As mentioned in Lecture 2, and as discussed in Chapter 4 of your text, spatial is special. Phenomenon such as spatial autocorrelation and distance decay produce effects such as the Modifiable Areal Unit Problem (MAUP) scale and aggregation effects when working with real data. There are a number of complex concepts covered in this chapter (temporal and spatial autocorrelation, spatial sampling, distance decay, induction vs deduction, intensive vs extensive), but as you will discover in your labs they do affect our interpretations of the world (as represented in a GIS). Here is a link to a 'game' that asks you to create patterns with different spatial autocorrelations.

In today's lecture I will review the content of Chapter 4 of your text.

Learning objectives:

• Understand that spatial autocorrelation is the formalization of Tobler's First Law of Geography;
• Recognize the dependencies between scale and representation;
• The principles of building representations around geographic samples;
• How the properties of smoothness and continuous variation can be used to characterize geographic variation;
• How fractals can be used to measure and simulate surface roughness.

Text: Chapter 4: The nature of geographic data [Overheads: 1 per page; 3 per page]

Keywords: spatial autocorrelation and the Tobler Law; scale; representation; fractals and self-similarity; spatial sampling; distance decay; induction and deduction; isopleth maps; choropleth maps; regression..