisations—like all images—necessarily simplify and frame the issue in particular ways. Moreover, these framings illustrate how numbers themselves feature as means of standardising and de-politicising complex issues, particularly in migration where there are no commonly agreed-upon objectives. In this way, visualisation potentially serves as a powerful way to recast migration in more neutral terms to achieve consensus or persuade key groups.
Taking stock of common features within migration data visualisations helps identify dominant approaches to visually communicating this globally important issue. It also opens opportunities to consider alternative ways of doing data visualisation that foreground migrants’ experiences and contexts.
For further reading:
Allen, William, Bridget Anderson, Nicholas Van Hear, Madeleine Sumption, Lena Rose, Jennifer Hough, Rachel Humphris, and Sarah Walker. 2018. ‘Who Counts in Crises? The New Geopolitics of International Migration and Refugee Governance’. Geopolitics 23 (1): 217–43. https://doi.org/10.1080/14650045.2017.1327740.
Baele, Stephane J., Thierry Balzacq, and Philippe Bourbeau. 2018. ‘Numbers in Global Security Governance’. European Journal of International Security 3 (1): 22–44. https://doi.org/10.1017/eis.2017.9.
Bandola-Gill, Justyna, Sotiria Grek, and Matteo Ronzani. 2021. ‘Beyond Winners and Losers: Ranking Visualizations as Alignment Devices in Global Public Policy’. Research in the Sociology of Organizations 74 (2): 27–52. [working paper version here]
Kennedy, Helen, and Rosemary Lucy Hill. 2018. ‘The Feeling of Numbers: Emotions in Everyday Engagements with Data and Their Visualisation’. Sociology 52 (4): 830–48. https://doi.org/10.1177/0038038516674675.
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