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Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications 2010 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 811 pages, kõrgus x laius: 235x155 mm, kaal: 1246 g, XV, 811 p., 1 Paperback / softback
  • Ilmumisaeg: 31-Oct-2014
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 364242452X
  • ISBN-13: 9783642424526
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Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications 2010 ed.Suurem pilt
  • Formaat: Paperback / softback, 811 pages, kõrgus x laius: 235x155 mm, kaal: 1246 g, XV, 811 p., 1 Paperback / softback
  • Ilmumisaeg: 31-Oct-2014
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 364242452X
  • ISBN-13: 9783642424526
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783642424526.html
Märksõnad:

This book details current models, methods, and techniques for the analysis of spatial data. It includes a number of example sections that demonstrate the application of spatial analysis in the economic, environmental and health sciences.



The Handbook is written for academics, researchers, practitioners and advanced graduate students. It has been designed to be read by those new or starting out in the field of spatial analysis as well as by those who are already familiar with the field. The chapters have been written in such a way that readers who are new to the field will gain important overview and insight. At the same time, those readers who are already practitioners in the field will gain through the advanced and/or updated tools and new materials and state-of-the-art developments included. This volume provides an accounting of the diversity of current and emergent approaches, not available elsewhere despite the many excellent journals and te- books that exist. Most of the chapters are original, some few are reprints from the Journal of Geographical Systems, Geographical Analysis, The Review of Regional Studies and Letters of Spatial and Resource Sciences. We let our contributors - velop, from their particular perspective and insights, their own strategies for m- ping the part of terrain for which they were responsible. As the chapters were submitted, we became the first consumers of the project we had initiated. We gained from depth, breadth and distinctiveness of our contributors’ insights and, in particular, the presence of links between them.

Arvustused

From the reviews:

The Handbook of Applied Spatial Analysis provides an important summary of, and gateway into, the rapidly developing field of spatial analysis. It aims to provide both a useful guide for researchers at all levels in spatial analytic fields and a basis for more in-depth research. certainly fulfils the expectations set out in its blurb. It provides a thorough insight into contemporary spatial analysis that many with an interest in this field will find useful. (James Cheshire, Environment and Planning B: Planning and Design, Vol. 37, 2010)

It provides a comprehensive introduction to a variety of problems and methods that may be beneficial to students and researchers who intend to learn and apply spatial analysis techniques in their studies and/or research. More importantly, the book provides valuable breadth and depth in its treatment of many topics. suitable for readers without substantial prior knowledge of spatial analysis. A book with such a combination of topics and qualities is a pleasant and valuable addition to the rich literature in this area. (Changshan Wu, Journal of Regional Science, Vol. 52 (2), 2012)

The editors have done an excellent job to bring together conceptual knowledge and application pursuits while empowering readers with the information they need to find these resources and try some of the included techniques using the examples to guide them. The book will be attractive to higher level students and professionals . There are not many books as up-to-date with such a wide coverage of spatial analysis tools and techniques along with supporting resource information. (Jeff Thurston, Sensors & Systems, July, 2010)

Preface v
Introduction 1 (26)
Manfred M. Fischer
Arthur Getis
Part A: GI Software Tools
A.1 Spatial Statistics in ArcGIS
Lauren M. Scott
Mark V. Janikas
A.1.1 Introduction
27(1)
A.1.2 Measuring geographic distributions
28(2)
A.1.3 Analyzing patterns
30(3)
A.1.4 Mapping clusters
33(2)
A.1.5 Modeling spatial relationships
35(3)
A.1.6 Custom tool development
38(1)
A.1.7 Concluding remarks
39(1)
References
40(3)
A.2 Spatial Statistics in SAS
Melissa J. Rura
Daniel A. Griffith
A.2.1 Introduction
43(1)
A.2.2 Spatial statistics and SAS
43(1)
A.2.3 SAS spatial analysis built-ins
44(1)
A.2.4 SAS implementation examples
45(6)
A.2.5 Concluding remarks
51(1)
References
51(2)
A.3 Spatial Econometric Functions in R
Roger S. Bivand
A.3.1 Introduction
53(2)
A.3.2 Spatial models and spatial statistics
55(2)
A.3.3 Classes and methods in modelling using R
57(3)
A.3.4 Issues in prediction in spatial econometrics
60(5)
A.3.5 Boston housing values case
65(3)
A.3.6 Concluding remarks
68(1)
References
69(4)
A.4 GeoDa: An Introduction to Spatial Data Analysis
Luc Anselin
Ibnu Syabri
Youngihn Kho
A.4.1 Introduction
73(3)
A.4.2 Design and functionality
76(2)
A.4.3 Mapping and geovisualization
78(2)
A.4.4 Multivariate EDA
80(2)
A.4.5 Spatial autocorrelation analysis
82(2)
A.4.6 Spatial regression
84(2)
A.4.7 Future directions
86(1)
References
87(4)
A.5 STARS: Space-Time Analysis of Regional Systems
Sergio J Rey
Mark V. Janikas
A.5.1 Introduction
91(1)
A.5.2 Motivation
92(1)
A.5.3 Components and design
92(6)
A.5.4 Illustrations
98(11)
A.5.5 Concluding remarks
109(2)
References
111(2)
A.6 Space-Time Intelligence System Software for the Analysis of Complex Systems
Geoffrey M. Jacquez
A.6.1 Introduction
113(2)
A.6.2 An approach to the analysis of complex systems
115(1)
A.6.3 Visualization
116(1)
A.6.4 Exploratory space-time analysis
117(2)
A.6.5 Analysis and modeling
119(3)
A.6.6 Concluding remarks
122(1)
References
123(2)
A.7 Geostatistical Software
Pierre Goovaerts
A.7.1 Introduction
125(2)
A.7.2 Open source code versus black-box software
127(1)
A.7.3 Main functionalities
128(3)
A.7.4 Affordability and user-friendliness
131(1)
A.7.5 Concluding remarks
132(1)
References
133(2)
A.8 GeoSurveillance: GIS-based Exploratory Spatial Analysis Tools for Monitoring Spatial Patterns and Clusters
Gyoungju Lee
Ikuho Yamada
Peter Rogerson
A.8.1 Introduction
135(2)
A.8.2 Structure of GeoSurveillance
137(1)
A.8.3 Methodological overview
138(4)
A.8.4 Illustration of GeoSurveillance
142(6)
A.8.5 Concluding remarks
148(1)
References
149(2)
A.9 Web-based Analytical Tools for the Exploration of Spatial Data
Luc Anselin
Yong Wook Kim
Ibnu Syabri
A.9.1 Introduction
151(1)
A.9.2 Methods
152(6)
A.9.3 Architecture
158(5)
A.9.4 Illustrations
163(7)
A.9.5 Concluding remarks
170(1)
References
171(4)
A.10 PySAL: A Python Library of Spatial Analytical Methods
Sergio J. Rey
Luc Anselin
A.10.1 Introduction
175(2)
A.10.2 Design and components
177(3)
A.10.3 Empirical illustrations
180(11)
A.10.4 Concluding remarks
191(1)
References
191(6)
Part B: Spatial Statistics and Geostatistics
B.1 The Nature of Georeferenced Data
Robert P. Haining
B.1.1 Introduction
197(2)
B.1.2 From geographical reality to the spatial data matrix
199(5)
B.1.3 Properties of spatial data in the spatial data matrix
204(4)
B.1.4 Implications of spatial data properties for data analysis
208(6)
B.1.5 Concluding remarks
214(1)
References
214(5)
B.2 Exploratory Spatial Data Analysis
Roger S. Bivand
B.2.1 Introduction
219(1)
B.2.2 Plotting and exploratory data analysis
220(4)
B.2.3 Geovisualization
224(5)
B.2.4 Exploring point patterns and geostatistics
229(7)
B.2.5 Exploring areal data
236(13)
B.2.6 Concluding remarks
249(1)
References
250(5)
B.3 Spatial Autocorrelation
Arthur Getis
B.3.1 Introduction
255(2)
B.3.2 Attributes and uses of the concept of spatial autocorrelation
257(2)
B.3.3 Representation of spatial autocorrelation
259(3)
B.3.4 Spatial autocorrelation measures and tests
262(10)
B.3.5 Problems in dealing with spatial autocorrelation
272(2)
B.3.6 Spatial autocorrelation software
274(1)
References
275(4)
B.4 Spatial Clustering
Jared Aldstadt
B.4.1 Introduction
279(1)
B.4.2 Global measures of spatial clustering
280(9)
B.4.3 Local measures of spatial clustering
289(8)
B.4.4 Concluding remarks
297(1)
References
298(3)
B.5 Spatial Filtering
Daniel A. Griffith
B.5.1 Introduction
301(2)
B.5.2 Types of spatial filtering
303(9)
B.5.3 Eigenfunction spatial filtering and generalized linear models
312(1)
B.5.4 Eigenfunction spatial filtering and geographically weighted regression
313(2)
B.5.5 Eigenfunction spatial filtering and geographical interpolation
315(1)
B.5.6 Eigenfunction spatial filtering and spatial interaction data
316(1)
B.5.7 Concluding remarks
317(1)
References
317(2)
B.6 The Variogram and Kriging
Margaret A. Oliver
B.6.1 Introduction
319(1)
B.6.2 The theory of geostatistics
319(2)
B.6.3 Estimating the variogram
321(6)
B.6.4 Modeling the variogram
327(4)
B.6.5 Case study: The variogram
331(6)
B.6.6 Geostatistical prediction: Kriging
337(7)
B.6.7 Case study: Kriging
344(6)
References
350(5)
Part C: Spatial Econometrics
C.1 Spatial Econometric Models
James P. LeSage
R. Kelley Pace
C.1.1 Introduction
355(5)
C.1.2 Estimation of spatial lag models
360(5)
C.1.3 Estimates of parameter dispersion and inference
365(1)
C.1.4 Interpreting parameter estimates
366(8)
C.1.5 Concluding remarks
374(1)
References
374(3)
C.2 Spatial Panel Data Models
J. Paul Elhorst
C.2.1 Introduction
377(1)
C.2.2 Standard models for spatial panels
378(4)
C.2.3 Estimation of panel data models
382(7)
C.2.4 Estimation of spatial panel data models
389(10)
C.2.5 Model comparison and prediction
399(4)
C.2.6 Concluding remarks
403(2)
References
405(4)
C.3 Spatial Econometric Methods for Modeling Origin-Destination Flows
James P. LeSage
Manfred M. Fischer
C.3.1 Introduction
409(1)
C.3.2 The analytical framework
410(6)
C.3.3 Problems that plague empirical use of conventional spatial interaction models
416(15)
C.3.4 Concluding remarks
431(1)
References
432(3)
C.4 Spatial Econometric Model Averaging
Olivier Parent
James P. LeSage
C.4.1 Introduction
435(1)
C.4.2 The theory of model averaging
436(4)
C.4.3 The theory applied to spatial regression models
440(4)
C.4.4 Model averaging for spatial regression models
444(6)
C.4.5 Applied illustrations
450(8)
C.4.6 Concluding remarks
458(1)
References
459(2)
C.5 Geographically Weighted Regression
David C. Wheeler
Antonio Paez
C.5.1 Introduction
461(1)
C.5.2 Estimation
462(5)
C.5.3 Issues
467(2)
C.5.4 Diagnostic tools
469(3)
C.5.5 Extensions
472(2)
C.5.6 Bayesian hierarchical models as an alternative to GWR
474(3)
C.5.7 Bladder cancer mortality example
477(7)
References
484(3)
C.6 Expansion Method, Dependency, and Multimodeling
Emilio Casetti
C.6.1 Introduction
487(1)
C.6.2 Expansion method
488(5)
C.6.3 Dependency
493(3)
C.6.4 Multimodeling
496(5)
C.6.5 Concluding remarks
501(1)
References
502(5)
C.7 Multilevel Modeling
S.V. Subramanian
C.7.1 Introduction
507(2)
C.7.2 Multilevel framework: A necessity for understanding ecological effects
509(1)
C.7.3 A typology of multilevel data structures
510(1)
C.7.4 The distinction between levels and variables
511(1)
C.7.5 Multilevel analysis
512(1)
C.7.6 Multilevel statistical models
513(8)
C.7.7 Exploiting the flexibility of multilevel models to incorporating 'realistic' complexity
521(2)
C.7.8 Concluding remarks
523(1)
References
524(5)
Part D: The Analysis of Remotely Sensed Data
D.1 ARTMAP Neural Network Multisensor Fusion Model for Multiscale Land Cover Characterization
Sucharita Gopal
Curtis E. Woodcock
Weiguo Liu
D.1.1 Background: Multiscale characterization of land cover
529(1)
D.1.2 Approaches for multiscale land cover characterization
530(2)
D.1.3 Research methodology and data
532(2)
D.1.4 Results and analysis
534(6)
D.1.5 Concluding remarks
540(1)
References
541(4)
D.2 Model Selection in Markov Random Fields for High Spatial Resolution Hyperspectral Data
Francesco Lagona
D.2.1 Introduction
545(4)
D.2.2 Restoration, segmentation and classification of HSRH images
549(1)
D.2.3 Adjacency selection in Markov random fields
550(4)
D.2.4 A study of adjacency selection from hyperspectral data
554(6)
D.2.5 Concluding remarks
560(1)
References
561(4)
D.3 Geographic Object-based Image Change Analysis
Douglas Stow
D.3.1 Introduction
565(1)
D.3.2 Purpose of GEOBICA
566(2)
D.3.3 Imagery and pre-processing requirements
568(1)
D.3.4 GEOBIA principles
569(2)
D.3.5 GEOBICA approaches
571(1)
D.3.6 GEOBICA strategies
572(3)
D.3.7 Post-processing
575(1)
D.3.8 Accuracy assessment
576(2)
D.3.9 Concluding remarks
578(1)
References
579(6)
Part E: Applications in Economic Sciences
E.1 The Impact of Human Capital on Regional Labor Productivity in Europe
Manfred M. Fischer
Monika Bartkowska
Aleksandra Riedl
Sascha Sardadvar
Andrea Kunnert
E.1.1 Introduction
585(1)
E.1.2 Framework and methodology
586(6)
E.1.3 Application of the methodology
592(3)
E.1.4 Concluding remarks
595(1)
References
596(3)
E.2 Income Distribution Dynamics and Cross-Region Convergence in Europe
Manfred M. Fischer
Peter Stumpner
E.2.1 Introduction
599(2)
E.2.2 The empirical framework
601(7)
E.2.3 Revealing empirics
608(14)
E.2.4 Concluding remarks
622(4)
References 623 Appendix
626(3)
E.3 A Multi-Equation Spatial Econometric Model, with Application to EU Manufacturing Productivity Growth
Bernard Fingleton
E.3.1 Introduction
629(1)
E.3.2 Theory
630(2)
E.3.3 Incorporating technical progress variations
632(5)
E.3.4 The econometric model
637(2)
E.3.5 Model restriction
639(3)
E.3.6 The final model
642(2)
E.3.7 Concluding remarks
644(3)
References 645 Appendix
647(6)
Part F: Applications in Environmental Sciences
F.1 A Fuzzy k-Means Classification and a Bayesian Approach for Spatial Prediction of Landslide Hazard
Pece V. Gorsevski
Paul E. Gessler
Piotr Jankowski
F.1.1 Introduction
653(2)
F.1.2 Overview of current prediction methods
655(3)
F.1.3 Modeling theory
658(8)
F.1.4 Application of the modeling approach
666(13)
F.1.5 Concluding remarks
679(1)
References
680(5)
F.2 Incorporating Spatial Autocorrelation in Species Distribution Models
Jennifer A. Miller
Janet Franklin
F.2.1 Introduction
685(2)
F.2.2 Data and methods
687(4)
F.2.3 Results
691(6)
F.2.4 Concluding remarks
697(2)
References
699(4)
F.3 A Web-based Environmental Decision Support System for Environmental Planning and Watershed Management
Ramanathan Sugumaran
James C. Meyer
Jim Davis
F.3.1 Introduction
703(1)
F.3.2 Study area
704(1)
F.3.3 Design and implementation of WEDSS
705(7)
F.3.4 The WEDSS in action
712(3)
F.3.5 Concluding remarks
715(1)
References
716(5)
Part G: Applications in Health Sciences
G.1 Spatio-Temporal Patterns of Viral Meningitis in Michigan, 1993-2001
Sharon K. Greene
Mark A. Schmidt
Mary Grace Stobierski
Mark L. Wilson
G.1.1 Introduction
721(2)
G.1.2 Materials and methods
723(2)
G.1.3 Results
725(5)
G.1.4 Concluding remarks
730(4)
References
734(3)
G.2 Space-Time Visualization and Analysis in the Cancer Atlas Viewer
Dunrie A. Greiling
Geoffrey M. Jacquez
Andrew M. Kaufmann
Robert G. Rommel
G.2.1 Introduction
737(2)
G.2.2 Data and methods
739(3)
G.2.3 Results
742(8)
G.2.4 Concluding remarks
750(1)
References
751(2)
G.3 Exposure Assessment in Environmental Epidemiology
Jaymie R. Meliker
Melissa J. Slotnick
Gillian A. AvRuskin
Andrew M Kaufmann
Geoffrey M Jacquez
Jerome O. Nriagu
G.3.1 Introduction
753(2)
G.3.2 Data and methods
755(2)
G.3.3 Features and architecture of Time-GIS
757(2)
G.3.4 Application
759(6)
G.3.5 Concluding remarks
765(1)
References
766(3)
List of Figures 769(10)
List of Tables 779(6)
Subject Index 785(8)
Author Index 793(12)
Contributing Authors 805