Skip to main content

Introduction to Spatial Analysis

Introduction to Spatial Analysis

Preface

Introduction to Spatial Analysis will introduce you to the principles and methods of spatial data analysis, with a view to developing deep understanding of how we conceptualise and model spatial patterns and relationships. You will be equipped with the knowledge and skills to identify and utilise appropriate spatial analyses, to critically assess outputs, and to communicate results to academic and non-academic audiences. You will also gain practical skills in ArcGIS Pro, and experience working with a range of environmental, social and ecological datasets.

In Introduction to Spatial Analysis, we will:

Chapter links will become available as the unit progresses.

Intended Learning Outcomes

The Intended Learning Outcomes for Introduction to Spatial Analysis are available on Canvas as a single source of truth.

Guidance

As you progress through the following practicals, your main objective is to develop understanding of how we conceptualise and model spatial patterns and relationships.

You will develop this understanding by working with spatial data, running tools, and inspecting outputs, with instructions formatted as follows:

This is an instruction.

You will see the following formatting for questions or as a prompt for reflection:

This is a question.

For course development and testing, the following formatting is for problems to be fixed:

This is a problem.

The following formatting is used for tasks for Matt to complete:

This is a task.

The following formatting is used for advice or key notes:

This is a note.

The following formatting is used for quotations:

This is a quote.

Acknowledgments

The design of the practical materials has been improved thanks to resources made available by the wider GIS community, including Sergio J. Rey, Dani Arribas-Bel and Levi J. Wolf (Geographic Data Science with Python), Luc Anselin (An Introduction to Spatial Data Science with GeoDa), Edzer Pebesma and Roger Bivand (Spatial Data Science with Applications in R), Paula Moraga (Spatial Statistics for Data Science: Theory and Practice with R), and Adam Dennett (Guide to Spatial Interaction Modelling).