**statistics:spatial**7

Geocomputation with R: Robin Lovelace, Jakub Nowosad, Jannes Muenchow

march 2018 by hallucigenia

This book is about harnessing the power of modern computers to do things with geographic data. It teaches a range of spatial skills, including: reading, writing and manipulating geographic data; making static and interactive maps; applying geocomputation to solve real-world problems; and modeling geographic phenomena. By demonstrating how various spatial operations can be linked, in reproducible ‘code chunks’ that intersperse the prose, the book also teaches a transparent and thus scientific workflow. Learning how to use the wealth of geospatial tools available from the R command line can be exciting but creating new ones can be truly liberating, by removing constraints on your creativity imposed by software. By the end of the book you should be able to create new tools for geocomputation in the form of shareable R scripts and functions.

Books
statistics:spatial
GIS
tidyverse
R
spatial_ecology
teaching:statistics
teaching:GIS
march 2018 by hallucigenia

Regression: Models, Methods and Applications - Ludwig Fahrmeir, Thomas Kneib, Stefan Lang, Brian Marx - Google Books

regression
statistics:regression
books
teaching:statistics
statistics:gams
statistics:spatial
to_read

february 2018 by hallucigenia

The aim of this book is an applied and unified introduction into parametric, non- and semiparametric regression that closes the gap between theory and application. The most important models and methods in regression are presented on a solid formal basis, and their appropriate application is shown through many real data examples and case studies. Availability of (user-friendly) software has been a major criterion for the methods selected and presented. Thus, the book primarily targets...

february 2018 by hallucigenia

VAST: Spatio-temporal analysis of univariate or multivariate data, e.g., standardizing data for multiple species or stage

september 2017 by hallucigenia

VAST

Is an R package for implementing a spatial delta-generalized linear mixed model (delta-GLMM) for multiple categories (species, size, or age classes) when standardizing survey or fishery-dependent data.

Builds upon a previous R package SpatialDeltaGLMM (public available here), and has unit-testing to automatically confirm that VAST and SpatialDeltaGLMM give identical results (to the 3rd decimal place for parameter estimates) for several varied real-world case-study examples

Has built in diagnostic functions and model-comparison tools

Is intended to improve analysis speed, replicability, peer-review, and interpretation of index standardization methods

Background

This tool is designed to estimate spatial variation in density using spatially referenced data, with the goal of habitat associations (correlations among species and with habitat) and estimating total abundance for a target species in one or more years.

The model builds upon spatio-temporal delta-generalized linear mixed modelling techniques (Thorson Shelton Ward Skaug 2015 ICESJMS), which separately models the proportion of tows that catch at least one individual ("encounter probability") and catch rates for tows with at least one individual ("positive catch rates").

Submodels for encounter probability and positive catch rates by default incorporate variation in density among years (as a fixed effect), and can incorporate variation among sampling vessels (as a random effect, Thorson and Ward 2014) which may be correlated among categories (Thorson Fonner Haltuch Ono Winker In press).

Spatial and spatiotemporal variation are approximated as Gaussian Markov random fields (Thorson Skaug Kristensen Shelton Ward Harms Banante 2014 Ecology), which imply that correlations in spatial variation decay as a function of distance.

statistics:gams
statistics:time_series
statistics:fisheries
fisheries
fisheries:methods
statistics:bayesian
statistics:spatial
R_packages
Is an R package for implementing a spatial delta-generalized linear mixed model (delta-GLMM) for multiple categories (species, size, or age classes) when standardizing survey or fishery-dependent data.

Builds upon a previous R package SpatialDeltaGLMM (public available here), and has unit-testing to automatically confirm that VAST and SpatialDeltaGLMM give identical results (to the 3rd decimal place for parameter estimates) for several varied real-world case-study examples

Has built in diagnostic functions and model-comparison tools

Is intended to improve analysis speed, replicability, peer-review, and interpretation of index standardization methods

Background

This tool is designed to estimate spatial variation in density using spatially referenced data, with the goal of habitat associations (correlations among species and with habitat) and estimating total abundance for a target species in one or more years.

The model builds upon spatio-temporal delta-generalized linear mixed modelling techniques (Thorson Shelton Ward Skaug 2015 ICESJMS), which separately models the proportion of tows that catch at least one individual ("encounter probability") and catch rates for tows with at least one individual ("positive catch rates").

Submodels for encounter probability and positive catch rates by default incorporate variation in density among years (as a fixed effect), and can incorporate variation among sampling vessels (as a random effect, Thorson and Ward 2014) which may be correlated among categories (Thorson Fonner Haltuch Ono Winker In press).

Spatial and spatiotemporal variation are approximated as Gaussian Markov random fields (Thorson Skaug Kristensen Shelton Ward Harms Banante 2014 Ecology), which imply that correlations in spatial variation decay as a function of distance.

september 2017 by hallucigenia

inlabru

july 2017 by hallucigenia

What is this?

The inlabru R package is being developed as part of a research project entitled “Modelling spatial distribution and change from wildlife survey data”, which is funded by the UK Engineering and Physical Sciences Research Council, to develop and implement innovative methods to model spatial distribution and change from ecological survey data. It involves developing Integrated Nested Laplace Approximation (INLA) methods for fitting realistically complex spatial models to data obtained from surveys on which the probability of detecting population members is unknown.

The project is a collaborative effort between the Universities of St Andrews (David Borchers, Janine Illian, Steve Buckland and Joyce Yuan) and Edinburgh (Finn Lindgren and Fabian E. Bachl).

R_packages
R
statistics:point_processes
statistics:additive_models
statistics:spatial
The inlabru R package is being developed as part of a research project entitled “Modelling spatial distribution and change from wildlife survey data”, which is funded by the UK Engineering and Physical Sciences Research Council, to develop and implement innovative methods to model spatial distribution and change from ecological survey data. It involves developing Integrated Nested Laplace Approximation (INLA) methods for fitting realistically complex spatial models to data obtained from surveys on which the probability of detecting population members is unknown.

The project is a collaborative effort between the Universities of St Andrews (David Borchers, Janine Illian, Steve Buckland and Joyce Yuan) and Edinburgh (Finn Lindgren and Fabian E. Bachl).

july 2017 by hallucigenia

CRAN - Package spBayes

june 2015 by hallucigenia

Fits univariate and multivariate spatio-temporal models with Markov chain Monte Carlo (MCMC).

statistics:spatial
statistics:time_series
statistics:multivariate
R_packages
R
to_try
june 2015 by hallucigenia

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