Descartes (formerly called IRIS) is a software system designed to support visual exploration of spatially referenced data, e.g. demographic, economical, or cultural information about geographical objects or locations such as countries, districts, or cities. Descartes offers two integrated services: automated presentation of data on maps, and facilities to interactively manipulate these maps. Automated mapping is enabled by incorporating generic knowledge on map design into the system. Descartes selects suitable presentation methods according to characteristics of the variables to be analysed and relationships among those variables if more than one were selected simultaneously. The cartographic knowledge of Descartes allows non-cartographers to receive proper presentations of their data, and the automation of map construction helps the users to save valuable time that can better be used for data analysis and problem-solving. Exploratory data analysis requires highly interactive, dynamic data displays. We s...

http://geoanalytics.net/and/papers/ijgis99.pdf

Interactive maps for visual data exploration

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 chorop...

Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series

Understanding uncertainty is critical for many analytical tasks. One common approach is to encode data values and uncertainty values independently, using two visual variables. These resulting bivariate maps can be difficult to interpret, and interference between visual channels can reduce the discriminability of marks. To address this issue, we contribute Value-Suppressing Uncertainty Palettes (VSUPs). VSUPs allocate larger ranges of a visual channel to data when uncertainty is low, and smaller ranges when uncertainty is high. This non-uniform budgeting of the visual channels makes more economical use of the limited visual encoding space when uncertainty is low, and encourages more cautious decision-making when uncertainty is high. We demonstrate several examples of VSUPs, and present a crowdsourced evaluation showing that, compared to traditional bivariate maps, VSUPs encourage people to more heavily weight uncertainty information in decision-making tasks.

Value-Suppressing Uncertainty Palettes

Ensemble and summary displays are two widely used methods to represent visual-spatial uncertainty; however, there is disagreement about which is the most effective technique to communicate uncertainty to the general public. Visualization scientists create ensemble displays by plotting multiple data points on the same Cartesian coordinate plane. Despite their use in scientific practice, it is more common in public presentations to use visualizations of summary displays, which scientists create by plotting statistical parameters of the ensemble members. While prior work has demonstrated that viewers make different decisions when viewing summary and ensemble displays, it is unclear what components of the displays lead to diverging judgments. This study aims to compare the salience of visual features – or visual elements that attract bottom-up attention – as one possible source of diverging judgments made with ensemble and summary displays in the context of hurricane track forecasts. We report that salient visual features of both ensemble and summary displays influence participant judgment. Specifically, we find that salient features of summary displays of geospatial uncertainty can be misunderstood as displaying size information. Further, salient features of ensemble displays evoke judgments that are indicative of accurate interpretations of the underlying probability distribution of the ensemble data. However, when participants use ensemble displays to make point-based judgments, they may overweight individual ensemble members in their decision-making process. We propose that ensemble displays are a promising alternative to summary displays in a geospatial context but that decisions about visualization methods should be informed by the viewer’s task.

/pdf/effects-of-ensemble-and-summary-displays-on-interpretations-2kzy4al8ao.pdf

Effects of ensemble and summary displays on interpretations of geospatial uncertainty data

A debate has ensued from the question of whether the attribute values of spatially aggregated data need to be classified prior to the display operator for representation as a choropleth map. Some recent studies have concluded that choropleth maps using Jenk's optimal classification scheme are more easily interpretated than either unclassed maps or more traditional classification methods. This paper presents a range of optimal classification strategies for displaying spatially aggregated data including techniques designed to enhance value identification.

A comparison of optimal classification strategies for choroplethic displays of spatially aggregated data

Choropleth Maps Without Class Intervals?: A Comment

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Choropleth Maps Without Class Intervals?: A Comment