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5 results for “INLA”
Multivariate Spatial Predictions of Air Pollutants with INLA
<p>Spatial predictions and uncertainty quantification for air pollutants: PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>3</sub><sup>−</sup>, NH<sub>4</sub><sup>+</sup>, EC, OC, SO<sub>4</sub><sup>2− </sup>, CO, NOx, NO<sub>2</sub>, SO<sub>2</sub> and O<sub>3</sub> covering the continental US for the period 2005–2014. The daily prediction is at 12km spatial resolution.</p>
Data from: Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners
<p>Statistical stream temperature models can predict the fine-scale spatial distribution of water temperatures and guide species recovery and habitat restoration efforts. However, stream temperature modelling is complicated by spatial autocorrelation arising from non-independence data collected within dendritic networks. We used data from miniature sensors deployed in Canadian Rocky Mountain streams to develop and validate two statistical stream temperature modelling techniques that account for spatial autocorrelation. The first was based on spatial steam network models (SSNs) specifically developed to account for spatial autocorrelation in dendritic stream networks. The second used integrated nested Laplace approximation (INLA) that accounts for spatial autocorrelation but was not designed to address anisotropic stream network data. We evaluated the best-fitted SSN and INLA models using leave-one-out cross validation from the data collected along the stream network. Both modelling techniques had similar RMSE and MAE (near 1<sup>o</sup>C) and r<sup>2</sup> (> 0.6) values, and proved flexible with respect to implementation; however, the SSN models required more preprocessing steps before incorporating spatially correlated random errors. We provide practical advice and open-access data and r-script to help non-experts develop statistical stream temperature models of their own.</p>
Data from: Statistical stream temperature modelling with SSN and INLA: An introduction for conservation practitioners
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Data from: Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation
<p class="MsoNormal">The ever-growing popularity of citizen science, as well as recent technological and digital developments, have allowed the collection of data on species' distributions at an extraordinary rate. In order to take advantage of these data, information of varying quantity and quality needs to be integrated. Point process models have been proposed as an elegant way to achieve this for estimates of species distributions. These models can be fitted efficiently using Bayesian methods based on integrated nested Laplace approximations (INLA) with stochastic partial differential equations (SPDE). This approach uses an efficient way to model spatial autocorrelation using a Gaussian random field and a triangular mesh over the spatial domain. The mesh is constructed by user-defined variables, so effectively represents a free parameter in the model. However, there is a lack of understanding about how to set these mesh parameters, and their effect on model performance. Here, we assess how mesh parameters affect predictions and model fit to estimate the distribution of the serotine bat, <em><span>Eptesicus serotinus</span></em><span>,<em> </em>in Great Britain. A Bayesian INLA model was fitted using five meshes of varying densities to a dataset comprising both structured observations from a national monitoring programme and opportunistic records. We demonstrate that mesh density impacted spatial predictions with a general loss of accuracy with increasing mesh coarseness</span>. However, we also show that the finest mesh was unable to overcome spatial biases in the data. In addition, the magnitude of the covariate effects differed markedly between meshes. This confirms that mesh parameterisation is an important and delicate process with implications for model inference. We discuss how species distribution modellers might adapt their use of INLA in light of these findings.</p>
Data from: Integrated species distribution models fitted in INLA are sensitive to mesh parameterisation
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