Temporal GIS : advanced functions for field-based applications
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Bibliographic Information
Temporal GIS : advanced functions for field-based applications
Springer-Verlag, c2001
- : softcover
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Softcover without CD-ROM
Includes bibliographical references (p. [209]-213) and index
Description and Table of Contents
Description
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.
Table of Contents
1 A BME View to the New Realities of TGIS.- 1.1 Introducing a Temporal Geographical Information System (TGIS).- 1.1.1 Purposefulness, Content, and Context.- 1.1.2 Synthesis, Organization, and Visualization.- 1.1.3 Action-Oriented.- 1.2 Field-Based TGIS.- 1.3 TGIS Functions.- 1.4 Novel Contribution to TGIS.- 1.4.1 BME-Based Advanced Functions.- 1.4.2 Stochastic Modelling.- 1.4.3 BMEIib Software.- 1.4.4 Epistemic Viewpoint.- 1.4.5 Scientific Hypothesis Testing and Explanation.- 1.4.6 Revisionistic Paradigm.- 1.5 Concluding Remarks.- 2 Spatiotemporal Modelling.- 2.1 Spatiotemporal Continuum.- 2.2 The Random Field Model.- 2.3 The Role of Metaphors in TGIS.- 2.4 The Importance of Physical Geometry.- 2.5 Synopsis.- 3 Knowledge Bases Integration.- 3.1 Integrating Knowledge Bases (KB) into TGIS.- 3.2 General KB and the Associated Physical Constraints.- 3.2.1 Space/Time Correlation Functions Between Two or More Points (Multiple-Point Statistics).- 3.2.2 Physical Models.- 3.3 Specificatory KB.- 3.3.1 Hard and Soft Data.- 3.3.2 The Effect of Soft Data on The Calculation of the Space/Time Correlation Functions.- 3.4 Accommodating Knowledge Needs.- 3.4.1 Knowledge Classification.- 3.4.2 Model Building and Reality Check.- 4 Spatiotemporal Mapping.- 4.1 A Formulation of the Spatiotemporal Mapping Problem.- 4.2 Formal BME Analysis and Mapping.- 4.2.1 The Basic BME Procedure.- 4.2.2 The Advantage of Composite Space/Time Mapping.- 4.2.3 Continuous-Valued Map Reconstruction.- 4.2.4 Modifications of the BME Procedure.- 4.2.5 Spatiotemporal Filtering.- 4.2.6 Spatiotemporal Mapping and Change-of-Scale Procedures.- 4.3 Other Mapping Techniques.- 4.3.1 Wiener-Kolmogorov Stochastic Interpolation.- 4.3.2 Geostatistical Kriging.- 4.3.3 Kalman-Bucy Filtering.- 4.3.4 Some Comparisons.- 4.4 Concluding Remarks.- 5 Interpretive BME.- 5.1 Interpretive Issues.- 5.2 An Epistemic Analysis of the BME Approach.- 5.3 Non-Bayesian Conditionalization.- 5.3.1 Material Biconditionalization.- 5.3.2 Material Conditionalization.- 5.4 By Way of a Summary.- 6 The BME Toolbox In Action.- 6.1 The Fundamental KB Operators.- 6.2 Step-by-Step BME.- 6.2.1 The Formal Representation.- 6.2.2 The Diagrammatic Representation.- 6.3 Analytic and Synthetic Case-Studies.- 6.3.1 Some Commonly Encountered Situations.- 6.3.2 Spatiotemporal Filtering.- 6.3.3 Exogenous Information.- 6.3.4 Physical Laws.- 6.3.5 Using Soft Data to Improve TGIS Mapping.- 6.3.6 Non-Bayesian Analysis.- 6.4 Quantifying the Mapping Efficiency of Soft Data.- 6.5 Numerical Investigations of Popular Techniques.- 6.5.1 The Use and Misuse of Soft Data by Statistical Regression-Based Techniques.- 6.5.2 The Inadequacy of Indicator Kriging.- 6.6 Merging Maps with BME.- 6.7 Synopsis.- 7 The BME Computer Library.- 7.1 Computational BME Analysis and the BMEIib.- 7.2 Getting Started.- 7.2.1 Notational Convenience.- 7.2.2 Getting Started with MatLab.- 7.2.3 Getting Started with BMEIib.- 7.3 The iolib Directory.- 7.3.1 The readGeoEAS.m and writeGeoEAS.m Functions.- 7.3.2 The readProba.m and writeProba.m Functions.- 7.3.3 The readBMEproba.m and writeBMEproba.m Functions.- 7.4 The graphlib Directory.- 7.4.1 The scatterplot.m function.- 7.4.2 The colorplot.m function.- 7.4.3 The marketplot.m function.- 7.4.4 The valplot.m function.- 7.4.5 A tutorial Use of the graphlib Directory.- 7.5 The modelslib Directory.- 7.5.1 The *C.m and *V.m Functions.- 7.5.2 The modelplot.m Function.- 7.5.3 A Tutorial Use of the modelslib Directory.- 7.6 The statlib Directory.- 7.6.1 The kerneldensity.m Function.- 7.6.2 The pdf2cdfm Function.- 7.6.3 The covario.m Function.- 7.6.4 The crosscovario.m Function.- 7.6.5 The crosscovarioST.m Function.- 7.6.6 A Tutorial Use of the statlib Directory.- 7.7 The bmeprobalib Directory.- 7.7.1 The proba*.m Function.- 7.7.2 The BMEprobaMoments.m Function.- 7.7.3 The BMEprobaMode.m Function.- 7.7.4 The BMEprobaPdfm Function.- 7.7.5 The BMEprobaCI.m Function.- 7.7.6 The BMEprobaTMode.m, BMEprobaTPdfm and BMEprobaTCI.m Functions.- 7.7.7 Working With Files.- 7.7.8 A Tutorial Use of the bmeprobalib Directory.- 7.8 The bmeintlib Directory.- 7.8.1 The BMEintervalMode.m Function.- 7.8.2 The BMEintervalPdf.m Function.- 7.8.3 The BMEintervalTMode.m Function.- 7.8.4 The BMEintervalTPdfm Function.- 7.8.5 A Tutorial Use of the bmeintlib Directory.- 7.9 The bmehrlib Directory.- 7.9.1 The kriging.m Function.- 7.9.2 The krigingfilter.m Function.- 7.9.3 A Tutorial Use of the bmehrlib Directory.- 7.10 Simulations.- 7.10.1 The simuchol.m Function.- 7.10.2 The simuseq.m Function.- 7.10.3 A Tutorial Use of the simulib Directory.- 7.11 The genlib Directory.- 7.11.1 The aniso2iso.m Function.- 7.11.2 The iso2aniso.m Function.- 7.11.3 The coord2dist.m Function.- 7.11.4 The coord2K.m Function.- 7.11.5 The kernelsmoothing.m Function.- 7.11.6 A Tutorial Use of the genlib Directory.- 7.12 The mvnlib Directory.- 7.12.1 The mvnlibcompile.m Function.- 7.12.2 Testing the mvnlib Directory.- 7.13 BMEIib Tutorials, Examples, and Tests.- 7.13.1 The tutorlib Directory.- 7.13.2 The exlib Directory.- 7.13.3 The testslib Directory.- 8 Scientific Hypothesis Testing, Explanation, and Decision Making.- 8.1 On Scientific Methodology.- 8.2 Hypothesis Testing.- 8.3 Scientific Explanation.- 8.4 Geographotemporal Decision Making.- 8.5 Prelude.- References.
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