You should be familiar, by now, with the basic spatial analytical capabilities of GIS since you have been performing many of them in your labs (both 270 and 370). However, since the contents of Chapters 13, 14 & 15 in your text, or in Chapters 7 & 8 in GIS Basics, and Chapters 5, 6 & 7 in Geospatial Analysis, cover a range of material, in this lecture I will cover an eclectic selection of the methods of spatial analysis.
Learning Objectives
- Be able to define spatial data analysis and to determine whether a method is spatial or not.
- Know of some of the methods for detecting relations between the various properties of places and understand the need for preparing data for such tests.
- Understand distance effects, and how they can be used in the creation of clusters, hotspots, and to identify outliers.
- Know what is meant by kernel density.
Required Readings
Text: Chapters 13: Spatial Data Analysis, Chapter 14: Spatial Analysis and Inference, and Chapter 15: Spatial Modeling with GI Systems
Slides for the Spatial Analysis I lecture.
Alternative text: GIS Basics: Chapters 7 & 8
Recommended Readings
Chapters 5, 6 & 7 of the Geospatial Analysis online text covers much of this material. (If you are in the GIS Minor stream you should definitely review the material presented in this online text.)
Useful Resources
ArcGIS Resources: Spatial Statistics Resources
A very useful resource--for those who really want to delve into spatial analysis, local and global spatial autocorrelation, and much more--are the recorded lectures of Luc Anselin from the University of Chicago.
GeoDa--the program I demonstrated in class that was developed by Luc Anselin. A very useful program with which to explore spatial data and try out different spatial data analysis tools.
Keywords
joins, buffers, Ripley's K function, kernel density estimation, Anselin Local Moran's I