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The forgotten semantics of regression modeling in geography

The forgotten semantics of regression modeling in geography
The forgotten semantics of regression modeling in geography

This article is concerned with the semantics associated with the statistical analysis of spatial data. It takes the simplest case of the prediction of variable y as a function of covariate(s) x, in which predicted y is always an approximation of y and only ever a function of x, thus, inheriting many of the spatial characteristics of x, and illustrates several core issues using “synthetic” remote sensing and “real” soils case studies. The outputs of regression models and, therefore, the meaning of predicted y, are shown to vary due to (1) choices about data: the specification of x (which covariates to include), the support of x (measurement scales and granularity), the measurement of x and the error of x, and (2) choices about the model including its functional form and the method of model identification. Some of these issues are more widely recognized than others. Thus, the study provides definition to the multiple ways in which regression prediction and inference are affected by data and model choices. The article invites researchers to pause and consider the semantic meaning of predicted y, which is often nothing more than a scaled version of covariate(s) x, and argues that it is naïve to ignore this.

0016-7363
1-22
Comber, Alexis John
96833ae8-582b-4faa-a0bd-168379cd1862
Harris, Paul
84e584b8-1be0-4569-aa1a-fff57b91b1ca
Lü, Yihe
e05b0caa-7bed-4a32-bfd3-c8b8fbb114d0
Wu, Lianhai
5f148a37-b58f-463d-90c2-82690af0cfc3
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Comber, Alexis John
96833ae8-582b-4faa-a0bd-168379cd1862
Harris, Paul
84e584b8-1be0-4569-aa1a-fff57b91b1ca
Lü, Yihe
e05b0caa-7bed-4a32-bfd3-c8b8fbb114d0
Wu, Lianhai
5f148a37-b58f-463d-90c2-82690af0cfc3
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b

Comber, Alexis John, Harris, Paul, Lü, Yihe, Wu, Lianhai and Atkinson, Peter M. (2019) The forgotten semantics of regression modeling in geography. Geographical Analysis, 1-22. (doi:10.1111/gean.12199).

Record type: Article

Abstract

This article is concerned with the semantics associated with the statistical analysis of spatial data. It takes the simplest case of the prediction of variable y as a function of covariate(s) x, in which predicted y is always an approximation of y and only ever a function of x, thus, inheriting many of the spatial characteristics of x, and illustrates several core issues using “synthetic” remote sensing and “real” soils case studies. The outputs of regression models and, therefore, the meaning of predicted y, are shown to vary due to (1) choices about data: the specification of x (which covariates to include), the support of x (measurement scales and granularity), the measurement of x and the error of x, and (2) choices about the model including its functional form and the method of model identification. Some of these issues are more widely recognized than others. Thus, the study provides definition to the multiple ways in which regression prediction and inference are affected by data and model choices. The article invites researchers to pause and consider the semantic meaning of predicted y, which is often nothing more than a scaled version of covariate(s) x, and argues that it is naïve to ignore this.

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Comber et al 2019 Geographical Analysis - Version of Record
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More information

Accepted/In Press date: 4 March 2019
e-pub ahead of print date: 29 May 2019

Identifiers

Local EPrints ID: 432138
URI: http://eprints.soton.ac.uk/id/eprint/432138
ISSN: 0016-7363
PURE UUID: 0e54e2d7-b8e4-43df-bcb5-6f48cf283875
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

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Date deposited: 03 Jul 2019 16:30
Last modified: 07 Oct 2020 01:37

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