2nd Edition. — CRC Press, 2011. — 336 p. — ISBN: 978-1439829196.Written in recognition of developments in spatial data analysis that focused on differences between places, the first edition of Local Models for Spatial Analysis broke new ground with its focus on local modelling methods. Reflecting the continued growth and increased interest in this area, the second edition describes a wide range of methods which account for local variations in geographical properties.What’s new in the Second Edition:1) Additional material on geographically-weighted statistics and local regression approaches. 2) A better overview of local models with reference to recent critical reviews about the subject area. 3) Expanded coverage of individual methods and connections between them. 4) Chapters have been restructured to clarify the distinction between global and local methods. 5) A new section in each chapter references key studies or other accounts that support the book. 6) Selected resources provided online to support learning.An introduction to the methods and their underlying concepts, the book uses worked examples and case studies to demonstrate how the algorithms work their practical utility and range of application. It provides an overview of a range of different approaches that have been developed and employed within Geographical Information Science (GIScience). Starting with first principles, the author introduces users of GISystems to the principles and application of some widely used local models for the analysis of spatial data, including methods being developed and employed in geography and cognate disciplines. He discusses the relevant software packages that can aid their implementation and provides a summary list in Appendix A.Presenting examples from a variety of disciplines, the book demonstrates the importance of local models for all who make use of spatial data. Taking a problem driven approach, it provides extensive guidance on the selection and application of local models.Contents: Introduction Remit of this book Local models and methods What is local? Spatial dependence and autocorrelation Spatial scale Stationarity Spatial data models Datasets used for illustrative purposes A note on notation Local Modelling Standard methods and local variations Approaches to local adaptation Stratification or segmentation of spatial data Moving window/kernel methods Locally-varying model parameters Transforming and detrending spatial data Categorising local statistical models Local models and methods and the structure of the book Grid Data Exploring spatial variation in gridded variables Global univariate statistics Local univariate statistics Analysis of grid data Moving windows for grid analysis Wavelets Segmentation Analysis of digital elevation models Spatial Patterning in Single Variables Local summary statistics Geographically weighted statistics Spatial autocorrelation: Global measures Spatial autocorrelation: Local measures Spatial association and categorical data Other issues Spatial Relations Global regression Spatial and local regression Regression and spatial data Spatial autoregressive models Multilevel modelling Allowing for local variation in model parameters Moving window regression (MWR) Geographically weighted regression (GWR) Spatially weighted classi¯cation Local regression methods: Some pros and cons Spatial Prediction 1: Deterministic Methods, Curve Fitting, and Smoothing Point interpolation Global methods Local methods Areal interpolation General approaches: Overlay Local models and local data Limitations: Point and areal interpolation Spatial Prediction 2: Geostatistics Random function models Stationarity Global models Exploring spatial variation Kriging Globally constant mean: Simple kriging Locally constant mean models Ordinary kriging Cokriging Equivalence of splines and kriging Conditional simulation The change of support problem Other approaches Local approaches: Nonstationary models Nonstationary mean Nonstationary models for prediction Nonstationary variogram Variograms in texture analysis Point Patterns and Cluster Detection Point patterns Visual examination of point patterns Measuring event intensity and distance methods Statistical tests of point patterns Global methods Measuring event intensity Distance methods Other issues Local methods Measuring event intensity locally Accounting for the population at risk The local K function Point patterns and detection of clusters Summary: Local Models for Spatial Analysis A Software References Index
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2nd Edition. — NY: CRC Press, 2014. 584 p. — (Monographs on Statistics and Applied Probability) — ISBN: 1439819173.
Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the...
Монография, Springer-Verlag, Berlin, 2010. — 828 p. — ISBN: 978-3-642-03646-0.
GI Software Tools
Spatial Statistics in ArcGIS
Spatial Statistics in SAS
Spatial Econometric Functions in R
GeoDa: An Introduction to Spatial Data Analysis
STARS: Space-Time Analysis of Regional Systems
Space-Time Intelligence System Software for the Analysis of...
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Amsterdam: Elsevier Science, 2009. — 765 р. — (Developments in Soil Science. Volume 33).
Geomorphometry is the science of quantitative land-surface analysis. It draws upon mathematical, statistical, and image-processing techniques to quantify the shape of earth's topography at various spatial scales. The focus of geomorphometry is the calculation of surface-form measures...
Wiley, 2008. — 560 р. — ISBN: 9780470014912.
Spatial point processes are mathematical models used to describe and analyse the geometrical structure of patterns formed by objects that are irregularly or randomly distributed in one-, two- or three-dimensional space. Examples include locations of trees in a forest, blood particles on a glass plate, galaxies in the universe, and...
Academic Press, 2009. — 864 p. — ISBN: 0123747651. Robert Nisbet, Pacific Capital Bank Corporation, Santa Barbara, CA, USA John Elder, Elder Research, Inc. and the University of Virginia, Charlottesville, USA Gary Miner, StatSoft, Inc. , Tulsa, OK, USA Description The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that...