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Improving environmental exposure analysis using cumulative distribution functions and individual geocoding.

Paul A Zandbergen1, Jayajit Chakraborty

  • 1Department of Geography, University of South Florida, 4202 E, Fowler Ave, NES107, Tampa, FL 33620, USA. zandberg@cas.usf.edu

International Journal of Health Geographics
|May 27, 2006
PubMed
Summary

Cumulative distribution functions (CDFs) and individual geocoding offer more accurate environmental exposure assessments than traditional buffer distances and aggregated data. These methods reduce bias and improve the reliability of health risk evaluations for populations.

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Area of Science:

  • Environmental Health
  • Geographic Information Systems (GIS)
  • Spatial Analysis

Background:

  • Traditional GIS-based environmental exposure assessments often rely on simplified assumptions, such as discrete buffer distances and aggregated demographic data.
  • These methods can introduce significant errors and biases in determining exposed populations and health risks.

Purpose of the Study:

  • To demonstrate how cumulative distribution functions (CDFs) and individual geocoded locations can overcome limitations in traditional GIS exposure assessments.
  • To examine the errors and biases introduced by discrete buffer distances and aggregated data.

Main Methods:

  • Applied CDFs and individual geocoded locations to assess exposure potential for 159,923 school children in Orange County, Florida.
  • Determined proximity to gasoline stations, air pollution sources, and industrial facilities (Toxic Release Inventory).

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  • Compared results with traditional methods using discrete buffers and aggregated census data.

    • Main Results:

    • Discrete buffer distances introduced substantial bias and led to contradictory conclusions regarding exposure potential.
    • CDFs provided a more meaningful representation of exposure without arbitrary distance assumptions.
    • Individual geocoding offered more accurate population characterization and reliable subgroup comparisons, unlike aggregated census data which introduced bias.

    Conclusions:

    • CDFs provide more robust results for distance-based environmental exposure assessment compared to discrete buffer distances.
    • Individual geocoding significantly reduces bias introduced by aggregated data, leading to more accurate environmental exposure assessments.
    • GIS techniques are well-suited for implementing CDFs and geocoding large datasets, making these advanced methods computationally feasible for widespread use.