Spatial autoregressive models for statistical inference from ecological data
Abstract 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...
Published in: | Ecological Monographs |
---|---|
Main Authors: | , , , , |
Other Authors: | |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Wiley
2018
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1002/ecm.1283 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fecm.1283 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecm.1283 |
id |
crwiley:10.1002/ecm.1283 |
---|---|
record_format |
openpolar |
spelling |
crwiley:10.1002/ecm.1283 2024-10-20T14:11:14+00:00 Spatial autoregressive models for statistical inference from ecological data Ver Hoef, Jay M. Peterson, Erin E. Hooten, Mevin B. Hanks, Ephraim M. Fortin, Marie‐Josèe National Oceanic and Atmospheric Administration 2018 http://dx.doi.org/10.1002/ecm.1283 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fecm.1283 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecm.1283 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Ecological Monographs volume 88, issue 1, page 36-59 ISSN 0012-9615 1557-7015 journal-article 2018 crwiley https://doi.org/10.1002/ecm.1283 2024-10-07T04:29:58Z Abstract 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 ... Article in Journal/Newspaper Phoca vitulina Alaska Wiley Online Library Ecological Monographs 88 1 36 59 |
institution |
Open Polar |
collection |
Wiley Online Library |
op_collection_id |
crwiley |
language |
English |
description |
Abstract 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 ... |
author2 |
National Oceanic and Atmospheric Administration |
format |
Article in Journal/Newspaper |
author |
Ver Hoef, Jay M. Peterson, Erin E. Hooten, Mevin B. Hanks, Ephraim M. Fortin, Marie‐Josèe |
spellingShingle |
Ver Hoef, Jay M. Peterson, Erin E. Hooten, Mevin B. Hanks, Ephraim M. Fortin, Marie‐Josèe Spatial autoregressive models for statistical inference from ecological data |
author_facet |
Ver Hoef, Jay M. Peterson, Erin E. Hooten, Mevin B. Hanks, Ephraim M. Fortin, Marie‐Josèe |
author_sort |
Ver Hoef, Jay M. |
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 |
publishDate |
2018 |
url |
http://dx.doi.org/10.1002/ecm.1283 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fecm.1283 https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecm.1283 |
genre |
Phoca vitulina Alaska |
genre_facet |
Phoca vitulina Alaska |
op_source |
Ecological Monographs volume 88, issue 1, page 36-59 ISSN 0012-9615 1557-7015 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
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_ |
1813451527450263552 |