The seven core chapters offer a route from noticing geographical differences to examining evidence and communicating what the evidence can—and cannot—tell us. Statistics, spatial analysis and visualisation are treated as connected forms of geographical reasoning rather than isolated techniques.
You can read the chapters in order, or use this roadmap to choose a topic and follow its links to related methods.
A roadmap through the book¶
The middle branches are complementary analytical perspectives, not mandatory prerequisites for one another. A problem may call for one or several of them. Visualisation also supports exploration throughout the book; Chapter 7 returns to it explicitly as a way of explaining and communicating evidence.
Find your starting point¶
| Chapter | Guiding question | Begin here |
|---|---|---|
| 1. Introduction | How do observations become geographical questions? | Geographical and statistical thinking |
| 2. Spatial data | What do our data represent, and how should we prepare them? | Spatial data and representation |
| 3. Statistical patterns | How can we describe and compare groups without overstating differences? | Statistical patterns |
| 4. Global point patterns | Is an observed point distribution consistent with a reference pattern? | Global point patterns |
| 5. Local point patterns | Where do density and clustering vary within a study area? | Local point patterns |
| 6. Areal patterns | How are values associated across neighbouring areas? | Areal patterns |
| 7. Geovisualisation | How can we communicate findings clearly and responsibly? | Geovisualisation and storytelling |
Choosing a reading path¶
New to the subject? Start with Chapters 1 and 2, then read Chapters 3–6 to explore different forms of geographical patterns. Finish with Chapter 7 to consider how analytical choices shape the story we tell. Some longer sections offer optional deeper explanations: a first reading need not include every technical detail.
Looking for a particular method? Enter at the relevant chapter. For point-event clustering, start with Chapters 4 or 5; for spatial autocorrelation among areas, start with Chapter 6. Refer back to Chapter 2 whenever data representation, boundaries or scale matter.
Teaching or reviewing? Use the guiding questions to select chapters according to the reasoning students need to practise, rather than assuming all chapters must be taught in one fixed order.
From reading to practice¶
The worked examples connect geographical questions to analytical decisions and interpretation, while data and notebooks provide opportunities to explore selected methods in Python. These resources complement the explanations in Part 1: you do not need to complete every practical exercise before continuing to the next chapter.