Spatial‐temporal specific neighbourhood rules for cellular automata land‐use modelling

Geertman, Stan; Hagoort, Michel; Ottens, Henk

doi:10.1080/13658810601064892

Cited by 34 publications

(20 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although PA has been useful in demonstrating the degree of agreement with observed reality, other metrics and indices could have been used. Landscape metrics (generally known as spatial metrics) such as number of patches, shape indices or relative distance indices, could also be applied to compare results by measuring fragmentation, dispersion or relative distance between simulated parcels (see McGarigal et al, 2002;Geertman et al, 2007). Landscape metrics can be particularly useful when comparing different simulations with similar PA values.…”

Section: Discussionmentioning

confidence: 99%

“…This metric is often used as an indicator of neighbourhood characteristics (see Verburg et al, 2004a;Verburg et al, 2004b;Geertman et al, 2007;Hansen, 2012;Pan et al, 2010;van Vliet et al, 2013) and to define the push-and-pull effect functions as part of the neighbourhood rules (Hagoort et al, 2008).…”

Section: Characterising the Neighbourhood Effect Using Spatial Metricsmentioning

confidence: 99%

“…Spatial metrics can be used to analyse land use patterns, spatial distribution and, in this case, the neighbourhood effect (Geertman et al, 2007). Verburg et al (2004a) proposed the enrichment factor as a generic metric to measure, quantify and understand neighbourhood characteristics in regular grid spaces (raster).…”

Section: Characterising the Neighbourhood Effect Using Spatial Metricsmentioning

confidence: 99%

“…The overall objective is to develop an approach able to model neighbourhood effect in irregular CA-based models. For this purpose, spatial metrics originally developed for raster environments, namely the enrichment factor (Verburg et al, 2004a) and the neighbourhood index (Hansen, 2012), were adapted and applied to measure the neighbourhood effect in irregular space (Geertman et al, 2007;Hagoort et al, 2008) in a past time period. These metrics were calculated enabling the generation of functions that reproduce the neighbourhood effect that pushes parcels to change their land use.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Configuring the neighbourhood effect in irregular cellular automata based models

González

Barros

2016

International Journal of Geographical Information Science

View full text Add to dashboard Cite

Abstract:Cellular automata (CA) models have been widely employed to simulate urban growth and land use change. In order to represent urban space more realistically, new approaches to CA models have explored the use of vector data instead of traditional regular grids. However, the use of irregular CA-based models brings new challenges as well as opportunities. The most strongly affected factor when using an irregular space is neighbourhood. Although neighbourhood definition in an irregular environment has been reported in the literature, the question of how to model the neighbourhood effect remains largely unexplored.In order to shed light on this question, this paper proposed the use of spatial metrics to characterise and measure the neighbourhood effect in irregular CA-based models. These metrics, originally developed for raster environments, namely the enrichment factor and the neighbourhood index, were adapted and applied in the irregular space employed by the model. Using the results of these metrics, distance-decay functions were calculated to reproduce the push-and-pull effect between the simulated land uses. The outcomes of a total of 55 simulations (five sets of different distance functions and eleven different neighbourhood definition distances) were compared with observed changes in the study area during the calibration period. Our results demonstrate that the proposed methodology improves the outcomes of the urban growth simulation model tested and could be applied to other irregular CA-based models.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Characterising the Neighbourhood Effect Using Spatial Metricsmentioning

confidence: 99%

Section: Characterising the Neighbourhood Effect Using Spatial Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Configuring the neighbourhood effect in irregular cellular automata based models

González

Barros

2016

International Journal of Geographical Information Science

View full text Add to dashboard Cite

show abstract

“…。城镇用地扩张作为土地利用动态演化研究的重要领域，具有系统性、开放性、动态性、非线性、层次性、随机性和不确定性等特点，是一个具有高度复杂性的过程 (周成虎等, 1999, 2009)。加强对城镇用地扩张机制的研究，有利于提高城市规划和土地利用规划的科学性，避免规划中主观性和随机性所带来的影响。随着研究的深入，大量辅助城镇用地规划决策的新方法与新理论不断涌现，利用约束性元胞自动机(constrained CA)，对城镇用地扩张进行动态模拟和情景分析，已成为新的研究热点 (黎夏等, 1999; 龙瀛等, 2009)。将元胞自动机模型与 GIS 技术等相结合，应用于城市土地利用动态演化研究中，能够弥补传统统计分析模型的不足，并提供具有时空特性的模拟结果。依据 CA 应用目的的不同，可以将其划分为约束性 CA、预报性 CA 和描述性 CA 等 3 种主要类型 (Geertman et al, 2007) [He C Y, Chen J, Shi P J, et al 2003. City expansion model of metropolitan area in China: A case study of Beijing [J].…”

unclassified

基于约束性ca的大都市郊区城镇增长的情景模拟与管控——以武汉市江夏区为例

王海军¹,

夏畅²,

张安琪³

et al. 2016

Progress in Geography

View full text Add to dashboard Cite

Land-Use Modelling in Planning Practice

Koomen¹,

Beurden²

2011

GeoJournal Library

View full text Add to dashboard Cite

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...

show abstract

Spatial‐temporal specific neighbourhood rules for cellular automata land‐use modelling

Cited by 34 publications

References 35 publications

Configuring the neighbourhood effect in irregular cellular automata based models

Configuring the neighbourhood effect in irregular cellular automata based models

基于约束性ca的大都市郊区城镇增长的情景模拟与管控——以武汉市江夏区为例

Land-Use Modelling in Planning Practice

Contact Info

Product

Resources

About