Representing Missing Spatial Data

Cartographers often struggle to represent missing data. Poor communication of missingness can reinforce analytical and social biases, since missing data is often associated with marginalisation, yet best practices remain under-researched. We identify two issues: selecting a suitable format to indicate missingness, and conveying the nature of missingness. Accordingly, a survey is used to test user perceptions of different visualisations. While participants do find some visualisations more effective than others, they do not generally associate specific visual formats with different forms of missingness. We also demonstrate substantial heterogeneity in perceived effectiveness across demographic groups.