About this product

Product Identifiers

PublisherElgar Publishing, Incorporated, Edward

ISBN-101800372833

ISBN-139781800372832

eBay Product ID (ePID)25057253827

Product Key Features

Number of Pages200 Pages

Publication NameAdvanced Introduction to Spatial Statistics

LanguageEnglish

SubjectHuman Geography, General, Econometrics, Statistics

Publication Year2022

TypeTextbook

AuthorDaniel A. Griffith, Bin Li

Subject AreaMathematics, Social Science, Business & Economics

SeriesElgar Advanced Introductions Ser.

FormatTrade Paperback

Dimensions

Item Height0.4 in

Item Weight9.9 Oz

Item Length8.5 in

Item Width5.5 in

Additional Product Features

Intended AudienceCollege Audience

LCCN2022-938778

Dewey Edition23

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.'

IllustratedYes

Dewey Decimal001.422

Table Of ContentContents: Preface 1. An advanced introduction to spatial statistics: motivation and scope 2. Describing spatial random variables 3. Spatial statistical model parameter estimation 4. A spatial statistical modeling workflow 5. Applications from A to Z of spatial statistical modeling 6. Nonparametric spatial statistical models Afterword References Index

SynopsisElgar 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.

LC Classification NumberQA278.2