Explains how to implement, interpret, and conduct diagnostics on the results of multivariate techniques. The book focuses on geo-referenced data analysis applications, with explicit diagnostics for the role played by spatial autocorrelation in multivariate analyses. It also aims to establish specific connections between popular spatial analysis and multivariate procedures, and outlines methodology for implementing spatial auto, logistic, and Poisson regressions.

Multivariate Statistical Analysis for Geographers

Science & Math

Griffith, Daniel A.

Amrhein, Carl G.

Earth Sciences

Pearson College Div

Covers principles of statistics with spatial (geographic) applications. The text is appropriate for statistics courses found in geography departments.

Statistical Analysis for Geographers

Desloges, Joseph R.

PRENTICE/HALL

This volume compiles geostatistical and spatial autoregressive data analyses involving georeferenced socioeconomic, natural resources, agricultural, pollution, and epidemiological variables. Benchmark analyses are followed by analyses of readily available data sets, emphasizing parallels between geostatistical and spatial autoregressive findings. Both SAS and SPSS code are presented for implementation purposes. This informative casebook will serve geographers, regional scientists, applied spatial statisticians, and spatial scientists from across disciplines.

A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets (Spatial Information Systems)

Layne, Larry J.

Oxford University Press

x 82p blue paperback, excellent fresh copy, many tables, first edition, bright and clean

Spatial Autocorrelation: A Primer (RESOURCE PUBLICATIONS IN GEOGRAPHY)

Assn of Amer Geographers

Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology (SAGE Advances in Geographic Information Science and Technology Series)

Spatial Statistics and Geostatistics is the definitive text on spatial statistics. Its focus is on spatial statistics as a distinct form of statistical analysis and it includes computer components for ArcGIS, R, SAS, and WinBUGS. The teaching and learning objective of the text is to illustrate the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all the relevant programs and software. Fully explanatory, Spatial Statistics and Geostatistics uses boxed computer code, diagrams, illustrations; and includes further readings. Case study and exemplary materials and data sets are also included.

Chun, Yongwan

Reference

Sage Publications Ltd.

Advanced Introduction to Spatial Statistics (Elgar Advanced Introductions series)

Business & Money

Li, Bin

Economics

Edward Elgar Publishing

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.\nThis 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.\nKey Features:\nA 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\nContemporary 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\nA detailed articulation of a spatial statistical workflow conceptualization\nThe 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.

Scientific visualization may be defined as the transformation of numerical scientific data into informative graphical displays. The text introduces a nonverbal model to subdisciplines that until now has mostly employed mathematical or verbal-conceptual models. The focus is on how scientific visualization can help revolutionize the manner in which the tendencies for (dis)similar numerical values to cluster together in location on a map are explored and analyzed. In doing so, the concept known as spatial autocorrelation - which characterizes these tendencies - is further demystified.

Spatial Autocorrelation and Spatial Filtering

Springer

In recent years there has been a growing interest in and concern for the development of a sound spatial statistical body of theory. This work has been undertaken by geographers, statisticians, regional scientists, econometricians, and others (e. g. , sociologists). It has led to the publication of a number of books, including Cliff and Ord's Spatial Processes (1981), Bartlett's The Statistical Analysis of Spatial Pattern (1975), Ripley's Spatial Statistics (1981), Paelinck and Klaassen's Spatial Economet~ics (1979), Ahuja and Schachter's Pattern Models (1983), and Upton and Fingleton's Spatial Data Analysis by Example (1985). The first of these books presents a useful introduction to the topic of spatial autocorrelation, focusing on autocorrelation indices and their sampling distributions. The second of these books is quite brief, but nevertheless furnishes an eloquent introduction to the rela tionship between spatial autoregressive and two-dimensional spectral models. Ripley's book virtually ignores autoregressive and trend surface modelling, and focuses almost solely on point pattern analysis. Paelinck and Klaassen's book closely follows an econometric textbook format, and as a result overlooks much of the important material necessary for successful spatial data analy sis. It almost exclusively addresses distance and gravity models, with some treatment of autoregressive modelling. Pattern Models supplements Cliff and Ord's book, which in combination provide a good introduction to spatial data analysis. Its basic limitation is a preoccupation with the geometry of planar patterns, and hence is very narrow in scope.

Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series (Advanced Studies in Theoretical and Applied Econometrics, 12)

See the best price to sell, buy, or rent books by Griffith, Daniel A.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Griffith, Daniel A.

See the best price to sell, buy, or rent books by Griffith, Daniel A.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Multivariate Statistical Analysis for Geographers

Statistical Analysis for Geographers

A Casebook for Spatial Statistical Data Analysis: A Compilation of Analyses of Different Thematic Data Sets (Spatial Information Systems)

Spatial Autocorrelation: A Primer (RESOURCE PUBLICATIONS IN GEOGRAPHY)

Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology (SAGE Advances in Geographic Information Science and Technology Series)

Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology (SAGE Advances in Geographic Information Science and Technology Series)

Advanced Introduction to Spatial Statistics (Elgar Advanced Introductions series)

Advanced Introduction to Spatial Statistics (Elgar Advanced Introductions series)

Spatial Autocorrelation and Spatial Filtering

Advanced Spatial Statistics: Special Topics in the Exploration of Quantitative Spatial Data Series (Advanced Studies in Theoretical and Applied Econometrics, 12)