Cynthia A. Brewer

Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation

Dasymetric maps display statistical data in meaningful spatial zones. Such maps can be preferable to choropleth maps that show data by enumeration zones, because dasymetric zones more accurately represent underlying data distributions. Though dasymetric mapping has existed for well over a century, the methods for producing these maps have not been thoroughly examined. In contrast, research on areal interpolation has been more thorough and has examined methods of transferring data from one set of map zones to another, an issue that is applicable to dasymetric mapping. Inspired by this work, we tested five dasymetric mapping methods, including methods derived from work on areal interpolation. Dasymetric maps of six socio-economic variables were produced fm a study area of 159 counties in the eastern U.S. using county choropleth data and ancillary land-use data. Both polygonal (vector) and grid (raster) dasymetric methods were tested. We evaluated map accuracy using both statistical analyses and visual presentations of error. A repeated-measures analysis of variance showed that the traditional limiting variable method had significantly lower error than the other four methods. In addition, polygon methods had lower error than their grid-based counterparts, though the difference was not statistically significant. Error maps largely supported the conclusions from the statistical analysis, while also presenting patterns of error that were not obvious from the statistics.

Evaluation of methods for classifying epidemiological data on choropleth maps in series

Our research goal was to determine which choropleth classification methods are most suitable for epidemiological rate maps. We compared seven methods using responses by fifty-six subjects in a two-part experiment involving nine series of U.S. mortality maps. Subjects answered a wide range of general map-reading questions that involved individual maps and comparisons among maps in a series. The questions addressed varied scales of map-reading, from individual enumeration units, to regions, to whole-map distributions. Quantiles and minimum boundary error classification methods were best suited for these general choropleth map-reading tasks. Natural breaks (Jenks) and a hybrid version of equal-intervals classing formed a second grouping in the results, both producing responses less than 70 percent as accurate as for quantiles. Using matched legends across a series of maps (when possible) increased map-comparison accuracy by approximately 28 percent. The advantages of careful optimization procedures in choropleth classification seem to offer no benefit over the simpler quantile method for the general map-reading tasks tested in the reported experiment.

Visualizing georeferenced data: representing reliability of health statistics

The power of human vision to synthesize information and recognize pattern is fundamental to the success of visualization as a scientific method. This same power can mislead investigators who use visualization to explore georeferenced data—if data reliability is not addressed directly in the visualization process. Here, we apply an integrated cognitive-semiotic approach to devise and test three methods for depicting reliability of georeferenced health data. The first method makes use of adjacent maps, one for data and one for reliability. This form of paired representation is compared to two methods in which data and reliability are spatially coincident (on a single map). A novel method for coincident visually separable depiction of data and data reliability on mortality maps (using a color fill to represent data and a texture overlay to represent reliability) is found to be effective in allowing map users to recognize unreliable data without interfering with their ability to notice clusters and characterize patterns in mortality rates. A coincident visually integral depiction (using color characteristics to represent both data and reliability) is found to inhibit perception of clusters that contain some enumeration units with unreliable data, and to make it difficult for users to consider data and reliability independently.

Mapping Mortality: Evaluating Color Schemes for Choropleth Maps

Use of color for representing health data on maps raises many unanswered questions. This research addresses questions about which colors allow accurate map reading and which colors map users prefer. Through the combination of a review of previous color research and an experiment designed to test specific combinations of colors on maps, criteria were established and evaluated for selecting colors for choropleth maps of mortality data. The color-selection criteria provide pairs of hues for diverging schemes that avoid naming and colorblind confusions. We also tested sequential and spectral schemes. Our results show that color is worth the extra effort and expense it adds to map making because it permits greater accuracy in map reading. In addition, people prefer color maps over monochrome maps. Interestingly, scheme preference is affected by levels of clustering within mapped distributions. In this research, people preferred spectral and purple/green hue combinations. Contrary to our expectations, spectral ...

An Evaluation of Color Selections to Accommodate Map Users with Color-Vision Impairments

An experiment shows that maps can be designed to accommodate the approximately 4 percent of the population with red-green color-vision impairments. The experiment used seven pairs of maps with seven different color schemes to determine the effects of color selection on the map-reading ability of people with impaired or normal color vision. One rendition in each pair had colors that were potentially confusing to people with red-green impairments; the other had colors selected specifically to accommodate this group. On the set with potentially confusing colors, people with red-green impairments were less accurate and took longer to respond than those with normal color vision. They were just as accurate as those with normal color vision on the set with accommodating colors but continued to have longer reaction times. Logit analysis of accuracy confirmed the interaction between vision group (normal, impaired) and rendition of the map (confusing, accommodating) and indicated that performance differed from one ...

Spectral Schemes: Controversial Color Use on Maps

Cartographers have long discouraged the use of spectral, or rainbow, color schemes on thematic maps of quantitative geographic data, though such color use is common in GIS and scientific visualization. Recent research, however, has shown that spectral schemes are preferred and are interpreted accurately when used as multi-hue renditions of diverging schemes. Both spectral and diverging schemes can emphasize a critical point within a data range with light colors and emphasize both high and low extremes of the data with dark colors. Although spectral schemes include multiple saturated hues, they can be designed to accommodate map reading by people with red-green impaired color vision by skipping over the yellow-greens in the spectral sequence. Cartographers should encourage use of spectral color schemes for depicting diverging quantitative data, rather than insisting that these schemes should not be used.

Qualitative Methods for Research on Mapmaking and Map Use

Contemporary cartographic research on mapmaking and map use has a broad mandate and, as a consequence, researchers need a broad suite of methods. Consistent with research developments in other geographic subdisciplines, cartographic researchers now use qualitative methods. They offer the advantage of bringing research closer to the problem-solving realms of mapmakers and map users. Our purpose here is to discuss an array of qualitative methods for mapmaking and map use. Questionnaires, interviews, and protocol methods are used to gather verbal data about mapmaking and map use. Ethnographies produce data from direct observation of mapmakers and users. Maps also are sources for document analysis. We use examples of published cartographic research to elaborate on each of these methods.

Guidelines for use of the perceptual dimensions of color for mapping and visualization

The color scheme typology that I propose matches the organization of the perceptual dimensions of color to the logical orderings in data sets that are displayed graphically. The appropriate use of color in complex data displays, such as thematic maps, allows patterns to be easily observed. In this paper, ten color scheme types are matched with systematic paths through HVC perceptual color space to provide guidance on color selection for a complete range of data visualization challenges. I also describe errors in the perceptual structuring of HLS, HSV, and HSB color models commonly available for computer graphics.

Mastering map scale: balancing workloads using display and geometry change in multi-scale mapping

This paper builds on a body of European research on multiple resolution data bases (MRDBs), defining a conceptual framework for managing tasks in a multi-scale mapping project. The framework establishes a workload incorporating task difficulty, time to complete a task, required level of expertise, required resources, etc. Project managers must balance the workload among tasks with lower and higher complexity to produce a high quality cartographic product on time and within budget. We argue for increased emphasis on the role of symbol design, which often carries a lower workload than multi-scale mapping based primarily on geometry change. Countering expectations that combining symbol change with geometry change will increase workloads, we argue that in many cases, integration of the two can reduce workloads overall. To demonstrate our points, we describe two case studies drawn from a recent multi-scale mapping and database building project for Ada County, Idaho. We extend the concept of workload balancing, demonstrating that insertion of Level of Detail (LoD) datasets at intermediate scales can further reduce the workload. Previous work proposing LoDs has not reported empirical assessment, and we encourage small and large mapping organizations to contribute to such an effort.

A Transition in Improving Maps: The ColorBrewer Example

Many map makers seek to share their map design efforts by distributing styles, fonts, templates, software, tips, and other sorts of instructions. For example, offers links to a variety of symbol and font design efforts by mapmakers who use ESRIs GIS products. In this article, I will reflect on the format for offering map design assistance that I have used in ColorBrewer (Figure 1). ColorBrewer is a web tool for selecting color schemes for thematic maps. It has elicited a trickle of enthusiastic e-mail from pleased users who tell me that their maps are improved, and they are relieved to save time on a design challenge for which they are not confident of their skills. ColorBrewer is described in detail in two papers (Harrower and Brewer, in press; Brewer et al. 2003). It is described briefly here to provide context for my reflections on a transition in cartography toward assisting people who want to represent their information spatially but who have little or no training in the conventions and principles of map design and data representation.