Date/Time
Date(s) - 17/01/2018
10:00 am - 4:30 pm
Categories No Categories
We are pleased to offer you this short course jointly organised by the Consumer Data Research Centre (CDRC) and the Administrative Data Research Centre for England (ADRC-E).
Please note this course can be taken as a one-day course, or can also be taken in conjunction with other two one-day courses on 15 January and 16 January 2018.
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.
Target Audience
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 16 January where these skills are covered.
Further course details, including fees can be found here.
More information regarding our courses can be found here.
Podcast for some of our previous courses can be found here.
Course Code (to quote to the ADRCE)
ADRCE-training049 Bearman
Course Date
17th January 2018
Places Available
25
Course Leader
Dr Nick Bearman
Course Description
Course Contents:
- Basic spatial analysis and statistics, such as Moran’s I and Local Indicators of Spatial Autocorrelation
- Using GeoDa and R to perform these analysis 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
By the end of the course, students will be able to:
- 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