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The importance of regional models in assessing canine cancer incidences in Switzerland

The importance of regional models in assessing canine cancer incidences in Switzerland
The importance of regional models in assessing canine cancer incidences in Switzerland

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

Journal Article
1932-6203
1-16
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Leyk, Stefan
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Brunsdon, Christopher
cb9c4a6c-99b5-495c-af1b-27c73980f91b
Graf, Ramona
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Pospischil, Andreas
9d5d0e71-7d0a-4f5a-b7c3-77edcdd74a24
Fabrikant, Sara Irina
10e73f2e-3343-4ef1-9a07-b0fe43acc96c
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Leyk, Stefan
00f91399-4d02-488d-833f-c3cd53538139
Brunsdon, Christopher
cb9c4a6c-99b5-495c-af1b-27c73980f91b
Graf, Ramona
8c76754f-e3a7-4d88-b952-cb8a4165d359
Pospischil, Andreas
9d5d0e71-7d0a-4f5a-b7c3-77edcdd74a24
Fabrikant, Sara Irina
10e73f2e-3343-4ef1-9a07-b0fe43acc96c

Boo, Gianluca, Leyk, Stefan, Brunsdon, Christopher, Graf, Ramona, Pospischil, Andreas and Fabrikant, Sara Irina (2018) The importance of regional models in assessing canine cancer incidences in Switzerland. PLoS ONE, 13 (4), 1-16. (doi:10.1371/journal.pone.0195970).

Record type: Article

Abstract

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

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journal.pone.0195970 - Version of Record
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More information

Accepted/In Press date: 3 April 2018
e-pub ahead of print date: 13 April 2018
Published date: 2018
Keywords: Journal Article

Identifiers

Local EPrints ID: 422472
URI: https://eprints.soton.ac.uk/id/eprint/422472
ISSN: 1932-6203
PURE UUID: d8132123-ee5c-4430-8499-008616fcef77
ORCID for Gianluca Boo: ORCID iD orcid.org/0000-0002-4078-8221

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Date deposited: 24 Jul 2018 16:30
Last modified: 14 Mar 2019 01:22

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