Showing papers on "Choropleth map published in 2002"
TL;DR: The authors compared seven methods using responses by fifty-six subjects in a two-part experiment involving nine series of U.S. mortality maps and found that matched legends across a series of maps (when possible) increased map-comparison accuracy by approximately 28 percent.
Abstract: 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...
429 citations
19 May 2002
TL;DR: In this paper, interactive extensions to two recently developed templates for displaying geospatially-indexed estimates are introduced, linked micromap plots and conditioned choropleth maps.
Abstract: This paper introduces interactive extensions to two recently developed templates for displaying geospatially-indexed estimates. The first template, linked micromap plots, links small generalized maps with statistical panels that describe regions. Research centered at the National Cancer Institute addressed the task of communicating state and county cancer statistics and tailored this template to show estimates, confidence intervals, and Healthy People 2010 target values. The research also integrated interactive options, such as variable selection, sorting, fixed header scrolling, mouse tips, enlarged dynamic map views and drill down, in a Java applet. This template has fared well in early usability tests. The second template, called conditioned choropleth maps, seeks to improve hypothesis generation about the spatial patterns shown in a classed choropleth map. Since variation of a study variable is often related to known risk factors, the template provides a way to control for the known variation. This paper describes dynamic sliders that change class boundaries for a study variable and that partition regions into a 3 x 3 layout of maps based on values of two risk factors. Highlighted regions in each map are more homogeneous with respect to both risk factors. Comparisons across maps and spatial patterns within maps provide the basis for generating hypotheses. The JAVA application shareware also includes dynamic statistical annotation and QQplots for comparing distributions
26 citations
19 May 2002
TL;DR: This article demonstrates the visual power of continuous smoothing and slicing of multivariate maps, through an example relating the rates of screening tests to colon cancer rates, and a new theoretical result provides legitimacy and understanding.
Abstract: This article demonstrates the visual power of continuous smoothing and slicing of multivariate maps, through an example relating the rates of screening tests to colon cancer rates. A new theoretical result provides legitimacy and understanding by demonstrating how the conditional maps are related to the usual single smoothed choropleth map of colon cancer rates.
2 citations
TL;DR: In this paper, the authors report on the bivariate mapping of data "quantity" and "quality" of Lyme disease forecasts for states of the United States, using historical state data for 1990-2000.
Abstract: Because maps typically represent the value of a single variable over 2-dimensional space, cartographers must simplify the display of multiscale complexity, temporal dynamics, and underlying uncertainty. A choropleth disease risk map based on data for polygonal regions might depict incidence (cases per 100,000 people) within each polygon for a year but ignore the uncertainty that results from finer-scale variation, generalization, misreporting, small numbers, and future unknowns. In response to such limitations, this paper reports on the bivariate mapping of data "quantity" and "quality" of Lyme disease forecasts for states of the United States. Historical state data for 1990-2000 are used in an autoregressive model to forecast 2001-2010 disease incidence and a probability index of confidence, each of which is then kriged to provide two spatial grids representing continuous values over the nation. A single bivariate map is produced from the combination of the incidence grid (using a blue-to-red hue spectru...