GEOG3915 Practicals
1
github-repo: rstudio/bookdown-demo
1.1
Structure
1.2
R Refresh (if you have not used R before)
1.2.1
Part 1. Background
1.2.2
Part 2. Your first R code
1.2.3
Some Tasks:
1.2.4
Packages
1.2.5
Answers to Tasks
1.3
Summary
2
Refresh: Using R for data and spatial data
2.1
Introduction
2.1.1
Packages
2.1.2
Data
2.2
R as a GIS
2.2.1
Creating point data
2.2.2
Intersections and Clip Operations
2.2.3
Merging spatial features and Buffers
2.2.4
Point-in-polygon and Area calculations
2.2.5
Creating distance attributes
2.2.6
Combining spatial datasets and their attributes
2.2.7
Reading and writing data in and out of R/RStudio
2.3
Answer to Tasks
2.4
Optional: mapping with
tmap
and
ggplot2
2.4.1
Mapping with
tmap
2.4.2
Mapping with
ggplot2
2.5
Useful Resources
3
Regression
3.1
Introduction
3.1.1
Which regression approach?
3.1.2
Packages
3.1.3
Data
3.2
Standard linear regression models (OLS)
3.3
Binomial Logistic Regression for binary variables
3.4
Poisson Regression for count variables
3.5
Outliers and Robust regression
3.6
Answers to Tasks
3.7
Addendum: Prediction vs Inference
3.8
Optional: the MAUP
3.8.1
Introduction
3.8.2
Impact on Correlations of Counts
3.8.3
Impact on Correlation of Rates
3.9
Additional Resources
4
Spatial Interpolation
4.1
Introduction
4.1.1
Packages
4.1.2
Data for KDE and IDW
4.1.3
Data for Area Weighted and Dasymetric interpolation
4.2
Kernel Density Estimation (point observations)
4.3
Inverse Distance Weighting (point values)
4.4
Area Weighted interpolation (area to area)
4.5
Dasymetric Interpolation
4.6
Answers to Tasks
4.7
Optional: Fine tuning IDW
4.7.1
Answer to Additional Task
5
Agent Based Models (with Jiaqi)
5.1
TBC
6
Principles of Data Visualisation (Roger)
6.1
TBC
7
Reading Week
7.1
Introduction
7.2
Spatial Autocorrelation and cluster analysis
7.2.1
Data and Packages
7.2.2
Neighbours and Lagged Mean Plots
7.2.3
Moran’s I for determining Clusters and Spatial Autocorrelation
7.2.4
LISA for determining
local
clusters and spatial autocorrelation
7.2.5
Getis-Ord G statistic
7.2.6
Answers to Tasks
7.2.7
Links
7.2.8
References
7.3
Autoregressive models
7.3.1
Data and Packages
7.3.2
Simultaneous AutoRegressive models
7.3.3
Conditional AutoRegressive models
7.3.4
Models with predictors: A Bivariate Example
7.3.5
Links: SAR vs CAR
7.3.6
References
8
Spatial Models - Geographically Weighted Regression
8.1
Introduction
8.2
Geographically Weighted Regression
8.2.1
Description
8.2.2
Undertaking a GWR
8.2.3
Evaluating the results
8.2.4
Mapping GWR outputs
8.2.5
GWR Summary
8.3
Multi-scale GWR
8.3.1
Description
8.3.2
Undertaking a MGWR
8.3.3
Evaluating the results
8.3.4
Mapping MGWR outputs
8.3.5
Comapring GWR and MGWR
8.3.6
MGWR Summary
8.4
Answers to Tasks
9
Location Allocations
9.1
TBC
10
APIs and Big Data
10.1
TBC
11
Classification and Alternative Representations
11.1
TBC
12
Spatial Interaction Models
12.1
Introduction
12.2
Data
References
GEOG3915 GeoComputation and Spatial Analysis practicals
Chapter 10
APIs and Big Data
10.1
TBC