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Trends in Spatial Analysis and Modelling

Overview of attention for book
Cover of 'Trends in Spatial Analysis and Modelling'

Table of Contents

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    Book Overview
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    Chapter 1 Reverse Engineering of Land Cover Data: Machine Learning for Data Replication in the Spatial and Temporal Domains
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    Chapter 2 Geospatial Analysis Requires a Different Way of Thinking: The Problem of Spatial Heterogeneity
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    Chapter 3 A Survey on Spatiotemporal and Semantic Data Mining
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    Chapter 4 Contribution Towards Smart Cities: Exploring Block Level Census Data for the Characterization of Change in Lisbon
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    Chapter 5 The Application of the SPAWNN Toolkit to the Socioeconomic Analysis of Chicago, Illinois
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    Chapter 6 The Evolution of the Land Development Industry: An Agent-Based Simulation Model
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    Chapter 7 Dynamic Relationships Between Human Decision Making and Socio-natural Systems
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    Chapter 8 Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons
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    Chapter 9 Applications of 3D City Models for a Better Understanding of the Built Environment
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    Chapter 10 An Automatic Approach for Generalization of Land-Cover Data from Topographic Data
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    Chapter 11 Epilogue
Attention for Chapter 1: Reverse Engineering of Land Cover Data: Machine Learning for Data Replication in the Spatial and Temporal Domains
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Citations

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Chapter title
Reverse Engineering of Land Cover Data: Machine Learning for Data Replication in the Spatial and Temporal Domains
Chapter number 1
Book title
Trends in Spatial Analysis and Modelling
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-52522-8_1
Book ISBNs
978-3-31-952520-4, 978-3-31-952522-8
Authors

Galen Maclaurin, Stefan Leyk, Maclaurin, Galen, Leyk, Stefan

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Student > Master 1 50%
Readers by discipline Count As %
Earth and Planetary Sciences 2 100%

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