A key cartographic challenge associated with the rise of big data is to show when spatial data observations are missing or to communicate variables that indicate absence. For example, showing where people are tweeting during a disaster might be interesting, but visually identifying where normal signals are missing could in fact highlight the most affected places. Parcel data might be fully present, but attributes of their observations could convey qualities of absence (e.g., abandoned structures). Current geovisualization approaches normally do not show anything at all when data are missing or contain qualities of absence and only in rare cases might use a specific hue to highlight the presence of absence on maps. This work argues that people perceive missingness and absence in a way that is distinct from other spatial data qualities, and we propose a typology of static and dynamic means by which we can draw user attention to the presence of absence. To explore the application of these techniques, I use urban parcel data to visualize patterns of property blight in a Detroit neighborhood. Based on conceptual development and case study application, I propose research challenges to evaluate visual representations of missing and absent information on maps.
All Science Journal Classification (ASJC) codes
- Geography, Planning and Development
- Earth-Surface Processes