statistics:spatial   7

Geocomputation with R: Robin Lovelace, Jakub Nowosad, Jannes Muenchow
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
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...
regression  statistics:regression  books  teaching:statistics  statistics:gams  statistics:spatial  to_read 
february 2018 by hallucigenia
VAST: Spatio-temporal analysis of univariate or multivariate data, e.g., standardizing data for multiple species or stage

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

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 
september 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 
july 2017 by hallucigenia
CRAN - Package spBayes
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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