rain3498 / geospatial-book

GeoSpatial Data Visualization, Analysis and Modeling in R

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GeoSpatial Data Visualization, Analysis and Modeling in R

参考材料

书籍

• Intro to GIS and Spatial Analysis https://mgimond.github.io/Spatial Manuel Gimond https://github.com/mgimond/Spatial
• 时空统计与 R 语言 Spatio-Temporal Statistics with R
• 空间点模式：理论与应用 Spatial Point Patterns: Methodology and Applications with R
• 空间微观模拟与R语言 Spatial Microsimulation with R
• 地质统计计算与 R 语言 Geocomputation with R
• 空间统计概念入门 Introduction to Geospatial Concepts
• 用于生态学家的数据分析与可视化 Data Analysis and Visualization in R for Ecologists
• Introduction to spatial analysis in R

博客、课程

• An Introduction to Spatial Econometrics in R
• Data wrangling visualisation and spatial analysis: R Workshop
• Regional smoothing using R
• Spatial Data in R: New Directions
• Mapping unemployment data
• Areal data. Lung cancer risk in Pennsylvania
• Geostatistical data. Malaria in The Gambia
• GeoSpatial Data Visualization in R
• Ecological Models and Data in R
• Geographic Data Science in Python
• Spatial
• Fast GeoSpatial Analysis in Python
• A gentle INLA tutorial
• Reproducible road safety research: an exploration of the shifting spatial and temporal distribution of car-pedestrian crashes
• 出租车地理信息可视化
• 地理数据分析和建模 Spatial Data Science with R raster 包 Geographic Data Analysis and Modeling
• Create maps from SITG files with sf and ggplot2
• ADMB 项目 https://github.com/admb-project 应用于空间模型 https://github.com/admb-project/admb-examples/tree/master/spatial-models
• glmmADMB 包 https://github.com/bbolker/glmmadmb
• Machine Learning algorithms for spatial and spatiotemporal data https://github.com/thengl/GeoMLA
• Vertica-Geospatial Examples https://github.com/vertica/Vertica-Geospatial

Geostatistical Analysis of Spatial Data

• LatticeKrig: Multiresolution Kriging Based on Markov Random Fields. Methods for the interpolation of large spatial datasets.
• RandomFields: Simulation and Analysis of Random Fields. Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.
• RandomFieldsUtils: Utilities for the Simulation and Analysis of Random Fields
• FieldSim: Random Fields (and Bridges) Simulations. Tools for random fields and bridges simulations.
• georob: Robust Geostatistical Analysis of Spatial Data
• constrainedKriging: Constrained, Covariance-Matching Constrained and Universal Point or Block Kriging

GLM’s for Spatial Data

• https://rpubs.com/corey_sparks/111362

Bayesian Regression using the INLA Approximation

• https://rpubs.com/corey_sparks/265648
• https://rpubs.com/corey_sparks/168849
• https://rpubs.com/corey_sparks/170843

Bayesian Multi-level Regression Models Using INLA

• https://rpubs.com/corey_sparks/132760

Bayesian Regression using INLA or Spatial Modeling with R-INLA

• https://rpubs.com/corey_sparks/127161

Spatial Regimes and Geographically Weighted Regression in R

• https://rpubs.com/corey_sparks/121993

Generalized Linear Modesl for Spatial Count data

• https://rpubs.com/corey_sparks/250410
• https://rpubs.com/corey_sparks/250388

Spatially Autoregressive Models 2

• https://rpubs.com/corey_sparks/250314

Spatially Autoregressive Models 1

• https://rpubs.com/corey_sparks/246342

• Displaying time series, spatial and space-time data with R

• CAST: 'caret' Applications for Spatial-Temporal Models https://github.com/environmentalinformatics-marburg/CAST https://cran.r-project.org/web/packages/CAST/vignettes/CAST-intro.html

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. This package includes functions to improve spatial-temporal modelling tasks using 'caret'. It prepares data for Leave-Location-Out and Leave-Time-Out cross-validation which are target-oriented validation strategies for spatial-temporal models. To decrease overfitting and improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to the target-oriented performance.

• spaMM: Mixed-Effect Models, Particularly Spatial Models https://kimura.univ-montp2.fr/~rousset/spaMM.htm

• 解有理矩阵方程与高斯过程回归模型可能有关 https://mirrors.tuna.tsinghua.edu.cn/CRAN/web/packages/SolveRationalMatrixEquation/index.html

• FastGP: Efficiently Using Gaussian Processes with Rcpp and RcppEigen Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).

• sgeostat: An Object-Oriented Framework for Geostatistical Modeling in S+ An Object-oriented Framework for Geostatistical Modeling in S+ containing functions for variogram estimation, variogram fitting and kriging as well as some plot functions. Written entirely in S, therefore works only for small data sets in acceptable computing time.

• sparseLTSEigen: RcppEigen back end for sparse least trimmed squares regression Use RcppEigen to fit least trimmed squares regression models with an L1 penalty in order to obtain sparse models.

• hBayesDM: hierarchical Bayesian modeling of Decision-Making tasks 任务决策的贝叶斯分层建模 https://github.com/CCS-Lab/hBayesDM https://rpubs.com/CCSL/hBayesDM

• LaplacesDemon: Complete Environment for Bayesian Inference Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.

• https://www.rspatial.org/

• https://edzer.github.io/sp/

• https://www.rspatial.org

• https://www.r-spatial.org

高精度世界地图 rworldxtra: Country boundaries at high resolution High resolution vector country boundaries derived from Natural Earth data, can be plotted in rworldmap. https://cran.r-project.org/web/packages/rworldxtra/index.html

https://gadm.org

GADM wants to map the administrative areas of all countries, at all levels of sub-division. We use a high spatial resolution, and of a extensive set of attributes. This is a never ending project, but we are happy to share what we have. You can write us with questions and suggestions, using this contact form.

运行环境

拉取 Docker 镜像

docker run --name book -d -p 8282:8787 -e ROOT=TRUE \
 -e USER=rstudio -e PASSWORD=cloud cloud2016/geospatial-book

克隆仓库

git clone https://github.com/XiangyunHuang/GeoSpatial-Book.git

安装依赖

devtools::install_deps('GeoSpatial-Book/')

编译网页书籍

bookdown::render_book("index.Rmd") # to build the book
browseURL("_book/index.html") # to view it