Modeling Positional Uncertainty Acquired Through Street Geocoding

Hyeongmo Koo, Yongwan Chun and Daniel A. Griffith
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Hyeongmo Koo: The University of Texas at Dallas, Richardson, USA
Yongwan Chun: The University of Texas at Dallas, Richardson, USA
Daniel A. Griffith: The University of Texas at Dallas, Richardson, USA

International Journal of Applied Geospatial Research (IJAGR), 2018, vol. 9, issue 4, 1-22

Abstract: This article describes how modeling positional uncertainty helps to understand potential factors of uncertainty, and to identify impacts of uncertainty on spatial analysis results. However, modeling geocoding positional uncertainty still is limited in providing a comprehensive explanation about these impacts, and requires further investigation of potential factors to enhance understanding of uncertainty. Furthermore, spatial autocorrelation among geocoded points has been barely considered in this type of modeling, although the presence of spatial autocorrelation is recognized in the literature. The purpose of this article is to extend the discussion about modeling geocoding positional uncertainty by investigating potential factors with regression, whose model is appropriately specified to account for spatial autocorrelation. The analysis results for residential addresses in Volusia County, Florida reveal covariates that are significantly associated with uncertainty in geocoded points. In addition, these results confirm that spatial autocorrelation needs to be accounted for when modeling positional uncertainty.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jagr00:v:9:y:2018:i:4:p:1-22

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