Spatial autoregressive models for statistical inference from ecological data
Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network-based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighb...
Published in: | Ecological Monographs |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | unknown |
Published: |
Wiley-Blackwell
2018
|
Subjects: | |
Online Access: | https://eprints.qut.edu.au/223523/ |
id |
ftqueensland:oai:eprints.qut.edu.au:223523 |
---|---|
record_format |
openpolar |
spelling |
ftqueensland:oai:eprints.qut.edu.au:223523 2024-05-12T08:10:01+00:00 Spatial autoregressive models for statistical inference from ecological data Ver Hoef, Jay Peterson, Erin Hooten, Mevin Hanks, Ephraim Fortin, Marie-Josee 2018 application/pdf https://eprints.qut.edu.au/223523/ unknown Wiley-Blackwell https://eprints.qut.edu.au/223523/1/115891.pdf doi:10.1002/ecm.1283 Ver Hoef, Jay, Peterson, Erin, Hooten, Mevin, Hanks, Ephraim, & Fortin, Marie-Josee (2018) Spatial autoregressive models for statistical inference from ecological data. Ecological Monographs, 88(1), pp. 36-59. https://eprints.qut.edu.au/223523/ Institute for Future Environments; Science & Engineering Faculty; ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS) free_to_read Consult author(s) regarding copyright matters This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au Ecological Monographs conditional autoregressive geostatistics intrinsic autoregressive prediction simultaneous autoregressive smoothing Contribution to Journal 2018 ftqueensland https://doi.org/10.1002/ecm.1283 2024-04-17T14:36:38Z Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network-based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row-standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals (Phoca vitulina) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row-standardization. We include several take-home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical ... Article in Journal/Newspaper Phoca vitulina Alaska Queensland University of Technology: QUT ePrints Ecological Monographs 88 1 36 59 |
institution |
Open Polar |
collection |
Queensland University of Technology: QUT ePrints |
op_collection_id |
ftqueensland |
language |
unknown |
topic |
conditional autoregressive geostatistics intrinsic autoregressive prediction simultaneous autoregressive smoothing |
spellingShingle |
conditional autoregressive geostatistics intrinsic autoregressive prediction simultaneous autoregressive smoothing Ver Hoef, Jay Peterson, Erin Hooten, Mevin Hanks, Ephraim Fortin, Marie-Josee Spatial autoregressive models for statistical inference from ecological data |
topic_facet |
conditional autoregressive geostatistics intrinsic autoregressive prediction simultaneous autoregressive smoothing |
description |
Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network-based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row-standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals (Phoca vitulina) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row-standardization. We include several take-home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical ... |
format |
Article in Journal/Newspaper |
author |
Ver Hoef, Jay Peterson, Erin Hooten, Mevin Hanks, Ephraim Fortin, Marie-Josee |
author_facet |
Ver Hoef, Jay Peterson, Erin Hooten, Mevin Hanks, Ephraim Fortin, Marie-Josee |
author_sort |
Ver Hoef, Jay |
title |
Spatial autoregressive models for statistical inference from ecological data |
title_short |
Spatial autoregressive models for statistical inference from ecological data |
title_full |
Spatial autoregressive models for statistical inference from ecological data |
title_fullStr |
Spatial autoregressive models for statistical inference from ecological data |
title_full_unstemmed |
Spatial autoregressive models for statistical inference from ecological data |
title_sort |
spatial autoregressive models for statistical inference from ecological data |
publisher |
Wiley-Blackwell |
publishDate |
2018 |
url |
https://eprints.qut.edu.au/223523/ |
genre |
Phoca vitulina Alaska |
genre_facet |
Phoca vitulina Alaska |
op_source |
Ecological Monographs |
op_relation |
https://eprints.qut.edu.au/223523/1/115891.pdf doi:10.1002/ecm.1283 Ver Hoef, Jay, Peterson, Erin, Hooten, Mevin, Hanks, Ephraim, & Fortin, Marie-Josee (2018) Spatial autoregressive models for statistical inference from ecological data. Ecological Monographs, 88(1), pp. 36-59. https://eprints.qut.edu.au/223523/ Institute for Future Environments; Science & Engineering Faculty; ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS) |
op_rights |
free_to_read Consult author(s) regarding copyright matters This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au |
op_doi |
https://doi.org/10.1002/ecm.1283 |
container_title |
Ecological Monographs |
container_volume |
88 |
container_issue |
1 |
container_start_page |
36 |
op_container_end_page |
59 |
_version_ |
1798853391358623744 |