Spatial Network Data Concepts and Techniques for Summarization
This brief explores two of the main challenges of spatial network data analysis: the many connected components in the spatial network and the many candidates that have to be processed. Within this book, these challenges are conceptualized, well-defined pr
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Dev Oliver
Spatial Network Data Concepts and Techniques for Summarization
123
SpringerBriefs in Computer Science Series editors Stan Zdonik Shashi Shekhar Jonathan Katz Xindong Wu Lakhmi C. Jain David Padua Xuemin (Sherman) Shen Borko Furht V.S. Subrahmanian Martial Hebert Katsushi Ikeuchi Bruno Siciliano Sushil Jajodia Newton Lee
More information about this series at http://www.springer.com/series/10028
Dev Oliver
Spatial Network Data Concepts and Techniques for Summarization
123
Dev Oliver ESRI Redlands, CA USA
ISSN 2191-5768 ISSN 2191-5776 (electronic) SpringerBriefs in Computer Science ISBN 978-3-319-39620-0 ISBN 978-3-319-39621-7 (eBook) DOI 10.1007/978-3-319-39621-7 Library of Congress Control Number: 2016941312 © The Author(s) 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
To Datra and Drew
Preface
Our daily lives often revolve around spatial networks such as transportation networks and utilities. Summarizing the activities that occur on these networks is of interest to professionals, organizations, and researchers in many domains including transportation safety, public safety, public health, and disaster response. For example, transportation planners and engineers may wish to identify road segments that pose risks for pedestrians and require redesign whereas law enforcement officials may desire to know which streets have increased crime activity in order to guide resource allocation decisions. The process of summarizing spatial network data entails finding a compact description or representation of observations or activities on large spatial or spatiotemporal networks. However, summarizing spatial network data can be computationally challenging for various reasons, depending on the domain. This brief explores two of the main challenges: (1) the many connected components in the spatial network an
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