Publications by Lex Comber and Chris Brunsdon
INIA International Workshop on Spatial Analysis in R - Session 5: Spatial Point Pattern Analysis
Overview In order to statistically interrogate spatial point data, a spatial point pattern analysis (e.g. Cressie 1993; Diggle 2003; Baddeley et al 2016) is required. This form of analysis may arise in many different contexts, for example: Crime incidences in Chicago, USA Gorilla nesting sites in a National park in Cameroon Kimboto trees in Par...
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INIA International Workshop on Spatial Analysis in R - Session 3: Exploratory Spatial Data Analysis (ESDA)
Overview This session covers the following topics: EDA of spatial properties with local summary statistics EDA of spatial properties with a local Indicator of Spatial Association (LISA) statistic Consideration of the Modifiable Areal Unit Problem (or MAUP) All of the work in this session is informed by 2 important but frequently overlooked cons...
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INIA International Workshop on Spatial Analysis in R - Session 4: Geostatistical Analysis
Overview The modelling objectives of a geostatistical analysis can generally be attributed to one of the following: Estimation of spatial dependence (or structure/continuity/correlation) via the semivariogram Prediction of unobserved variables and estimating associated measures of prediction uncertainty via kriging. Simulation of variables. Spat...
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INIA International Workshop on Spatial Analysis in R - Session 1: Reading, writing and creating Data and Spatial Data in R
Overview This session covers the following topics: Data (data tables, formats, creating, reading and writing, manipulating) Spatial Data (sp and sf formats, creating, reading and writing) The exercises use a dataset which will be downloaded from a GitHub repository. Other data provided by specific packages to demonstrate some of the functions a...
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INIA International Workshop on Spatial Analysis in R - Session 6: Regression with spatial autocorrelation effects
Overview This session covers the following topics on linear regression modelling with spatial data: Ordinary Least Squares (OLS) estimation EDA for evidence of residual spatial autocorrelation Restricted Maximum Likelihood (REML) for unbiased estimation with spatial autocorrelation effects The effects of predictor variable selection on regressio...
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INIA International Workshop on Spatial Analysis in R - Session 0: R Basics (you should know this!)
Overview We assume that workshop attendees have some familiarity with R and that they are able to do a number of things. This document provides a brief summary of these. Part 1 describes how to set up R / RStudio and ways of working in R. Part 2 provides a brief introduction to R and R data types. Part 3 has some useful additional information. ...
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Spatial Accuracy GWPCA workshop - Session 1: Data, Spatial Data, Regression and GWR
This session sets up the main sessions for the rest of the workshop. The aims of this session are to: Make sure you have R / RStudio installed and ready on your computer ad you have the basic tools for the workshop Load some data Develop a regression Create spatial data Develop a Geographically weighted Regression (GWR) 1. Introduction and gett...
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Spatial Accuracy GWPCA workshop - Session 3: Geographically Weighted Principal Component Analysis
The thinking behind this tutorial This practical is designed to have an explanatary text, together with code examples. Note that all code examples have a light yellow background, and are boxed. Output from R is also shown, and text output is also boxed. Graphical output is shown ‘in line’. The idea is to copy the text in the boxes into a runn...
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Spatial Accuracy GWPCA workshop - Session 2: Geographically Weighted Summary Statistics
Pre-amble To get started with this tutorial, first load the GWmodel package and some other helper packages: library(GWmodel) library(raster) library(rgdal) library(rgeos) library(RColorBrewer) library(grDevices) library(tmap) Review of Summary Statistics Summary statistics are basic statistics used to summarise a large data set. For examp...
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