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Basic Concepts of Spatial Analysis and Space Conceptualization
Published online by Cambridge University Press: 20 May 2020
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
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