of georeferenced health statistics (using static printed maps) has,
in the past, lead to insights concerning health/environment interaction.
Print maps have been a primary visual thinking tool used to identify
geographic clusters (i.e., “hot spots” and other regular features
of disease and to compare these features with patterns of potential
etiologic agents. For example, a map of oral cancer death rates
among white females in the U.S. prompted a study of occupation and
lifestyle in North Carolina that identified snuff dipping as a major
risk factor for this tumor (see: Winn, Blot et al., 1981). Dynamic
geovisualization methods extend traditional cartographic approaches
for representing georeferenced health statistics and related information
in at least two ways: by emphasizing the use of maps and other representation
forms to construct knowledge (not just to present it) and by dynamically
linking the visual map display with both the underlying geographic
data structures and the system users (resulting in maps that change
in response to changes in data and/or to actions on the part of
||A screenshot of an exploratory health
visualization tool developed at the GeoVISTA Center. This image
provides an example of linking traditional statistical graphics
(a scatterplot) and mapped heath data.
In a series
of studies supported by the U.S. Centers for Disease Control, National
Center for Health Statistics, members of our research team investigated
a series of questions associated with design and symbolization for
disease atlases, and worked on production of a NCHS mortality atlas.
This work was (and continues to be) followed by a series of studies
focused on dynamic geovisualization methods that offer the potential
to extend substantially the role of maps and related visualization
methods in analysis of health statistics, as well as analysis of
socioeconomic statistics more generally (supported by NCHS, the
National Cancer Institute, and the National Science Foundation).
One focus of this work has been to understand the cognitive aspects
of map use in the context of health data analysis. Another has been
to develop a suite of visual analysis tools that integrate principles
from cartography, GIS, and exploratory data analysis (EDA) and to
test the usability of these tools. The initial tangible product
of this later research is a set of extensions to ESRI’s ArcView
GIS to support dynamically linked views, focusing, brushing, and
|These images show an animation of heart disease mortalilty
rates over a fifteen-year period. Click the first image to start
the sequence of images or view each image independently.
-More about HealthVis
Methods for Time Series Geo-referenced Health Statistics Data
-For details of National Cancer
Institute supported research
about our NCHS-funded research
about the Mortality Atlas
-Read about other projects
worked on by GeoVISTA
studies are complemented by related work on the integration of visual
and analytical methods for finding clusters in point pattern data
(see the images below) as well as recent work in GeoComputation.
more about our health data visualization and analysis research,
check out the following:
C. A., and A. M. MacEachren. 1995. Evaluation of map color schemes
for the NCHS mortality atlas. Proceedings, International Symposium
on Computer Mapping in Epidemiology, Tampa, FL, February 12-15.
C. A., A. M. MacEachren, and L. W. Pickle. 1995. Evaluation of
map color schemes for the NCHS mortality atlas, in Cognitive Aspects
of Statistical Mapping, vol. Cognitive Methods Staff, Working
Papers Series, No. 18. Edited by L. W. Pickle and D. J. Herrmann,
pp. 191-200. Washington, D. C.: Centers for Disease Control, National
Center for Health Statistics. MacEachren, A. M., C. A.
and L. Pickle. 1995. Mapping health statistics: Representing data
reliability. Proceedings of the 17th International Cartographic
Conference, Barcelona, Spain, September 3-9.
C. A., A. M. MacEachren, L. W. Pickle, and D. Herrmann. 1997.
Mapping mortality: Evaluating color schemes for choropleth maps.
Annals of the Association of American Geographers 87:411-438.
D., A. M. MacEachren, F. Boscoe, D. Brown, M. Marrara, C. Polsky,
and J. Beedasy. 1997. Implementing exploratory spatial data analysis
methods for multivariate health statistics. Proceedings of GIS/LIS
'97, Cincinnati, OH, Oct. 28-30, 1997, pp. 205-212.
A. M., C. Polsky, D. Haug, D. Brown, F. Boscoe, J. Beedasy, L.
Pickle, and M. Marrara. 1997. Visualizing spatial relationships
among health, environmental, and demographic statistics: interface
design issues. Proceedings, 18th International Cartographic Conference,
Stockholm, June 23-27, pp. 880-887.
A. M., F. P. Boscoe, D. Haug, and L. W. Pickle. 1998. Geographic
Visualization: Designing Manipulable Maps for Exploring Temporally
Varying Georeferenced Statistics. Proceedings, Information Visualization
'98, Reliegh-Durham, NC, Oct. 19-20, 1998, pp. 87-94.
A. M., C. A. Brewer, and L. Pickle. 1998. Visualizing Georeferenced
data: Representing reliability of health statistics. Environment
and Planning: A 30:1547-1561.