Welcome | Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. Read more →

Background, methods and exercises for using R for environmental data science. The focus is on applying the R language and various libraries for data abstraction, transformation, data analysis, spatial data/mapping, statistical modeling, and time series, applied to environmental research. Applies exploratory data analysis methods and tidyverse approaches in R, and includes contributed chapters presenting research applications, with associated data and code packages. Read more →

This contains materials to support the University of Leeds GEOG3195 module, delivered by Lex Comber […] This is an on-line book written to support the practicals for the GEOG3915 GeoComputation and Spatial Analysis module, delivered by Lex Comber of the School of Geography, from the University of Leeds. It draws from An Introduction to Spatial Analysis and Mapping by Brunsdon and Comber (2018) (link here) which provides a foundation for spatial data handling, GIS-related operations and spatial analysis in R. Each chapter is self contained and with instructions for loading and data and … Read more →

This is a minimal example of using the bookdown package to write a book. The HTML output format for this example is bookdown::bs4_book, set in the _output.yml file. [...] Welcome to website of the course “Introduction to Bayesian Inference and Statistical Learning.” offered by the Chair of Spatial Data Science and Statistical Learning at the University of Goettingen. This course is designed to equip you with both the theoretical foundations and practical tools necessary for applying Bayesian and statistical learning approaches to real-world data. By the end of the course, you will be ... Read more →

Alex Edwards and Emily Peterson Authors: Alex Edwards Fall 2024 This Advanced GIS (DATA 532) class is a project-based exploration of advanced topics in GIS and geospatial technology, with a focus on spatial modeling, advanced spatial analysis and geoprocessing, spatial data manipulation, and geocomputation. For information on course expectations, assignments, grading, and schedule, please review the course syllabus listed on Canvas. We will use this e-book for lectures, and in-class activities. All course content will be housed in this book for your reference. Advanced GIS analysis methods … Read more →

Emily Peterson emily.nancy.peterson@emory.edu Welcome to Spatial Epidemiology Workshop! Spatial epidemiology is a rapidly growing field that focuses on the geographic distribution of health outcomes, disease patterns, and the environmental or social factors that influence them. Understanding the spatial aspects of public health can provide insights into how and why diseases spread, identify communities at higher risk, and inform targeted interventions. The ability to visualize, analyze, and model spatial data has become a crucial skill for researchers, public health practitioners, and … Read more →

This contains materials to support the University of Leeds GEOG5917 module, delivered by Lex Comber […] This is an on-line book written to support the practicals for the GEOG5917 Big Data and Consumer Analytics module, delivered by Lex Comber of the School of Geography, from the University of Leeds. A real book was written based on the materials developed for this module: Geographical Data Science and Spatial Data Analysis: An Introduction in R (Comber and Brunsdon 2021 - link here) and the module also draws from An Introduction to Spatial Analysis and Mapping in R (Brunsdon and Comber 2018 … Read more →

Workflow for Refining ClimateNA Temperature Predictions […] Here are summaries of boosted regression trees that predict differences between ClimateNA temperature predictions and micro-climate conditions. Temperature offset layers and project spatial data can be viewed at https://bgcasey.users.earthengine.app/view/climateoffsets. Code and a description of the project’s workflow can be found at https://github.com/bgcasey/climate_downscaling. … Read more →

This contains materials to support the University of Leeds GEOG1400 module, delivered by Lex Comber […] This is an on-line book written to support the practicals for the GEOG1400 Digital Geographies module, delivered by Lex Comber of the School of Geography, from the University of Leeds. It is based on An Introduction to Spatial Analysis and Mapping (Brunsdon and Comber 2018 (link here) which provides a foundation for spatial data handling, GIS-related operations and spatial analysis in R.) The chapters in this book contain an individual Practical with a discrete set of activities, and … Read more →

A book example for a Chapman & Hall book. […] We live in a time of unprecedented environmental change, driven by the effects of fossil fuels on the Earth’s climate and the expanding footprint of human land use. To mitigate and adapt to these changes, there is a need to understand their myriad impacts on human and natural systems. Achieving this goal requires geospatial data on a variety of environmental factors, including climate, vegetation, biodiversity, soils, terrain, water, and human populations. Consistent monitoring is also necessary to identify where changes are occuring and … Read more →

This book will describe methods for linking organisms to their habitat […] … Read more →

This is the final report summary of spatial statistics analysis. January 31, 2022. […] Spatial data is considered as a random process ({Z(s),s\in D}) in this part. Set coalash dataset as an example (Figure 1.1). (D) is the region with values. and (s) indicates percent of coalash in this location. Many kinds of exploratory statistics can be applied here to test stationarity, local stationarity and so on. The key idea in this chapter is to model the above random process ({Z(s),s\in D}) with values on known locations. Then inference of unknown locations can be made. Variogram is … Read more →

This is a tutorial book with R Markdown for CASA0005 final coursework containing code and instruction of the whole analyzing process. Course name: CASA0005 Geographic Information Systems and Science Program: MSc Spatial Data Science and Visualisation Department: Centre for Advanced Spatial Analysis GitHub repository: https://github.com/LingruFeng/GIS_assessment Rpubs link: https://rpubs.com/Lingru/GIS_assessment … Read more →

Guide to Creating Interactive Maps in R […] Prior to working through the exercises and modules in this book, please watch the precursor videos that provide an introduction to GIS and geospatial data. The videos can be found here: First things first, you should install the R and RStudio softwares. To download R, visit the webpage https://cloud.r-project.org. You should see three download links at the top of the page, please click and install the appropriate R software for your machine (Linux, Mac OS, or Windows). Next, install RStudio Desktop from the webpage https://rstudio.com/download. … Read more →

Using the Uber Movement dataset, we combine it with the OpenStreetMap data for Berlin. […] Uber released for some cities the datasets of their drivers movement. Those include the OSM way identifier, the mean and standard speed deviation. In order to anonymize them, the data have been aggregated per hour. Let’s have a look at the Berlin data for the month of June 2019, and how they are distributed in space and time. For this, we will combine those data with the OpenStreetMap shapefile for Berlin. Through this book, we will use some concepts of data analysis … Read more →

Data science is concerned with finding answers to questions on the basis of available data, and communicating that effort. Besides showing the results, this communication involves sharing the data used, but also exposing the path that led to the answers in a comprehensive and reproducible way. It also acknowledges the fact that available data may not be sufficient to answer questions, and that any answers are conditional on the data collection or sampling protocols employed. This book introduces and explains the concepts underlying spatial data: points, lines, polygons, rasters, coverages, … Read more →