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Overview: This book is the first in a two-volume series that introduces the field of Spatial Data Science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user’s guide for the widely adopted GeoDa open-source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa. The libgeoda library has a clearly defined Application Programming Interface (API), which allows other C++ code to access its functionality directly. In fact, this is what currently happens under the hood for part of desktop GeoDa, and in the experimental web-GeoDa (jsgeoda, implemented through javascript). In addition to achieving a more flexible interaction with different graphical user interface implementations, the API also allows other software, such as R or Python programs to access the functionality through well-defined wrapper code.
Genre: Non-Fiction > Tech & Devices
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