Training and Events
Confident Spatial Analysis and Statistics in R & GeoDa
|ADRC-E/University of Southampton & CDRC/UCL|
Dr Nick Bearman
Southampton Statistical Sciences Research Institute, Building 39, University of Southampton, Southampton
View in Google Maps (SO17 1BJ)
Course No.: ADRCE Training049 Bearman
Course places are limited and registration by 10 January 2018 is strongly recommended.
We are pleased to offer you this short course jointly organised by the Administrative Data Research Centre for England (ADRC-E) www.adrn.ac.uk/about/network/england and Consumer Data Research Centre (CDRC) www.cdrc.ac.uk
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.
If you are not already familiar with the basic elements of GIS or R, you may wish to attend the course “Introduction to Spatial Data & Using R as a GIS” prior to this course 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 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
Dr Nick Bearman, FRGS, CGeog (GIS), AFHEA
Nick has been teaching undergrad & postgrad level at a range universities in the UK including Universities of Liverpool, East Anglia and Exeter.
He is a Chartered Geographer (GIS), Fellow of Royal Geographical Society (RGS) and Associate Member of Higher Education Authority, with 9 years’ experience. He has advanced knowledge of Geographic Information Systems (GIS), working with many sources and types of spatial and non-spatial data, including big data.
Nick has taught GIS to a wide range of students across many subject areas including Geography, International Development and Public Health. The courses range from day courses to two week residential courses, for users from undergraduate students with no GIS experience to advanced spatial analysis, designed to give the students the best set of skills for the work they need to do.
This course is ideal for anyone who wishes to use spatial data in their role and already has a basic knowledge of GIS and R. The target audience includes government & other public sector researchers who have spatial data and wish to perform more detailed analysis.
This course is for you if you already know the basics of GIS, but want to develop your GIS skills in R. Some scripting experience is recommended. In this course you will understand how RStudio manages spatial data and be able to use RStudio for a range of spatial analysis. If you are not already familiar with the basic elements of GIS or R, you may wish to attend the course “Introduction to Using R for Spatial Analysis” prior to this course where these skills are covered.
Intermediate (some prior knowledge)
Website and registration
Spatial Data Analysis, Geographical Information System (GIS), Spatial data, GIS, R, RStudio, GeoDa, exploratory data analysis, spatial autocorrelation, spatial data analysis, overlays, map, mapping, geographic information systems, clustering
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