Land-Use Modelling in Planning Practice

Abstract

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) ForewordA generation or more ago when land use transport models were first being developed, the focus was on how different models compared with one another in technical and theoretical terms. There was a concern for dynamics, for interaction and for calibration and validation, but less so for how such models might be interfaced with wider planning processes and the stakeholders that operate such systems and are indeed influenced by the plans that emerged from them. The state of the art then consisted of comparative studies of cross-sectional comprehensive spatial interaction model applications catalogued, for example, in the ISGLUTI Project -the International Study Group on Land Use Transport Interaction -and reported in the book by Webster, Bly and Paulley (1988). The dominant focus was very much in terms of the technical performance of models rather than their use in planning or policy-making.As our experience of these models grew and evolved, this focus began to shift to the context in which models were best used. Onto the agenda came ideas about the various tools that had been developed to inform how we might best make good plans, and how these could be stitched together into coherent planning methods. Planning support systems in analogy to decision support in management were first formally suggested over 20 years ago by Britton Harris (1989) in his seminal article Beyond geographic information systems: computers and the planning professional as a way of bridging the development of computer models and tools with the activities of plan-making. Since then, a series of contributions to ways of building this bridge have been forged, the most recent being reported by Brail's (2008) in the collection of papers in his book Planning Support Systems for Cities and Regions. Many of these sketch the wider context and illustrate how a diversity of models and methods are coming together to define appropriate forums for dialogues between model builders, planners and the wider set of stakeholders involved in policy and its implementation.So far we do not have a detailed blow by blow account of building and applying models as part of planning support systems. Until now that is, because this book represents the first such chronology of how a suite of land-use modelling tools called LUMOS -Land Use MOdelling System -which is centred on the Land Use Scanner model with another model Environment Explorer being sometimes v vi Foreword used in parallel, is being fashioned to examine a wide array of different planning issues ranging...

Spatio-temporal analysis of land use/land cover pattern changes in Arasbaran Biosphere Reserve: Iran

“…Land use change models are increasingly used for prediction and analysis of LULC and its consequent impacts (Koomen and Beurden 2011;Lambin et al 2001) in decision-making and for planning support systems aiming at sustainable land use and development patterns.…”

“…: (Heistermann et al 2006;Koomen and Beurden 2011;Verburg et al 2004). There are several conceptual approaches to land use change modelling.…”

“…There are several conceptual approaches to land use change modelling. These include: models based on Cellular Automata, or those using Statistical Analysis for modelling, models using Markov chain approach, or Artificial Neural Network and finally those of Agent Based Modelling (Koomen and Beurden 2011;Subedi et al 2013). Integration of Markov chains and Cellular Automata approaches to modelling (C-A Markov model) is used in advantage for this case study in order to predict the future LULC of Arasbaran BR.…”

“…Markov chains are stochastic processes (Balzter 2000) and uses the matrices that show changes between land-use categories (based on the basic core principle of continuation of historical development) (Koomen and Beurden 2011) and widely used in modeling and simulating changes and trends of LULC (Balzter 2000;Coppedge et al 2007;Hathout 2002;Luo et al 2008;Muller and Middleton 1994;Weng 2002). The homogeneous Markov model for prediction of land use change can be mathematically presented as (Subedi et al 2013):…”

“…And P m j¼1 P ij ¼ 1(i, j = 1, 1, 2,…, m) is the transition probability matrix in a given state. A disadvantage of this type of analysis is that it is not spatial, requiring additional assumptions to allocate spatial characteristics to LULC types (Araya and Cabral 2010;Koomen and Beurden 2011).…”