Spatial Data Uncertainty in Public Health Research

Spatial data is comprised of two components, namely, attribute and location information. Attribute information describes the non-locational characteristics of features, whereas locational information indicates relative and/or absolute positioning of these features. Like aspatial data, attribute data have parameters that can be estimated with sample data. Thus, one major source of uncertainty is sampling error (i.e., deviations of sample statistics from their corresponding population parameter values). Most often, the scoring of attributes also contains measurement error (i.e., differences between pairs of true and measured values). This additional major source of uncertainty involves the proximity of an instrument reading to its corresponding true value, and includes rounding of numbers and sometimes recording mistakes. Because all models are simplified descriptions of reality, these descriptions contain specification error (i.e., differences between reality and a model’s description of it); one goal of science is to minimize this error so that it is not too serious. Only approximate, rather than absolute, positions of features can be tagged to a coordinate system, resulting in location data also having uncertainty (i.e., deviations between true and approximate positions), introducing a third major source of error to georeferenced data. All four of these sources of uncertainty interact, impacting upon the quality of spatial data and spatial analyses, frequently embracing stochastic noise that further corrupts signals from and map patterns in georeferenced data. 

This Special Issue seeks papers that contribute to filling this gap in research practice by exploring various important geographic uncertainty situations. Studies that propose an innovative methodological approach and present novel applications addressing a wide range of uncertainty or accuracy issues in spatial environmental or health data are welcome. Preferred themes include, but are not limited to, measurement error patterns in space and/or space-time data, individual health exposure measurement uncertainty, sampling design to improve data accuracy, error propagation in spatial analysis, and incorporation of measurement errors in spatial data modeling. 

Prof. Dr. Daniel A. Griffith
Dr. Yongwan Chun
Guest Editors

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