Spatial prediction in the presence of positional error

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Spatial prediction in the presence of positional error. / Fanshawe, Thomas; Diggle, Peter.
In: Environmetrics, Vol. 22, No. 2, 03.2011, p. 109-122.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fanshawe, T & Diggle, P 2011, 'Spatial prediction in the presence of positional error', Environmetrics, vol. 22, no. 2, pp. 109-122. https://doi.org/10.1002/env.1062

APA

Fanshawe, T., & Diggle, P. (2011). Spatial prediction in the presence of positional error. Environmetrics, 22(2), 109-122. https://doi.org/10.1002/env.1062

Vancouver

Fanshawe T, Diggle P. Spatial prediction in the presence of positional error. Environmetrics. 2011 Mar;22(2):109-122. doi: 10.1002/env.1062

Author

Fanshawe, Thomas ; Diggle, Peter. / Spatial prediction in the presence of positional error. In: Environmetrics. 2011 ; Vol. 22, No. 2. pp. 109-122.

Bibtex

@article{60ebb7305ffb4cde8c65e97623de2114,

title = "Spatial prediction in the presence of positional error",

abstract = "Standard analyses of spatial data assume that measurement and prediction locations are measured precisely. In this paper we consider how appropriate methods of estimation and prediction change when this assumption is relaxed and the locations are subject to positional error. We describe basic models for positional error and assess their impact on spatial prediction. Using both simulated data and lead concentration pollution data from Galicia, Spain, we show how the predictive distributions of quantities of interest change after allowing for the positional error, and describe scenarios in which positional errors may affect the qualitative conclusions of an analysis. The subject of positional error is of particular relevance in assessing the exposure of an individual to an environmental pollutant when the position of the individual is tracked using imperfect measurement technology. ",

keywords = "environmental epidemiology , location error , measurement error , Monte Carlo inference",

author = "Thomas Fanshawe and Peter Diggle",

year = "2011",

month = mar,

doi = "10.1002/env.1062",

language = "English",

volume = "22",

pages = "109--122",

journal = "Environmetrics",

issn = "1099-095X",

publisher = "John Wiley and Sons Ltd",

number = "2",

}

RIS

TY - JOUR

T1 - Spatial prediction in the presence of positional error

AU - Fanshawe, Thomas

AU - Diggle, Peter

PY - 2011/3

Y1 - 2011/3

N2 - Standard analyses of spatial data assume that measurement and prediction locations are measured precisely. In this paper we consider how appropriate methods of estimation and prediction change when this assumption is relaxed and the locations are subject to positional error. We describe basic models for positional error and assess their impact on spatial prediction. Using both simulated data and lead concentration pollution data from Galicia, Spain, we show how the predictive distributions of quantities of interest change after allowing for the positional error, and describe scenarios in which positional errors may affect the qualitative conclusions of an analysis. The subject of positional error is of particular relevance in assessing the exposure of an individual to an environmental pollutant when the position of the individual is tracked using imperfect measurement technology.

AB - Standard analyses of spatial data assume that measurement and prediction locations are measured precisely. In this paper we consider how appropriate methods of estimation and prediction change when this assumption is relaxed and the locations are subject to positional error. We describe basic models for positional error and assess their impact on spatial prediction. Using both simulated data and lead concentration pollution data from Galicia, Spain, we show how the predictive distributions of quantities of interest change after allowing for the positional error, and describe scenarios in which positional errors may affect the qualitative conclusions of an analysis. The subject of positional error is of particular relevance in assessing the exposure of an individual to an environmental pollutant when the position of the individual is tracked using imperfect measurement technology.

KW - environmental epidemiology

KW - location error

KW - measurement error

KW - Monte Carlo inference

U2 - 10.1002/env.1062

DO - 10.1002/env.1062

M3 - Journal article

VL - 22

SP - 109

EP - 122

JO - Environmetrics

JF - Environmetrics

SN - 1099-095X

IS - 2

ER -

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