Welcome
Acknowledgement
Citation
Course information
Hello GIS
Intended course learning outcomes
How to use this book
Getting started
Self guided learning
Software installation
QGIS
R
Basics
Packages
ArcGIS
External usage
How to adopt this course
How to contribute
Supporting the module
License
Updates
Packages
I GIS tools
1
Geographic Information
1.1
Learning outcomes
1.2
Homework
1.3
Recommended listening 🎧
1.4
The Basics of Geographic Information
1.4.1
Data types in statistics
1.4.2
Important GIS data formats
1.5
General data flow
1.6
Data
1.6.1
File paths
1.6.2
Data download
1.6.3
Data pre-processing
1.6.4
QGIS
1.6.5
R
1.6.6
Export data
1.6.7
What will I use
1.7
Data sources and task
1.7.1
UK Data Service
1.7.2
ONS
1.7.3
nomis
1.7.4
OS
1.7.5
Edina Digimap
1.7.6
OSM
1.7.7
DEFRA
1.7.8
Data lists
1.8
Summary
1.9
Feedback
2
Introduction to R
2.1
Learning outcomes
2.2
Homework
2.3
Recommended listening 🎧
2.4
Introduction
2.4.1
Online forums are your friend!!
2.4.2
Health warning
2.4.3
R and RStudio
2.4.4
Getting started
2.4.5
Basics
2.4.6
Scripts and some basic commands
2.4.7
Functions
2.4.8
Basic plotting
2.4.9
Help
2.4.10
Data structures
2.4.11
Elements of a data frame
2.5
Reading data into R
2.5.1
Old skool cleaning
2.5.2
Here
2.5.3
New skool cleaning
2.5.4
Examining your new data
2.5.5
Data manipulation in R
2.5.6
Plotting
2.5.7
Pimp my graph!
2.5.8
Spatial Data in R
2.5.9
Simple mapping
2.6
Tidying data
2.7
Feedback
3
Spatial descriptive statistics
3.1
Learning outcomes
3.2
Homework
3.3
Recommended listening 🎧
3.4
Introduction
3.5
Part 1 projections
3.5.1
Changing projections
3.5.2
WorldClim data
3.5.3
Data loading
3.5.4
Raster location
3.6
Part 2 descriptive statistics
3.6.1
Data preparation
3.6.2
Histogram
3.6.3
Using more data
3.6.4
Histogram with ggplot
3.7
Feedback
4
Git, GitHub and RMarkdown
4.1
Learning outcomes
4.2
Homework
4.3
Recommended listening 🎧
4.4
Introduction
4.5
Git and GitHub
4.5.1
The three ways
4.5.2
Set up your GitHub
4.5.3
Using RStudio with Git
4.5.4
Using the Git GUI - way 1
4.5.5
Create a new version control in RStudio - way 2
4.5.6
If have an existing project - way 3
4.5.7
Commiting to Git
4.5.8
Push to Github
4.5.9
Pull from GitHub
4.5.10
Using Git outside RStudio
4.5.11
Troubleshooting
4.5.12
Fork a repository
4.5.13
Branches
4.5.14
Health warning
4.6
RMarkdown
4.6.1
HTML
4.6.2
Knit options
4.6.3
Shortcuts
4.7
Further reading
4.8
Feedback
5
Map making
5.1
Learning outcomes
5.2
Homework
5.3
Recommended listening 🎧
5.4
Introduction
5.4.1
OSM
5.4.2
Airbnb
5.5
Data
5.5.1
OSM
5.5.2
London boroughs
5.5.3
World cities
5.5.4
Uk outline
5.5.5
Airbnb
5.6
Plan
5.7
CRS and filter
5.8
Join and sum
5.8.1
Key advice
5.9
Mapping
5.9.1
tmap syntax
5.9.2
Complete map
5.10
Bad maps
5.11
Feedback
II GIS analysis
6
Detecting spatial patterns
6.1
Learning outcomes
6.2
Homework
6.3
Recommended listening 🎧
6.4
Introduction
6.5
Setting up your data
6.5.1
Data cleaning
6.5.2
Spatial subsetting
6.5.3
Spatial clipping
6.5.4
Key advice
6.5.5
Study area
6.6
Point pattern analysis
6.6.1
Kernel Density Estimation
6.6.2
Quadrat Analysis
6.6.3
Try experimenting…
6.6.4
Ripley’s K
6.6.5
Alternatives to Ripley’s K
6.7
Density-based spatial clustering of applications with noise: DBSCAN
6.8
Point pattern analysis summary
6.9
Feedback
7
Spatial autocorrelation
7.1
Learning outcomes
7.2
Homework
7.3
Recommended listening 🎧
7.4
Introduction
7.4.1
Analysing Spatial Autocorrelation with Moran’s I, LISA and friends
7.4.2
Data download
7.5
Data cleaning
7.6
Data manipulation
7.7
Weight matrix
7.7.1
Matrix style
7.8
Autocorrelation
7.8.1
Moran’s I
7.8.2
Geary’s C
7.8.3
Getis Ord
7.8.4
Summary
7.8.5
Local Moran’s I
7.8.6
Local Getis Ord
\(G_{i}^{*}\)
7.8.7
Summary
7.9
Other variables
7.10
Feedback
8
Explaining spatial patterns
8.1
Learning objectives
8.2
Homework
8.3
Recommended listening 🎧
8.4
Introduction
8.4.1
Setting up your Data
8.5
Analysing GCSE exam performance - testing a research hypothesis
8.5.1
Research Question and Hypothesis
8.5.2
Regression Basics
8.5.3
Running a Regression Model in R
8.5.4
tidymodels
8.5.5
Bootstrap resampling
8.5.6
Variables
8.5.7
Assumptions Underpinning Linear Regression
8.5.8
Assumption 1 - There is a linear relationship between the dependent and independent variables
8.5.9
Assumption 2 - The residuals in your model should be normally distributed
8.5.10
Assumption 3 - No Multicolinearity in the independent variables
8.5.11
Assumption 4 - Homoscedasticity
8.5.12
Assumption 5 - Independence of Errors
8.6
Spatial Regression Models
8.6.1
Dealing with Spatially Autocorrelated Residuals - Spatial Lag and Spatial Error models
8.6.2
Key advice
8.6.3
Which model to use
8.6.4
More data
8.6.5
Extending your regression model - Dummy Variables
8.6.6
TASK: Investigating Further - Adding More Explanatory Variables into a multiple regression model
8.7
Task 3 - Spatial Non-stationarity and Geographically Weighted Regression Models (GWR)
8.8
Feedback
Extra
Multiple Linear Regression
Module resources
8.9
Markscheme
8.10
Old practicals
8.11
Events / meet other spatial data professionals
8.12
Cool stuff to explore
8.13
Books/reading resources
8.14
New developments
8.14.1
Twitter
8.15
Data
8.16
Data lists
Extra reproducibility
8.17
Git and GitHub
8.17.1
Back in time
8.18
renv
8.19
Referencing and more
QGIS Maps
8.20
Load data
8.21
Manipulate data
8.22
Map data
8.23
Export map
8.24
Graphical modeler
Tmap version 3
8.25
Learning outcomes
8.26
Homework
8.27
Recommended listening 🎧
8.28
Introduction
8.28.1
OSM
8.28.2
Airbnb
8.29
Data
8.29.1
OSM
8.29.2
London boroughs
8.29.3
World cities
8.29.4
Uk outline
8.29.5
Airbnb
8.30
Spatial joining
8.30.1
Static map
8.30.2
Inset map
8.30.3
Export
8.30.4
Basic interactive map
8.30.5
Advanced interactive map
8.31
Bad maps
8.32
Feedback
Functions
Interactive maps
8.33
Advanced interactive map
9
Past questions
10
Field trip
License: CC-BY-SA
CASA0005 Geographic Information Systems and Science
Chapter 9
Past questions