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Computational Approaches for Urban Environments

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Cover of 'Computational Approaches for Urban Environments'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Computational Approaches for Urban Environments: An Editorial
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    Chapter 2 From Fractal Urban Pattern Analysis to Fractal Urban Planning Concepts
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    Chapter 3 Knowledge Discovery in Spatial Planning Data: A Concept for Cluster Understanding
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    Chapter 4 Clustering Contextual Neural Gas: A New Approach for Spatial Planning and Analysis Tasks
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    Chapter 5 Hedonic House Price Modeling Based on Multilevel Structured Additive Regression
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    Chapter 6 Simple Agents, Complex Emergent City: Agent-Based Modeling of Intraurban Migration
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    Chapter 7 Quantifying Urban Diversity: Multiple Spatial Measures of Physical, Social, and Economic Characteristics
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    Chapter 8 Everyday Cycling in Urban Environments: Understanding Behaviors and Constraints in Space-Time
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    Chapter 9 Performance Improvements for Large-Scale Traffic Simulation in MATSim
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    Chapter 10 Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data
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    Chapter 11 Fusion of Airborne Hyperspectral and LiDAR Remote Sensing Data to Study the Thermal Characteristics of Urban Environments
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    Chapter 12 Modeling Urban Land Use Change: Integrating Remote Sensing with Socioeconomic Data
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    Chapter 13 Linked Activity Spaces: Embedding Social Networks in Urban Space
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    Chapter 14 Using Non-authoritative Sources During Emergencies in Urban Areas
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    Chapter 15 Towards a Comparative Science of Cities: Using Mobile Traffic Records in New York, London, and Hong Kong
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    Chapter 16 Epilogue
Attention for Chapter 1: Computational Approaches for Urban Environments: An Editorial
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Chapter title
Computational Approaches for Urban Environments: An Editorial
Chapter number 1
Book title
Computational Approaches for Urban Environments
Published by
Springer International Publishing, February 2016
DOI 10.1007/978-3-319-11469-9_1
Book ISBNs
978-3-31-911468-2, 978-3-31-911469-9

Marco Helbich, Jamal Jokar Arsanjani, Michael Leitner

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 50%
Student > Bachelor 1 17%
Other 1 17%
Student > Doctoral Student 1 17%
Readers by discipline Count As %
Environmental Science 1 17%
Business, Management and Accounting 1 17%
Agricultural and Biological Sciences 1 17%
Earth and Planetary Sciences 1 17%
Energy 1 17%
Other 0 0%
Unknown 1 17%