The conventions and politics of migration data visualizations

Abstract

What visual features characterize online migration data visualizations, and what do they suggest for the politics of representing migration and informing public attitudes? Audiences increasingly encounter quantitative information through visualization, especially in digital environments. Yet visualizations have political dimensions that manifest themselves through “conventions,” or shared symbols and practices conveying meaning. Using content analysis, I identify patterns of representation in a sample of 277 migration data visualizations scraped from Google Images. I find evidence of several conventions including appeals to objectivity and traceability as well as perspectives and units of analysis centered on states—particularly higher income migrant destinations. Then, by locating my analysis within the growing field of digital migration studies, I argue these conventions potentially shape public attitudes and understandings about migrants, and contribute to broader migration politics involving categorization and quantification that have relevance both on- and off-line.

The politics of data visualisation and policy making

“…A growing area of multi-disciplinary scholarship has argued that, depending on their intended uses, data visualisations can frame issues in persuasive ways (Hullman and Diakopoulos, 2011; Pandey et al, 2014), give over-confident impressions of causality (Xiong et al, 2020), and prioritise some values such as positivist ideals of scientific objectivity and neutrality over others (Kennedy et al, 2016). These qualities potentially have consequences for the ways that audiences think about and act upon important issues including abortion (Hill, 2017), human rights (Rall and Margaret, 2016) and immigration (Allen, 2021), as well as how they behave online during key moments such as election campaigns (Amit-Danhi and Shifman, 2020). They also invite questions about how visualisations may contribute to misinformation, mis-representation or even exclusion altogether as they introduce sometimes-opaque choices about how to manage increasingly complex datasets.…”