Geospatial Analysis and Modelling of Urban Structure and Dynamics
The increasingly urbanized world has created various problems of environment, climate, consumption of resources, and public health, which are closely linked to the side-effects of urbanization such as sprawl, congestion, housing affordability and loss of
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The GeoJournal Library Volume 99 Managing Editor:
Daniel Z. Sui, College Station, USA Founding Series Editor:
Wolf Tietze, Helmstedt, Germany Editorial Board: Paul Claval, France
Yehuda Gradus, Israel Sam Ock Park, South Korea Herman van der Wusten, The Netherlands
For further volumes: http://www.springer.com/series/6007
Bin Jiang
l
Xiaobai Yao
Editors
Geospatial Analysis and Modelling of Urban Structure and Dynamics
Foreword by Michael Batty
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Editors Bin Jiang University of Ga¨vle Department of Technology and Built Environment Division of Geomatics SE-801 76 Ga¨vle Sweden [email protected]
Xiaobai Yao Department of Geography University of Georgia Athens GA 30602 Room 204, GG Bldg. USA [email protected]
ISSN 0924-5499 ISBN 978-90-481-8571-9 e-ISBN 978-90-481-8572-6 DOI 10.1007/978-90-481-8572-6 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2010922459 # Springer ScienceþBusiness Media B.V. 2010 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
A Coming of Age: Geospatial Analysis and Modelling in the Early Twenty First Century Forty years ago when spatial analysis first emerged as a distinct theme within geography’s quantitative revolution, the focus was largely on consistent methods for measuring spatial correlation. The concept of spatial autocorrelation took pride of place, mirroring concerns in time-series analysis about similar kinds of dependence known to distort the standard probability theory used to derive appropriate statistics. Early applications of spatial correlation tended to reflect geographical patterns expressed as points. The perspective taken on such analytical thinking was founded on induction, the search for pattern in data with a view to suggesting appropriate hypotheses which could subsequently be tested. In parallel but using very different techniques came the development of a more deductive style of analysis based on modelling and thence simulation. Here the focus was on translating prior theory into forms for generating testable predictions whose outcomes could be compared with observations about some system or phenomenon of interest. In the intervening years, spatial analysis has broadened to embrace both inductive and deductive approaches, often combining both in different mixes for the variety of problems to which it is now applied. Moreover, the focus has become more explicitly geographical although the term spatial still has a wider usage for many of the statistics and models that form the arsenal of techniques in this area are applicable to spatial systems other than the obvi
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