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Visualizing health data with micromaps.

Linda Williams Pickle1, Daniel B Carr

  • 1StatNet Consulting LLC and The Pennsylvania State University, 20203 Goshen Rd., No. 189, Gaithersburg, MD 20879, USA. Linda@statnetconsulting.com

Spatial and Spatio-Temporal Epidemiology
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Summary
This summary is machine-generated.

Micromaps visually link disease rates and local characteristics on small maps. This method helps explore geographic patterns and associations, aiding epidemiologic research and communication.

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Area of Science:

  • Epidemiology
  • Geographic Information Systems (GIS)
  • Data Visualization

Background:

  • Geographic patterns of disease have historically been visualized using maps, leading to significant epidemiological discoveries.
  • Advancements in computing enable interactive spatial data display, facilitating desktop analysis of disease patterns.
  • Identifying disease 'hot spots' and their potential causes is crucial for public health research.

Purpose of the Study:

  • To introduce and describe micromaps as a novel graphic tool for exploring spatial disease patterns.
  • To demonstrate how micromaps simultaneously examine disease patterns and local characteristics.
  • To illustrate the utility of micromaps in exploring and communicating associations between variables and geographic locations.

Main Methods:

  • Development and application of micromaps, a graphics system linking statistical data to organized sets of small maps.
  • Exploration of spatial patterns in an outcome variable and related covariates.
  • Illustration of three types of micromaps: linked, conditioned, and comparative.

Main Results:

  • Micromaps facilitate the visual examination of geographic disease patterns and associated local characteristics.
  • The graphics effectively link statistical information to geographic locations for pattern exploration.
  • Three distinct micromap types (linked, conditioned, comparative) are presented and illustrated.

Conclusions:

  • Micromaps offer a powerful approach for visually exploring and communicating complex spatial disease data.
  • This method enhances the understanding of associations between disease outcomes, covariates, and geography.
  • Micromaps represent an advancement in the visual analysis and dissemination of epidemiologic findings.