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Geographical Models with Mathematica.

Author: Andre Dauphine
Publisher: San Diego : Elsevier Science, 2017.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Geographical Models with Mathematica provides a fairly comprehensive overview of the types of models necessary for the development of new geographical knowledge, including stochastic models, models for data analysis, for geostatistics, for networks, for dynamic systems, for cellular automata and for multi-agent systems, all discussed in their theoretical context. The author then provides over 65 programs, written in  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Dauphine, Andre.
Geographical Models with Mathematica.
San Diego : Elsevier Science, ©2017
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Andre Dauphine
ISBN: 9780081022306 0081022301
OCLC Number: 973188123
Notes: 9.2. Modeling complex spatial structures: machine learning and choremes.
Description: 1 online resource (316 pages)
Contents: Front Cover ; Geographical Models with Mathematica; Copyright; Contents; Introduction; I.1. The scientific practice of the geographer; I.2. The three forms of geography projects; I.3. Plan of the work; I.4. How should this work be read?; I.5. Appendix 1: a general modeling language Mathematica; PART 1: Modeling the Relationships between Societies and Nature; 1. The Theoretical Context of Classical Geography; 1.1. Environmentalism --
a theory that is still rejected; 1.2. The theoretical double paradox of classical geography; 1.3. The general theory of systems and the theories derived therefrom. 1.4. Conclusion1.5. Appendix 2: Importing data within Mathematica; 2. Statistical and Probability Models for Given Relationships Between Societies and the Natural Environment; 2.1. Acknowledging the probability model for recorded data; 2.2. Modeling the relationships between two or several variables; 2.3. Temporalities and time series models; 2.4. Conclusion; 2.5. Appendix 3: chronological program processing; 3. Models of Ordinary Dynamic Systems; 3.1. Four lines of questioning to understand the behavior of a dynamic system; 3.2. Initiation in the modeling of dynamic systems. 3.3. Assets and restrictions of ODE models3.4. More realistic models of geographical systems; 3.5. Conclusion; 3.6. Appendix 4: crowd behavior in catastrophic situations; PART 2: Modeling Geographic Locations; 4. Theories of Geographical Locations; 4.1. Introduction to spatial economic theories; 4.2. A new urban economy and a new economic geography; 4.3. Conclusion; 5. Theoretical Geolocation Models; 5.1. Von Thünen and d'Alonso's monocentric and polycentric models; 5.2. Steiner's model generalizes Weber's; 5.3. Central place models in the making; 5.4. Conclusion. PART 3: Spatial Structures and Territorial Dynamics6. Theories Used to Understand Territorial Structures and Dynamics; 6.1. From terrestrial to geographical space ; 6.2. Some theories drawn from various fields and used to explain simple territorial forms; 6.3. From morphology to morphogenesis; 6.4. An overview of morphogenetic theories; 6.5. Conclusion; 6.6. Appendix 5: globalization at the root of a paradox: homogenization and global fracturing; 7. Models of Basic Structures: Points and Fields; 7.1. Modeling the point structures of a geographical space; 7.2. Modeling geographical fields. 7.3. Conclusion7.4. Appendix 6: Introduction to the morphometric analysis of the Grenoble Alps; 8. Models of Basic Structures: Networks; 8.1. The two aspects of a network: graphs and matrices; 8.2. Modeling the structure of a spatial network; 8.3. Qualitative geographical models and graph theory; 8.4. Modeling network dynamics; 8.5. Conclusion; 8.6. Appendix 7: A geometric approach to the network of French metropolises; 9. Geographical Space as a Mixture of Basic Spatial Structures; 9.1. Testing links between two elementary spatial structures.

Abstract:

Geographical Models with Mathematica provides a fairly comprehensive overview of the types of models necessary for the development of new geographical knowledge, including stochastic models, models for data analysis, for geostatistics, for networks, for dynamic systems, for cellular automata and for multi-agent systems, all discussed in their theoretical context. The author then provides over 65 programs, written in the Mathematica language, that formalize these models. Case studies are provided to help the reader apply these programs to their own studies.

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