Nontemporal Animated Maps

Animated maps do not have to depict temporal data. One kind of a nontemporal animated map is a fly over map that simulates the experience of ‘flying’ over a digital landscape or zooming into or away from a location (as is possible with Google Earth). Most often with fly overs the map itself is static (e.g., the colors of the map do not change); it is the vantage point of the map reader that is animated. It is generally assumed that animating both the vantage point of the user and the underlying map/data can quickly exceed the information processing capabilities of most map readers and hence few maps attempt to do both. While many fly over maps show an actual topographic surface (usually with strong vertical exaggeration), any geographic data surface can be used (e.g., precipitation, income).With fly over maps, the flight path may be either predetermined or the reader may have some to complete interactive control of the flight path. While allowing users to explore these virtual spaces on their own allows for more flexible map use, it places an increased cognitive burden on them since they must: (1) learn how to control their navigation (usually in 3 D), and (2) remember where they are at risk becoming disoriented. Most of the research to create more effective fly over maps and virtual spaces has been done in computer science and psychology.

A second kind of a nontemporal animation is a map that cycles through some sequence of data in which each frame of the animation shows a different ‘slice’ or treatment of the data. For example, an animated choropleth map might show voting patterns in a city by age, in which each frame maps a different age cohort (18–27, 28–37, and so on). By playing these frames in sequence as an animation, differences in spatial voting patterns between these age cohorts can be easily seen. Another example of application would be to run an urban simulation model that produces a single static map of anticipate growth in the next 25 years. By running the model many times and changing the parameters or assumptions in each run, an animation could be built up frame by frame from each model run and then played in rapid succession to see what areas remain consistently flagged for growth. Because the eye–brain system is especially attuned to seeing even very slight changes in color/position (such as a single pixel among millions flickering on and off) this is in fact a common technique for visually searching through massive amounts of data in scientific visualization.