“sliders”—buttons that allow for continuous changing of class definition—are positioned above, to the right and below the grid of maps and provide control of the partitioning of states to low, middle, and high classes for each of three interactively selected variables. The software also provides dynamic statistical feedback, guidance about slider settings, and alternative views. Carr and Pickle (2010) describe these capabilities and also address other design issues, such as simplifying map boundaries for visualization purposes.
International interest in National Patterns is a reason, Carr said, to make world maps to show spatial-temporal patterns for many nations. He noted, however, that world maps typically have visibility problems for small nations and that for some data the big difference between neighboring nations makes it more difficult to see and learn spatial patterns.
As an alternative to world maps, Carr showed examples from Gapminder.4 He mentioned that Gapminder’s animated bubble scatter-plots help visualize time series for two variables. Although animation poses some visual and cognitive problems, they can be partially addressed.
Carr said that one can juxtapose a few state maps to show all the state class memberships and changes over time. He showed a temporal change maps design that displays state expenditures of R&D funding relative to the gross domestic product for just four of the yearly maps: see Figure 6-2. In this design, an analyst uses a dynamic three-class slider below the maps to put states into low, middle, and high classes based on their values. The blue, gray, and red colors in the middle row of maps indicate the class memberships over time. However, even when studying two maps that are in sight, such as the 1993 and 1998 maps, it is hard to find all the class changes. When people’s eyes jump from map to map in movements called saccades, they are effectively blind, and their change detectors are reset. People can only remember a little area in focal attention long enough to make a comparison across maps.
In general, careful comparison of two juxtaposed similar images requires tedious back-and-forth comparisons of small corresponding areas. People see the new focal location, but the usual feedback about change in the large visual field is absent. Change blindness is the phenomena of not noticing many changes because one’s visual change detectors have been reset. Explicitly showing the class changes, as in Figure 6-2, addresses the change blindness problem. Specifically, the top row of maps shows all the states that changed to a higher category in their new color which is either gray or red. The bottom row shows all the states that changed to a lower category in their new color which is either gray or blue.
Carr and Pickle (2010) describe a variety of comparative micromaps. Their examples include maps that can be indexed by such variables as age