Overview

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.

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9781800372818

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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

'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

Author Information

Daniel A. Griffith, Ashbel Smith Professor of Geography and Geospatial Information Science, School of Economic, Political, and Policy Sciences, University of Texas at Dallas and Bin Li, Professor of Geography, Department of Geography and Environmental Studies, Central Michigan University, US

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