Advanced Introduction to Spatial Statistics : Elgar Advanced Introductions - Daniel A. Griffith

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Advanced Introduction to Spatial Statistics

By: Daniel A. Griffith, Bin Li

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Paperback | 5 August 2022

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Elgar Advanced Introductions are stimulating and thoughtful introductions to major fields in the social sciences, business and law, expertly written by the world's leading scholars. Designed to be accessible yet rigorous, they offer concise and lucid surveys of the substantive and policy issues associated with discrete subject areas.

This Advanced Introduction provides a critical review and discussion of research concerning spatial statistics, differentiating between it and spatial econometrics, to answer a set of core questions covering the geographic-tagging-of-data origins of the concept and its theoretical underpinnings, conceptual advances, and challenges for future scholarly work. It offers a vital tool for understanding spatial statistics and surveys how concerns about violating the independent observations assumption of statistical analysis developed into this discipline.

Key Features:

A concise overview of spatial statistics theory and methods, looking at parallel developments in geostatistics and spatial econometrics, highlighting the eclipsing of centography and point pattern analysis by geostatistics and spatial autoregression, and the emergence of local analysis
Contemporary descriptions of popular geospatial random variables, emphasizing one- and two-parameter spatial autoregression specifications, and Moran eigenvector spatial filtering coupled with a broad coverage of statistical estimation techniques
A detailed articulation of a spatial statistical workflow conceptualization

The helpful insights from empirical applications of spatial statistics in agronomy, criminology, demography, economics, epidemiology, geography, remotely sensed data, urban studies, and zoology/botany, will make this book a useful tool for upper-level students in these disciplines.

Industry Reviews

'With widespread and increasingly available georeferenced data, this book offers a timely assessment of contemporary methods, models, and metrics-such as the eigenvector spatial filtering approach to handling spatial autocorrelation-in spatial statistics. I salute the authors for this enlightening contribution! The book will greatly empower us to better uncover mechanisms behind georeferenced data.' -- Li An, San Diego State University, US

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