This chapter

• Presents the basic concepts, terms and definitions pertaining to spatial analysis

• Introduces a spatial analysis workflow that follows a Describe–Explore–Explain structure

• Presents in detail the reasons why spatial data are special, namely spatial autocorrelation, scale, the modifiable area unit problem, spatial heterogeneity, the edge effects and the ecological fallacy

• Explains why conceptualization of spatial relationships is extremely important in spatial analysis

• Presents the approaches used to conceptualize spatial relationships

• Explains how distance, contiguity/adjacency, neighborhood, proximity polygons and space–time window are used in space conceptualization

• Defines the spatial weights matrix, which is essential to almost every spatial statistic/ technique

• Introduces the real-world project along with the related dataset to be worked throughout the book

After a thorough study of the theory and lab sections, you will be able to

• Implement a comprehensive workflow when you conduct spatial analysis

• Distinguish spatial from nonspatial data

• Understand why spatial data should be treated with new methods (e.g., spatial statistics)

• Understand the importance of applying conceptualization methods according to the problem at hand

• Understand essential terms for conducting spatial analysis as for example distance, contiguity/adjacency, neighborhood, proximity polygons and space–time

• Describe the spatial analysis process to be adopted for solving the real-world project of this book

• Presents the project’s data with ArcGIS and GeoDa