Temporal GIS: Advanced Functions for Field-Based Applications

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Springer Science & Business Media, 11 янв. 2002 г. - Всего страниц: 219
Trustonlymovement. Life happens at the level of events not of words. Trust movement. A. Adler As its title suggests, the main goal of this book is the development of advanced fu- tions for field-based Temporal Geographical Information Systems (TGIS).These fields may describe a variety of natural, epidemiological, economical, and social phen- ena distributed across space and time.Within such a framework, the book makes an attempt to establish links between, (a) the currently conceived TGIS techniques, and (b) the Bayesian maximum entropy (BME) techniques of Modern Spatiotemporal G- statistics.This link could be vital for offering significant improvements in the advanced functions of TGIS analysis and modelling, as well as generating useful information in a variety of real-world decision making and planning situations. To achieve the above goals, the eight Chapters of the book are organized around four main themes: Concepts, mathematical tools, computer programs, and applications. In fact, the focus is mainly on the step-by-step implementation of the compu- tional BME approach and the extensive use of illustrative examples and real-world applications.Indeed, because of the applied character of the present book, no detailed theoretical explanations or mathematical derivations are included.Instead, the reader is referred to the earlier book by Christakos (Modern Spatiotemporal Geostatistics, Oxford Univ.Press, New York, N.Y., 2000) for a comprehensive presentation of these BME aspects.With this in mind, the chapter-by-chapter organization of the book is described next.
 

Содержание

A BME View to the New Realities of TGIS
1
112 Synthesis Organization and Visualization
2
113 ActionOriented
3
12 FieldBased TGIS
4
13 TGIS Functions
8
14 Novel Contribution to TGIS
10
141 BMEBased Advanced Functions
11
143 BMElib Software
12
636 NonBayesian Analysis
119
64 Quantifying the Mapping Efficiency of Soft Data
120
65 Numerical Investigations of Popular Techniques
122
652 The Inadequacy of Indicator Kriging
131
66 Merging Maps with BME
138
67 Synopsis
141
The BME Computer Library
143
72 Getting Started
144

145 Scientific Hypothesis Testing and Explanation
13
15 Concluding Remarks
14
Spatiotemporal Modelling
17
22 The Random Field Model
24
23 The Role of Metaphors in TGIS
27
24 The Importance of Physical Geometry
28
25 Synopsis
32
Knowledge Bases Synthesis
33
32 General KB and the Associated Physical Constraints
35
321 SpaceTime Correlation Functions Between Two or More Points MultiplePoint Statistics
36
322 Physical Models
41
33 Specificatory KB
44
331 Hard and Soft Data
45
332 The Effect of Soft Data on the Calculation of the SpaceTime Correlation Functions
50
34 Accommodating Knowledge Needs
51
Spatiotemporal Mapping
53
42 Formal BME Analysis and Mapping
56
421 The Basic BME Procedure
57
422 The Advantage of Composite SpaceTime Mapping
59
423 ContinuousValued Map Reconstruction
63
424 Modifications of the BME Procedure
64
425 Spatiotemporal Filtering
66
426 Spatiotemporal Mapping and ChangeofScale Procedures
68
43 Other Mapping Techniques
73
432 Geostatistical Kriging and Neural Networks
74
433 KalmanBucy Filtering
76
434 Some Comparisons
77
44 Concluding Remarks
81
Interpretive BME
83
52 An Epistemic Analysis of the BME Approach
84
53 NonBayesian Conditionalization
87
531 Material Biconditionalization
88
532 Material Conditionalization
94
54 By Way of a Summary
95
The BME Toolbox in Action
97
62 StepbyStep BME
98
622 The Diagrammatic Representation
100
63 Analytical and Numerical CaseStudies
103
632 Spatiotemporal Filtering
104
633 Exogenous Information
105
634 Physical Laws
110
635 Using Soft Data to Improve TGIS Mapping
113
723 Getting Started with BMELib
145
73 The iolib Directory
149
731 The readGeoEASm and writeGeoEASm Functions
150
732 The readProbam and writeProbam Functions
151
733 The readBMEprobam and writeBMEprobam Functions
153
742 The colorplotm Function
154
744 The valplotm Function
155
75 The modelslib Directory
156
752 The modelplotm Function
158
753 A Tutorial Use of the modelslib Directory
159
761 The kerneldensitym Function
161
763 The covariom Function
162
764 The crosscovariom Function
163
766 A Tutorial Use of the statlib Directory
164
771 The probam Functions
165
773 The BMEprobaModem Function
170
775 The BMEprobaClm Function
171
776 The BMEprobaTModem BMEprobaTPdfm and BMEprobaTCIm Functions
172
777 Working with Files
173
778 A Tutorial Use of the bmeprobalib Directory
175
781 The BMEintervalModenl Function
176
783 The BMEintervalTModem Function
177
784 The BMEintervalTPdfm Function
178
785 A Tutorial Use of the bmeintlib Directory
179
792 The krigingfilterm Function
180
793 A Tutorial Use of the bmehrlib Directory
181
7102 The simuseqm Function
182
7103 A Tutorial Use of the simulib Directory
183
7112 The iso2anisom Function
184
7114 The coord2Km Function
185
7116 A Tutorial Use of the genlib Directory
186
7121 The mvnlibcompilem Function
187
7133 The testslib Directory
188
Scientific Hypothesis Testing Explanation and Decision Making
189
82 Hypothesis Testing
193
83 Scientific Explanation
197
84 Geographotemporal Decision Making
200
85 Prelude
205
References
209
Index
215
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