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

1: Geography and Cognitive Science. 1.1. Introduction. 1.2. Cognitive Processing. 1.3. Conclusions. 2: A Connectionist Approach to Spatial Cognition. 2.1. Introduction. 2.2. Memory Hardware. 2.3. Map Reading. 2.4. Analyzers. 2.5. Types of Memory. 2.6. Episodic Analyzer. 2.7. Action System. 2.8. Interacting Systems. 2.9. Fuzzy Cognitive Maps. 2.10. Interactive Activation Competition Models. 2.11. Interaction Activation Competition Model as Cognitive Map. 2.12. Conclusions. 3: Cognitive Maps. 3.1. Introduction. 3.2. Encoding Processes. 3.3. Conclusions. 4: Storing Spatial Information in Memory. 4.1. Introduction. 4.2. Object Files. 4.3. Mental Models. 4.4. Conclusions. 5: Spatial Search Processes. 5.1. Introduction. 5.2. Cognitive Theories of Search. 5.3. Geographic Applications. 5.4. Conclusions. 6: Learning Geographic Information. 6.1. Introduction. 6.2. Learning Categories. 6.3. Climate Categories. 6.4. Learning Higher-Order Categories. 6.5. The Organization of Geographic Information. 6.6. Conclusions. 7: Spatial Prototypes. 7.1. Introduction. 7.2. Category Prototypes. 7.3. Using Prototypes. 7.4. When is What Where? 7.5. Conclusions. 8: Similarity. 8.1. Introduction. 8.2. Comparing Maps. 8.3. Theoretical Similarity of Maps. 8.4. Conclusions. 9: Neural Network Applications. 9.1. Introduction. 9.2. Types of Problems. 9.3. The Gravity Model. 9.4. Residential Integration. 9.5. Acquiring Spatial Information. 9.6. Pattern Recognition. 9.7. Conclusions. 10: Conclusions. 10.1. Introduction. 10.2. Summary of Ideas. References. Index.

Spatial Cognition: Geographic Environments

Thematic map effectiveness can be defined only in terms of specific user requirements. Consideration of common requirements results in four criteria by which map effectiveness is evaluated: effectiveness for direct acquisition of both specific and general information and for memory of both specific and general information. The influence of visual map complexity and two methods of symbolization (choropleth and shaded isopleth maps) on each aspect of complexity is evaluated. Though isopleth maps prove to be consistently less complex than choropleth maps, the only significant difference in effectiveness between the methods is for memory of general patterns, in which case isopleth maps are more effective. Map complexity is found to have two components that influence effectiveness in quite different ways. Complexity of a mapped distribution is inversely related to effectiveness of maps in communicating general patterns but has little relation to communication of specific details. The second aspect of ...

The Role of Complexity and Symbolization Method in Thematic Map Effectiveness

One critical visual task when using choropleth maps is to identify spatial clusters in the data. If spatial units have the same color and are in the same neighborhood, this region can be visually identified as a spatial cluster. However, the choice of classification method used to create the choropleth map determines the visual output. The critical map elements in the classification scheme are those that lie near the classification boundary as those elements could potentially belong to different classes with a slight adjustment of the classification boundary. Thus, these elements have the most potential to impact the visual features (i.e., spatial clusters) that occur in the choropleth map. We present a methodology to enable analysts and designers to identify spatial regions where the visual appearance may be the result of spurious data artifacts. The proposed methodology automatically detects the critical boundary cases that can impact the overall visual presentation of the choropleth map using a classification metric of cluster stability. The map elements that belong to a critical boundary case are then automatically assessed to quantify the visual impact of classification edge effects. Our results demonstrate the impact of boundary elements on the resulting visualization and suggest that special attention should be given to these elements during map design.

Quantifying the Visual Impact of Classification Boundaries in Choropleth Maps

It may be possible to make the visual similarity of a set of maps conform to the statistical intercorrelations among the variables used to produce the maps. Conceptualizing the intercorrelations as the signal and blackness differences as noise, three sets of maps were produced allowing blackness differences to vary for the first and second'set and holding blackness differences constant for the third set. Subjects viewing the third set of maps made judgments concerning the visual similarity of the maps which conformed more closely to the intercorrelations among the variables. The judgments of the visual similarity of the maps for subjects viewing the other two sets of maps did not conform as well to the intercorrelations of the variables and seemed to be influenced by the blackness differences among these maps.

Visual and statistical comparison of choropleth maps

This study was conducted to determine how people make judgments about the similarity of patterns on choropleth maps Two sets of maps were prepared from the same data using different classing techniques Two groups of subjects were asked to select the two maps with the most similar patterns from triads of maps in their respective sets Although it was anticipated that the Ss (subjects) would consider only the similarity of the distributions, they also used relative blackness as a major decisionmaking criterion The classing techniques (equal interval and Jenks-Caspall method) used to produce the sets of maps did not have a significant effect on the Ss ability to judge similarity, as the resulting maps differed only slightly Decisions on the similarity of the maps appeared to be influenced by both the similarity of the spatial distributions and the relative blackness of the maps

The Decisionmaking Process for Judging the Similarity of Choropleth Maps

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The Decisionmaking Process for Judging the Similarity of Choropleth Maps