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Spatial Data Handling in Big Data Era

Overview of attention for book
Cover of 'Spatial Data Handling in Big Data Era'

Table of Contents

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    Book Overview
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    Chapter 1 Using T-Drive and BerlinMod in Parallel SECONDO for Performance Evaluation of Geospatial Big Data Processing
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    Chapter 2 Integrated Geo-information Database for Geological Disposal of High-Level Radioactive Waste in China
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    Chapter 3 Analyzing the Uncertainties of Ground Validation for Remote Sensing Land Cover Mapping in the Era of Big Geographic Data
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    Chapter 4 Error in Spatial Ecology (PVM)
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    Chapter 5 A Framework for Event Information Extraction from Chinese News Online
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    Chapter 6 Evaluating Neighborhood Environment and Utilitarian Walking Behavior with Big Data: A Case Study in Tokyo Metropolitan Area
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    Chapter 7 A Space-Time GIS for Visualizing and Analyzing Clusters in Large Tracking Datasets
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    Chapter 8 An Extended Community Detection Algorithm to Compare Human Mobility Flow Based on Urban Polycentric Cluster Boundaries: A Case Study of Shenzhen City
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    Chapter 9 Improving GIScience Visualization: Ideas for a New Methodology
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    Chapter 10 Leveraging Big (Geo) Data with (Geo) Visual Analytics: Place as the Next Frontier
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    Chapter 11 A Comparative Study of Various Properties to Measure the Road Hierarchy in Road Networks
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    Chapter 12 Detail Resolution: A New Model to Describe Level of Detail Information of Vector Line Data
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    Chapter 13 Urban Growth Evaluation: A New Approach Using Neighborhood Characteristics of Remotely Sensed Land Use Data
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    Chapter 14 Influential Factors of Building Footprint Location and Prediction of Office Shape in City Blocks in Tokyo’s Commercial Zones
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    Chapter 15 Modelling Urban Growth Evolution Using SLEUTH Model: A Case Study in Wuhan City, China
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Citations

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Title
Spatial Data Handling in Big Data Era
Published by
Springer Singapore, January 2017
DOI 10.1007/978-981-10-4424-3
ISBNs
978-9-81-104424-3, 978-9-81-104423-6
Editors

Chenghu Zhou, Fenzhen Su, Francis Harvey, Jun Xu

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters 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 33 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Colombia 1 3%
Unknown 32 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Researcher 4 12%
Student > Master 3 9%
Student > Doctoral Student 3 9%
Other 2 6%
Other 5 15%
Unknown 9 27%
Readers by discipline Count As %
Environmental Science 7 21%
Social Sciences 5 15%
Computer Science 4 12%
Arts and Humanities 2 6%
Earth and Planetary Sciences 2 6%
Other 3 9%
Unknown 10 30%