This is a training and capacity building event organised by the Consumer Data Research Centre (CDRC), an ESRC funded research project of which Jonathan Reynolds is Deputy Director and Co-Investigator.
In this course, we will cover how to prepare and analyse spatial data in RStudio & GeoDa. We will use RStudio to perform spatial overlay techniques (such as union, intersection, and buffers) to combine different spatial data layers to support a spatial analysis decision.
We will also use RStudio and GeoDa to explore a range of different spatial analyses including Moran’s I and clustering. By the end of the course, you will understand how RStudio manages spatial data and be able to use RStudio for a range of spatial analysis.
This course is ideal for anyone who wishes to use spatial data in their role. If you are not already familiar with the basic elements of GIS or R, you may wish to attend the one-day course ‘Introduction to Spatial Data & Using R as a GIS’ prior to this course on 23rd April 2018 where these skills are covered.
Basic spatial analysis and statistics, such as Moran’s I and Local Indicators of Spatial Autocorrelation
Using GeoDa and R to perform these analyses and understand the outputs
Be aware of the advantages and disadvantages of different pieces of software
Perform spatial decision making in R, using buffers, overlays, and spatial joins
Know how to use RStudio and GeoDa for a range of spatial analysis
Understand why spatial autocorrelation is important and how to measure it
Be able to use GeoDa to perform clustering analysis
Understand how to use buffers and overlays to support your proposals
Develop your confidence in using RStudio for data handling using scripts
Know how to develop custom functions in RStudio
Please note this course can be taken as a one-day course, or can also be taken in conjunction with another one-day course on 23rd April 2018.
All fees include event materials, lunch, morning and afternoon tea. They do not include travel and accommodation costs.
Numbers on the course are limited and allocated on a first come, first served basis.