Map-User Response to Computer-Generated Choropleth Maps: Comparative Experiments in Classification and Symbolization

Mak, Katherine; Coulson, Michael

doi:10.1559/152304091783805527

Cited by 17 publications

(6 citation statements)

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“…We therefore measured the variability of perceived location of clusters across subjects, analogous to earlier uses of variance measures (e.g. Mak & Coulson, 1991;Muller, 1980). In addition, because a map that gives rise to a memorially stable perception is preferable to one that cannot be remembered with great accuracy, the experiment also measured the change in perceived locations of clusters between an immediate and delayed test.…”

Section: Methodsmentioning

confidence: 99%

“…In particular, we were sensitive to criticisms that most previous psychological research on map comprehension has used artificial laboratory tasks not relevant to real-life use of maps (Gilmartin, 1981). Hence, following Mak and Coulson (1991), and in analogy to an important and common aspect of epidemiological data analysis, subjects in the two experiments reported here had to identify contiguous map regions that were perceived to form clusters of identical or similar data values. To maximize similarity to actual medical-cartographic tasks, the maps used in the present studies represented several recent epidemiological data sets.…”

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confidence: 99%

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Perception of clusters in statistical maps

Lewandowsky

Herrmann

Behrens

et al. 1993

Applied Cognitive Psychology

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Two experiments observed performance on a cluster identification task across a variety of common statistical maps. Stimulus maps displayed mortality rates for several diseases and subjects had to identify regions of the map that were perceived to form a cluster of particularly high (or low) mortality. Subjects marked the perceived centroid of each cluster, and analyses focused on the dispersion of centroid location across subjects. Under these circumstances, monochrome classed choropleth maps were found to minimize dispersion, compared to a two opposing colours scheme, a dot density map, a pie map, and a categorical (hue-based) colour scheme. Maps using a familiar geographical unit (i.e. a US. state) supported better recall of the information than maps using less familiar and smaller geographical units. The results were found to be interpretable within current cognitive theory.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Perception of clusters in statistical maps

Lewandowsky

Herrmann

Behrens

et al. 1993

Applied Cognitive Psychology

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“…Tobler presented the original idea of unclassed maps in 1973 and was first rebutted by Dobson (1973). Investigation, application, and comment have continued in papers by Muller and Honsaker (1978), Muller (1979), Dobson (1980), Groop and Smith (1982), MacEachren (1982), Gale and Halperin (1984), Lavin and Archer (1984), Mak and Coulson (1991), and Kennedy (1994). Peterson's (1979) research included evaluation of classed and unclassed maps using a whole-map comparison task.…”

Section: Other Choropleth Mapping Issuesmentioning

confidence: 99%

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

Brewer

Pickle²

2002

Annals of the Association of American Geographers

329

165

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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.

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“…According to Tobler's first law of geography (Tobler, 1970), attribute values that are close in space tend to have similar values and are more likely to fall into the same class interval (Mak and Coulson, 1991). Research has revealed that land-use spatial data follow the first law of geography and possess certain autocorrelation (Xie et al, 2006;Qiu et al, 2007;Gao et al, 2010).…”

Section: The Spatial Autocorrelation Of Geographical Datamentioning

confidence: 99%

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

Liu

Zhan

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 KEY WORDS: Uncertainty, Land suitability assessment, GIS, Planning ABSTRACT:The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS) based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the "Nature Breaks" method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

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Map-User Response to Computer-Generated Choropleth Maps: Comparative Experiments in Classification and Symbolization

Cited by 17 publications

References 0 publications

Perception of clusters in statistical maps

Perception of clusters in statistical maps

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

The Uncertainties on the Gis Based Land Suitability Assessment For Urban and Rural Planning

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