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Updated: Jul 17, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Modeling the probability distribution of positional errors incurred by residential address geocoding.

Dale L Zimmerman¹, Xiangming Fang, Soumya Mazumdar

¹Department of Statistics and Actuarial Science and Center for Health Policy and Research, University of Iowa, Iowa City, IA 52242, USA. dale-zimmerman@uiowa.edu

International Journal of Health Geographics

|January 12, 2007

Summary

Automated geocoding and E911 geocoding introduce positional errors in public health studies. Mixtures of bivariate t distributions effectively model these geocoding errors, improving spatial epidemiology research.

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Last Updated: Jul 17, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Geographic Public Health
Spatial Epidemiology
Geographic Information Systems (GIS)

Background:

Automated geocoding assigns point-level coordinates to residences, crucial for geographic public health studies.
This process involves matching addresses to street segments and interpolating positions, which inherently introduces positional errors.
Understanding and modeling these errors is vital for accurate spatial analysis.

Purpose of the Study:

To model the probability distribution of positional errors from automated geocoding and E911 geocoding.
To compare the accuracy of different geocoding methods in rural settings.
To identify suitable statistical distributions for representing geocoding positional errors.

Main Methods:

Positional errors were calculated as the vector difference between geocoded locations and true locations (determined by orthophoto and parcel data).
Errors were assessed for 100%-matched automated geocodes, 60%-matched automated geocodes, and E911 geocodes in Carroll County, Iowa.
The empirical distributions of errors were analyzed and compared against standard statistical distributions.

Main Results:

E911 geocoding (median error 44 m) was more accurate than 100%-matched automated geocoding (median error 168 m).
Significant outliers (>15 km) were observed in 60%-matched geocoding errors.
The error distributions exhibited a unique Greek-cross shape, not well-fitted by single normal or t distributions.

Conclusions:

Mixtures of bivariate t distributions with two or three components provided excellent fits to the observed geocoding positional error distributions.
These flexible yet parsimonious models are suitable for spatial epidemiology applications involving measurement error.
The findings support the use of advanced statistical modeling for geocoding error in public health research.