A research agenda: Does geocoding positional error matter in health GIS studies?

Abstract

Until recently, little attention has been paid to geocoding positional accuracy and its impacts on accessibility measures; estimates of disease rates; findings of disease clustering; spatial prediction and modeling of health outcomes; and estimates of individual exposures based on geographic proximity to pollutant and pathogen sources. It is now clear that positional errors can result in flawed findings and poor public health decisions. Yet the current state-of-practice is to ignore geocoding positional uncertainty, primarily because of a lack of theory, methods and tools for quantifying, modeling, and adjusting for geocoding positional errors in health analysis. This paper proposes a research agenda to address this need. It summarizes the basics of the geocoding process, its assumptions, and empirical evidence describing the magnitude of geocoding positional error. An overview of the impacts of positional error in health analysis, including accessibility, disease clustering, exposure reconstruction, and spatial weights estimation is presented. The proposed research agenda addresses five key needs: 1) A lack of standardized, open-access geocoding resources for use in health research; 2)A lack of geocoding validation datasets that will allow the evaluation of alternative geocoding engines and procedures; 3) A lack of spatially explicit geocoding positional error models; 4)A lack of resources for assessing the sensitivity of spatial analysis results to geocoding positional error; 5)A lack of demonstration studies that illustrate the sensitivity of health policy decisions to geocoding positional error.

Understanding the Representativeness of Mobile Phone Location Data in Characterizing Human Mobility Indicators

“…The incompleteness of temporally or spatially sampled location data is also a considerable factor leading to uncertainty issues in GIScience [53,54], raising concerns regarding how uncertainties could affect the findings [55,56]. Some researchers think that long periods of time help increase sample size; Jacobs [57] notes that these data are large numbers of repeated observations over time and/or space and may not get rid of the sparse issue.…”

The effect of acquisition error and level of detail on the accuracy of spatial analyses

“…Here we focus on the positional error, as it is one of the most prominent types of errors in many context and application (Ariza-López & Rodríguez-Avi, 2015b;Drummond, 1995) and subject to intensive research (e.g. Biljecki, Heuvelink, Ledoux, & Stoter, 2015;Cheung & Shi, 2004;Chow, Dede-Bamfo, & Dahal, 2016;Jacquez, 2012;McKenzie, Hegarty, Barrett, & Goodchild, 2016;RuizLendínez, Ariza-López, & Ureña-Cámara, 2016). We term the combination of the acquisitioninduced and positional errors as combined error, and describe them in further detail in the next subsections.…”

Importance of the Geocoding Level for Historical Demographic Analyses: A Case Study of Rural Parishes in Sweden, 1850–1914

“…Quality aspects are vital for geocoding and can be described by the completeness (the share of the records that are geocoded), the concordance to the geographic unit (the share of geocoded records that are linked to an incorrect geographic unit), and the positional accuracy of the reference data. Although several studies have been reported on this issue in modern demography and epidemiology (e.g., Zandbergen 2007Zandbergen , 2009Griffith et al 2007;Mazumdar et al 2008;Vieira et al 2010), this issue is a rather neglected topic that requires additional attention (Jacquez 2012). In addition, with a few exceptions (e.g., Delmelle et al 2014) little attention has been focused on the temporal quality of the geocoding of longitudinal data.…”