by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
This is the online home of Geocomputation with R, a book on geographic data analysis, visualization and modeling.
by Paula Moraga
This book describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. After a detailed introduction of geospatial data, the book shows how to develop Bayesian hierarchical models for disease mapping and apply computational approaches such as the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) to analyze areal and geostatistical data.
by Manuel Gimond
A well structures book which serves as an introduction to GIS and spatial data analysis. The book is structures around the authors Introduction to GIS and Spatial Analysis course (ES214). The book provides a good introduction to working with geographical datasets and performing spatial analysis such as point pattern analysis, hypothesis testing, spatial autocorrelation and spatial interpolation,
This book introduces processing and analysis methods for working with spatial data in R. The book is composed of two parts. The first part gives an overview of the basic syntax and usage of the R language, required before we can start working with spatial data. The second part then covers spatial data workflows, including how to process rasters, vector layers, and both of them together, as well as two selected advanced topics: spatio-temporal data and spatial interpolation.
Predictive Soil Mapping (PSM) with R explains how to import, process and analyze soil data in R using the state-of-the-art soil and Machine Learning packages with ultimate objective to produce most objective spatial predictions of soil numeric and factor-type variables. Especial focus has been put on using R in combination with the Open Source GIS such as GDAL, SAGA GIS and similar, and on using Machine Learning packages ranger, xgboost, SuperLearner and similar. This book is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Contributions of new chapters are welcome.
The materials aim to cover the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses. The first part of the module will focus on core programming techniques, data wrangling and practices for reproducible research. The second part of the module will focus on non-spatial data analysis approaches, including statistical analysis and machine learning.
16.7 sits: Data Analysis and Machine Learning on Earth Observation Data Cubes with Satellite Image Time Series
by Gilberto Camara, Rolf Simoes, Felipe Souza, Alber Sanchez, Lorena Santos, et al
Using time series derived from big Earth Observation data sets is one of the leading research trends in Land Use Science and Remote Sensing. One of the more promising uses of satellite time series is its application to classify land use and land cover. Information on land is critical for sustainable development because our growing demand for natural resources is causing significant environmental impacts. The target audience for sits is the new generation of specialists who understand the principles of remote sensing and can write scripts in R. Ideally, users should have basic knowledge of data science methods using R.
This book presents sits, an open-source R package for land use and land cover classification using big Earth observation data.
by Edzer Pebesma, Roger Bivand
This book introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, geometry attributes, data cubes, reference systems, as well as higher-level concepts including how attributes relate to geometries and how this affects analysis.
This website provides materials to learn about spatial data analysis and modeling with R. R is a widely used programming language and software environment for data science. R has advanced capabilities for managing spatial data; and it provides unparalleled opportunities for analyzing such data.
Imagine a world in which data on companies, households and governments were widely available. Imagine, further, that researchers and decision-makers acting in the public interest had tools enabling them to test and model such data to explore different scenarios of the future. People would be able to make more informed decisions, based on the best available evidence. In this technocratic dreamland pressing problems such as climate change, inequality and poor human health could be solved.
These are the types of real-world issues that we hope the methods in this book will help to address. Spatial microsimulation can provide new insights into complex problems and, ultimately, lead to better decision-making. By shedding new light on existing information, the methods can help shift decision-making processes away from ideological bias and towards evidence-based policy.
by Francisco Rowe, Dani Arribas-Bel
This is the website for “Spatial Modeling for Data Scientists”. This is a course taught by Dr. Francisco Rowe and Dr. Dani Arribas-Bel in the Second Semester of 2020/21 at the University of Liverpool, United Kingdom. You will learn how to analyse and model different types of spatial data as well as gaining an understanding of the various challenges arising from manipulating such data.
by Malone, Brendan P., Minasny, Budiman, McBratney, Alex B
Describes in detail, with ample exercises, how digital soil mapping is done This work includes a number of work-flows that direct users how to create digital soil maps for their own projects This work includes tutorials for users to learn the fundamentals of R, but with a focus on how to use it for digital soil mapping
Created and maintained by Oscar Baruffa
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