The Urban Big Data Centre at the University of Glasgow is pleased to announce the return of our SASNet data skills training programme for summer 2017.

The programme will run from 1st August - 6th September offering an opportunity for skills development in key areas of data analytics, methods, and data management. Please visit http://ubdc.ac.uk/outreach-plus-training/sasnet/summer-training-2017 to view the full selection of courses.

Please contact keith.maynard@glasgow.ac.uk with any enquiries about the training programme.

Introduction to Areal Data Modelling using R

Course instructor: Francesca Pannullo and Charis Chanialidis, School of Mathematics and Statistics, University of Glasgow

Course duration: half day (Wednesday 30th August 2017, 10:00am – 1:00pm)

Course location: Jura teaching lab, Level 4 Annexe, Glasgow University Library

Audience: Social scientists, students and practitioners

Fees: £25 - For UK registered students
£35 - For staff at UK academic institutions, Research Council UK funded researchers, UK public sector staff and staff at UK registered charity organisations
£50 - For all other participants

Pre-requisite knowledge: Introduction to R and some previous knowledge of spatial data is preferred.

Course summary:

Spatial data are an increasingly common form of data in many different applications. For modelling, there are three main forms of spatial data, point process, areal and geostatistical.

This course focuses on areal data, which are characterised by the region of interest being partitioned into a set of non-overlapping areal units and the feature of interest is recorded at each areal unit. Applications might include the number of hospital admissions recorded at each datazone, or the number of crimes recorded at postcode level.

There are a number of different statistical models that can be used in such a context, dependent on the questions of interest. They have in common one feature, that we cannot assume that the observations are independent, so we will need to introduce and model forms of spatial dependence.
The objectives of this course are to introduce some common spatial regression models, to use R in visualizing and modelling such types of spatial data.

By the end of the course, you will be understand different measures of spatial dependence, be able to fit, test and check spatial models and undertake statistical inference on the model output.

Course content:

• Read spatial data from a variety of data formats.
• Visualise areal data.
• Assess the degree of spatial dependence, including Moran’s I.
• Development of spatial regression models (SAR and CAR), fitting and checking.
• Drawing inferences from the model results.

Payment and registration:

Lunch will be provided in the break room adjacent to the IT lab - please specify any dietary or access requirements when registering.

Payment by card preferred.

If you are unable to pay by debit or credit card we are able to accept payment by invoice. Please supply the following information by e-mail to keith.maynard@glasgow.ac.uk within 14 days of registering on Eventbrite in order to confirm your place on the course:

• Contact name for invoice
• Purchase Order number (note that the supplier is University of Glasgow)
• Contact e-mail address for payment correspondence
• Full name of organisation/department
• VAT registration (if applicable)
• Currency
• Address for Invoice to be sent and telephone number

To cancel your place and request a refund, please email keith.maynard@glasgow.ac.uk. Please note that a refund of course fees less 10% administrative charge can only be issued if you cancel your place in writing at least 7 days prior to the course after which time fees will be non-refundable. Substitutes can be made at no extra charge up until 5pm on the last working day before the course.

Related 2017 courses:

Introduction to R

Using R: Mapping Spatial Data

Principles of Visualising Data with R and ggplot

Getting started with Data Management

Measuring Segregation and Scale in R