Application of Objective Visual Complexity Measures to Binary Dasymetric Maps
Dasymetric maps have been shown to be an improvement over more common mapping techniques in terms of spatial and attribute accuracy. This research is a step towards evaluating dasymetric maps in terms of their effectiveness in communicating information to map readers. The effectiveness of a map depends, in part, on the visual complexity of the pattern it presents to the map reader. Previous research has evaluated the visual complexity of maps using objective measures, but such measures have not been applied to dasymetric maps. In this study a set of classed binary dasymetric population density maps was constructed for testing. As complexity is widely recognized as multifaceted, the maps were evaluated using a variety of objective measures. Several results from previous research with choropleth maps were confirmed. In particular, this study found a) there is considerable redundancy among complexity measures, b) that map complexity is affected profoundly by the data classification method, and c) that generally speaking complexity increases as the number of class increases. A finding unique to binary dasymetric maps is that complexity increases as the percentage of ancillary features increases. Another part of the study compared a set of choropleth test maps with dasymetric maps. Four out of the six complexity measures indicate that dasymetric maps are more complex than choropleth maps. In particular, dasymetric maps create a more fragmented pattern with a greater disparity in the size of enumeration units as well. It remains for future research to determine if the differences in complexity found here have significant implications for map use.
Binary Dasymetric Maps
Includes Figures, Tables, Maps, Appendices and Bibliography.