Space (doesn't have to be) the Final Frontier: Some Tips in Accounting for Spatial Dependence within Your Regression-Type Models in R

Overview

If your data has some reference to where it was collected, then you can probably consider it to be “spatial” in some way. Why does that matter? Observations/measurements collected closer to one another are often more closely related. When modelling your variable of interest, this spatial dependence usually needs to be accounted for to make the model useful for things like prediction or inference. This can even be true for models that boast a healthy suite of predictor variables.

In this seminar I will use examples to demonstrate how we can account for spatial dependence (and when we might want to) within regression-style models. I do this by including the dataset’s spatial information within (generalised) additive/mixed models. If this sounds daunting, fear not. In practice, these models are just a straightforward extension of the humble linear regression model – representing one small step for applied researchers (even if a giant leap for math-kind)!

The talk will be about 40 minutes long and following by discussion.

Date: Thursday 24 October 2024

Time: 2.30pm - 3.30pm

Guest Speaker: Elliot Dovers, Research Associate, School of Mathematics and Statistics

Delivery Mode: Hybrid

Location: E19 Patricia O'Shane G04 (previously CLB 3)

Meeting Link: See ordered ticket

FAQs

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Please send us an email at: stats.central@unsw.edu.au

Seminar: Space (doesn't have to be) the Final Frontier: Some Tips in Accounting for Spatial Dependence within Your Regression-Type Models in R

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