Spatial externalities, neighbourhood rules and CA land-use modelling

Hagoort, Michel; Geertman, Stan; Ottens, Henk

doi:10.1007/s00168-007-0140-8

Cited by 66 publications

(43 citation statements)

References 24 publications

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“…This process has indirectly revealed the appropriateness of the selected variables involved in the land use changes in the vineyards. To this end, the Cohen (Cohen's Kappa) concordance index was used with success for its simplicity and clarity of interpretation (Carletta 1996;Hagoort et al 2008;Pontius 2000) through which the two compared maps have revealed a fair degree of conformity from both quantitative (number of pixels in the vineyard) and spatial points of view (i.e., checking the exact location of each pixel in both maps): two maps can have the same percentage of pixels for each class of land use, but different spatial allocations of these quantities. The study of spatial inconsistency is complex; it derives from a shift in position between pixels of different categories (Pontius and Schneider 2001).…”

Section: Validation Of Resultsmentioning

confidence: 99%

“…In the case of this work the rules of transition (refer to Introduction Section) of the geographic surfaces surrounding the study areas are considered. As stated by Hagoort et al (2008) the rules of transition consist of variables capable of driving the process of evolution of certain land uses which are determined based primarily on review of the literature and of interviews with industry experts. Furthermore, Hagoort et al (2008), in view of the possible influence of the territory on such variables, identify different types of functions relating to the variables on the basis of their distance from the land use in question.…”

Section: Areas Of Studymentioning

confidence: 99%

“…As stated by Hagoort et al (2008) the rules of transition consist of variables capable of driving the process of evolution of certain land uses which are determined based primarily on review of the literature and of interviews with industry experts. Furthermore, Hagoort et al (2008), in view of the possible influence of the territory on such variables, identify different types of functions relating to the variables on the basis of their distance from the land use in question. It is, however, an area difficult to determine, and difficulty increases exponentially according to the land use considered (Dear 1977).…”

Section: Areas Of Studymentioning

confidence: 99%

“…In addressing this issue in certain regions of the Netherlands, Hagoort et al (2008) have shown that regional and temporal calibration of neighborhood rules is a refinement in the application of CA-based land use models that pay off by increasing the validity of the model outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Use of cellular automata in the study of variables involved in land use changes

Riccioli

Asmar

et al. 2012

Environ Monit Assess

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The study of changes in land use has been included lately in territorial processes in order to optimize future management decisions. The different functions that the territory plays (production, aesthetic, and natural functions, etc.) make planning choices more difficult. This work focuses on the selection and combination of a set of indicators to analyze the variables through which the changes occur in the land used in viticulture for wine production. The proposed approach makes use of the Geographic Information System (GIS) in the development of a map of land use scenario. It is applied to a case study through a model involving cellular automata (CA) implemented with maps of suitability for viticulture and Markov chains. The use in this case of the CA is aimed at validating the scenario map in order to deduce the variables and the orientations of the farmers in the field of wine production.

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Section: Validation Of Resultsmentioning

confidence: 99%

Section: Areas Of Studymentioning

confidence: 99%

Section: Areas Of Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of cellular automata in the study of variables involved in land use changes

Riccioli

Asmar

et al. 2012

Environ Monit Assess

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“…Then CA model provides transition/reclassification rules depending on the previous state and neighbourhood pixels of the current pixel under study. CA-Markov model evaluates multiple criteria and predicts the future change using probability matrices and reclassification rules [17,18,19]. The current study aims to assess landscape dynamics in the protected areas of Southern Karnataka.…”

Section: Introductionmentioning

confidence: 99%

Visualization and Prediction of Landscape Dynamics in the Protected Areas of Karnataka

Setturu¹

2017

IJRET

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A spatial simulation model for karst rocky desertification combining top‐down and bottom‐up approaches

Zhang

2018

Land Degrad Dev

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Karst rocky desertification (KRD) is a serious ecological and environmental issue threatening southwestern China. The evolution of KRD calls for predicting its potential expansion or contraction, to support policy‐making for combating KRD. A spatial model named ‘SDKRD’ for simulating KRD dynamics was developed in this study. The novelty of it is the ability to explore spatially local KRD evolution and integrate this information into a global estimation. The SDKRD model consists of three modules: a global KRD forecast and scenario setting, a local exploration of spatially varying effects of driving forces and neighbourhood influence on KRD evolution, and an iteration module for combining results of aforementioned modules. The SDKRD model was tested in the Changshun County as a case‐study. The accuracy assessment confirmed a spatially visual consistency and a robust statistical result. Spatial KRD maps from 2010 to 2030 were simulated under three scenarios. The historical extrapolation scenario found a spatially global KRD reversion and locally scattered KRD deterioration. Different KRD restoration strategies under the other two scenarios could reverse the transformation of KRD classes at different magnitudes. Intermediate classes in the KRD evolution process could be more easily reversed. Under three scenarios with the increased strength of KRD management measures, areas of no KRD increased by 8.7%, 13.5%, and 16.6%, and areas of moderate KRD decreased by 38.3%, 53.0%, and 53.9%, respectively. SDKRD model presents specific KRD evolution trajectories and visualizes the KRD transformation at each location, which can help find cost‐effective ways of combating KRD with differentiated strategies.

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Spatial externalities, neighbourhood rules and CA land-use modelling

Abstract: R14,

Cited by 66 publications

References 24 publications

Use of cellular automata in the study of variables involved in land use changes

Use of cellular automata in the study of variables involved in land use changes

Visualization and Prediction of Landscape Dynamics in the Protected Areas of Karnataka

A spatial simulation model for karst rocky desertification combining top‐down and bottom‐up approaches

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