Najat Qader - Academia.edu (original) (raw)

Papers by Najat Qader

International Journal of Geographical Information Science, 2012

In recent years, agent-based models (ABMs) have become a prevalent approach for modelling complex... more In recent years, agent-based models (ABMs) have become a prevalent approach for modelling complex urban systems. As a class of bottom-up method, ABMs are capable of simulating the decision-making as well as the multiple interactions of autonomous agents and between agents and the environment. The definition of agents' behaviour is a vital issue in implementing ABMs to simulate urban dynamics. Urban economic theory has provided effective ways to cope with this problem. This theory argues that the formation of urban spatial structure is an endogenous process resulting from the interactions among individual actors that are spatially distributed. However, this theory is used to explain urban phenomena regardless of spatial heterogeneity in most cases. This study combines GIS, ABM and urban economic models to simulate complex urban residential dynamics. The time-extended model is incorporated into an ABM so as to define agents' behaviour on a solid theoretical basis. A spatial variable is defined to address the neighbourhood effect by considering spatial heterogeneity. The proposed model is first verified by the simulation of three scenarios using hypothetical data: (1) single dominated preference; (2) varying preferences on the basis of income level; and (3) spatially heterogeneous environment. Then the model is implemented by simulating the residential dynamics in Guangzhou, China.

This paper studies the development of GIS from a new point of view, which both from the accumulat... more This paper studies the development of GIS from a new point of view, which both from the accumulation of GIS scientific articles and the evolution process of GIS using co-citation analysis. After 10 years' slow development from 1977, the number of GIS scientific literature began to increase rapidly since the early 1990s. By using the newly developed information visualization technology, the authors make Author Co-citation Analysis (ACA) and Document Co-citation Analysis (DCA) for 12,417 GIS scientific articles indexed in SCI-Expanded, SSCI, A&HCI. The result reveals the evolution process of GIS from intellectual base to several application fronts: hydrology, land use planning, landslide hazard, landscape ecology, when these applications in engineering areas also accelerate development of GIS. The pivotal authors and literature in the evolution process of GIS from intellectual base to applied fronts are also detected by the visualization of ACA and DCA of GIS scientific literature.

Agriculture Ecosystems & Environment, 2001

Scientists need a better and larger set of tools to validate land-use change models, because it i... more Scientists need a better and larger set of tools to validate land-use change models, because it is essential to know a model's prediction accuracy. This paper describes how to use the relative operating characteristic (ROC) as a quantitative measurement to validate a land-cover change model. Typically, a crucial component of a spatially explicit simulation model of land-cover change is a map of suitability for land-cover change, for example a map of probability of deforestation. The model usually selects locations for new land-cover change at locations that have relatively high suitability. The ROC can compare a map of actual change to maps of modeled suitability for land-cover change. ROC is a summary statistic derived from several two-by-two contingency tables, where each contingency table corresponds to a different simulated scenario of future land-cover change. The categories in each contingency table are actual change and actual non-change versus simulated change and simulated non-change. This paper applies the theoretical concepts to a model of deforestation in the Ipswich watershed, USA.

Annals of The Association of American Geographers, 2011

In geographical analysis, spatial simulation and optimization are usually separate processes tack... more In geographical analysis, spatial simulation and optimization are usually separate processes tackling different problems. It is, however, increasingly necessary to integrate them. Particularly in a fast developing area, the development to be simulated is seldom inertial (i.e., strictly following the historical trend); instead, it is likely to be interfered by new planning measures. Meanwhile, in such an area an optimization plan might not be even meaningful if it only addresses a snapshot of a highly dynamic landscape. In this study, we explored the possibility of integrating cellular automata (CA), a widely used method for simulating urban development and land use changes, and ant colony optimization (ACO), an advanced technique for solving complex path optimization problems. We named the resulting integrated system the geographical simulation and optimization system (GeoSOS) and applied it to a case study concerning finding the optimal path for a planned expressway in Dongguan, a fast-growing city in one of the most economically active regions of China. In the case study, the CA component of the GeoSOS generated simulations of the industrial land use changes for some years in the next decade. The ACO component of the GeoSOS, which had been revised from the conventional ACO to work on raster surfaces, took the simulations as input and completed raster-based path optimizations. In terms of the cumulative utility, a measurement used to evaluate the performance of the optimization, the coupling method surpasses the noncoupling method by 10.3 percent. En análisis geográfico la simulación y optimización espaciales usualmente son procesos separados que abordan problemas diferentes. Sin embargo, cada vez se hace más necesario integrarlos. En particular en una región que se desarrolle con rapidez, el desarrollo que se deba simular rara vez sigue la inercia (o sea, que siga estrictamente la tendencia histórica); en vez de eso, lo más seguro es que sea interferido por nuevas medidas de planificación. Mientras tanto, en tal tipo de área un plan de optimización podría no ser siquiera significativo si apenas cubriera una fracción de un paisaje altamente dinámico. En este estudio exploramos la posibilidad de integrar autómata celular (AC), un método ampliamente utilizado para simular desarrollo urbano y cambios en el uso del suelo, y optimización de hormiguero (ACO), una técnica avanzada para solucionar problemas de optimización de ruta compleja. Al sistema integrado que resultó lo denominamos sistema geográfico de simulación y optimización (GeoSOS), el cual aplicamos a un estudio de caso dedicado a encontrar la ruta óptima para una supercarretera planificada en Dongguan, una ciudad de rápido crecimiento en una de las regiones económicamente más activas de China. En el estudio del caso, el componente CA del GeoSOS generó simulaciones de los cambios en uso del suelo industrial para algunos años de la próxima década. El componente ACO del GeoSOS, que había sido revisado del ACO convencional para trabajar en superficies raster, tomó las simulaciones como insumos y completó las optimizaciones de ruta de base raster. En términos de la utilidad acumulativa, medida usada para evaluar el desempeño de la optimización, el método de acoplamiento sobrepasa al método sin acople en un 10.3 por ciento.

Journal of The Indian Society of Remote Sensing, 2005

The transition to agricultural sustainability involves difficult choices and an understanding of ... more The transition to agricultural sustainability involves difficult choices and an understanding of the complex trade-offs associated with agricultural activities. Decision support tools and techniques assist in making the informed decisions for a transition to sustainable agriculture. Georgia Basin — Quite Useful Ecosystem Scenario Tool (GB-QUEST) is a computer-based, user-friendly tool that has been developed to look at the future sustainability scenarios of the Georgia Basin in British Columbia. The objective of this paper is to describe the agricultural model that has been developed for implementation in GB-QUEST. We present its framework, spatial methodology for land-use simulation, and the initial results of its application. The agriculture model is a spatial model that examines the social, economic and environmental consequences of user-defined agricultural development strategies. The model simulates changes in the Georgia Basin from the year 2000 to 2040 in decadal steps. User choices of local and global development factors, along with their "worldview" choices, are important inputs in the model that determine the effects on environmental and socio-economic systems. The model has two components — Generation of land-use scenarios, and Development of Indicator models. The first component uses cell-based spatial algorithms to simulate likely changes/conversions in land-use up to the year 2040. The approach used here integrates the functionality of Multi-Criteria Evaluation (MCE) and Cellular Automata (CA) techniques in order to simulate the land-use conversions. It uses Geographic Information Systems (GIS) and remote sensing techniques for creating, storing and deriving the data sets required for the model. The second component develops the indicator models for relating scenario variables to socio-economic and environmental variables such as physical and economic yields, economic operation costs and nutrient surplus per unit area. These indicator models are used to evaluate land-use scenarios generated by the users. The model encourages understanding of sustainability, by allowing one to explore different possible scenarios of the future for their environmental and socio-economic consequences.

It is in great need of identifying the future urban form of Beijing, which faces challenges of ra... more It is in great need of identifying the future urban form of Beijing, which faces challenges of rapid growth in urban development projects implemented in Beijing. We develop Beijing Urban Developing Model (BUDEM in short) to support urban planning and corresponding policies evaluation. BUDEM is the spatio-temporal dynamic model for simulating urban growth in Beijing metropolitan area, using cellular automata (CA) and Multi-agent system (MAS) approaches. In this phase, the computer simulation using CA in Beijing metropolitan area is conducted, which attempts to provide a premise of urban activities including different kinds of urban development projects for industrial plants, shopping facilities, houses. In the paper, concept model of BUDEM is introduced, which is established basing on prevalent urban growth theories. The method integrating logistic regression and MonoLoop is used to retrieve weights in the transition rule by MCE. After model sensibility analysis, we apply BUDEM into three aspects of urban planning practices: (1) Identifying urban growth mechanism in various historical phases since 1986; (2) Identifying urban growth policies needed to implement desired urban form (BEIJING2020), namely planned urban form; (3) Simulating urban growth scenarios of 2049 (BEIJING2049) basing on the urban form and parameter set of BEIJING2020.

International Journal of Geographical Information Science, 2011

Although being increasingly powerful in handling spatial data, geographical information systems (... more Although being increasingly powerful in handling spatial data, geographical information systems (GIS) still lack the powerful functionality for process modeling in terms of simulation and optimization. This article discusses the concepts and methodologies of a geographical simulation and optimization system (GeoSOS). GeoSOS integrates cellular automata (CA), agent-based models (ABMs), and swarm intelligence models (SIMs) for solving process simulation and optimization problems. A general form of the so-called interaction rules is proposed for implementing this integrated system. The GeoSOS software is developed to provide these complementary functions that are unavailable in the current GIS. Experiments have demonstrated that GeoSOS is able to model the reciprocal relationships between urban simulation and spatial optimization (e.g., facility sitting, transport development, and natural protection) in fast-growing regions. Better modeling performances have been achieved using the coupling strategies of GeoSOS.

Applied Spatial Analysis and Policy

This paper presents a conceptual framework for geosimulation of the New-build gentrification proc... more This paper presents a conceptual framework for geosimulation of the New-build gentrification process in an integrated approach. The combination of Multi-Criteria Evaluation (MCE) and Geographic Automata Systems (GAS) facilitates to translate the expert Knowledge into model rules. Analytic Network Process (ANP) is considered as MCE that addresses the relative importance of criteria for New-build gentrification as a complex urban phenomenon. It will be used to determine the different weights of each parameter in every time elapse. GAS which unites Cellular Automata (CA) and Multi-agent Systems (MAS) provides an excellent tool for modeling New-build gentrification. The transition rules are proposed as a combination of land use transformation and residential decision for housing area. The land use change is based on human-agent effects (local authority, neighborhood’s property value and developer) and residential decision is the adaptation of a stress-resistance hypothesis defined by Benenson (2004). The integrated approach is believed to provide a more accurate real-world urban modeling and simulation that can highlight the systematic inequalities of urban context based on the theory of New-build gentrification.

International Journal of Geographical Information Science, 2006

Rule‐based cellular automata (CA) have been increasingly applied to the simulation of geographica... more Rule‐based cellular automata (CA) have been increasingly applied to the simulation of geographical phenomena, such as urban evolution and land‐use changes. However, these models have difficulties and uncertainties in soliciting transition rules for a large complex region. This paper presents an extended cellular automaton in which transition rules are represented by using case‐based reasoning (CBR) techniques. The common k‐NN algorithm

Land use/cover changes are analyzed in the Bindura district of Zimbabwe, Africa through the integ... more Land use/cover changes are analyzed in the Bindura district of Zimbabwe, Africa through the integration of data from a time series of Landsat imagery (1973, 1989 and 2000), a household survey and GIS coverages. We employed a hybrid supervised/unsupervised classification approach to generate land use/cover maps from which landscape metrics were calculated. Population and other household variables were derived from a sample of surveyed villages, while road accessibility and slope were obtained from topographic maps and digital elevation model, respectively. Markov-cellular automata modeling approach that incorporates Markov chain analysis, cellular automata and multi-criteria evaluation (MCE) / multi-objective allocation (MOLA) procedures was used to simulate land use/cover changes. A GIS-based MCE technique computed transition potential maps, whereas transition areas were derived from the 1973-2000 land use/cover maps using the Markov chain analysis. A 5 x 5 cellular automata filter was used to develop a spatially explicit contiguity- weighting factor to change the cells based on its previous state and those of its neighbors, while MOLA resolved land use/cover class allocation conflicts. The kappa index of agreement was used for model validation. Observed trends in land use/cover changes indicate that deforestation and the encroachment of cultivation in woodland areas is a continuous trend in the study area. This suggests that economic activities driven by agricultural expansion were the main causes of landscape fragmentation, leading to landscape degradation. Rigorous calibration of transition potential maps done by a MCE algorithm and Markovian transition probabilities produced accurate inputs for the simulation of land use/cover changes. Overall standard kappa index of agreement ranged from 0.73 to 0.83, which is sufficient for simulating land use/cover changes in the study area. Land use/cover simulations under the 1989 and 2000 scenario indicated further landscape degradation in the rural areas of the Bindura district. Keywords: Zimbabwe, land use/cover changes, landscape fragmentation, GIS, land use/cover change modeling, multi-criteria evaluation/multi-objective allocation procedures, Markov-cellular automata

CA = Cellular Automata LR = Literature Review AM = Analyse available urban CA Models EI = Expert ... more CA = Cellular Automata LR = Literature Review AM = Analyse available urban CA Models EI = Expert Interview TP =Transition Potential of cell NB =Neighbourhood Effect AC =Accessibility Effect SU =Suitability Effect PL =Planning Influence SEco =Social-Economic influence TR =Transition Rule TPR =Transition Potential Rule CRR =Confliction Resolving Rule

International Journal of Geographical Information Science, 2012

This article presents a new method of assimilating process context information into change detect... more This article presents a new method of assimilating process context information into change detection for monitoring land use changes. The accurate information about land use changes is important for implementing many global and regional environmental models. Two types of models have been independently developed to obtain such information, including change detection models (e.g. pixel-to-pixel comparison, post-classification comparison and object-based change analysis) and simulation models (e.g. cellular automata (CA) and agent-based modelling). These models may have limitations in capturing land use dynamics when used alone. In this study, the ensemble Kalman filter is used to obtain the best estimate of land use changes by combining remote-sensing observations with urban simulation. Urban simulation is able to provide process context information such as diffusion and coalescence of urban development. This type of complementary information is useful for improving the performance of change detection. Compared with traditional change detection models, this integrated model has the potential to improve the performance of change detection in terms of accuracies and landscape metrics. For example, the assimilating (MLC + CA) method can show improvement of the total accuracy and the kappa coefficient by 2.5–5.2% and 3.6–7.4%, respectively, in this study.

The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

Cellular automata (CA) is increasingly used to simulate various dynamic courses, e.g. urban spati... more Cellular automata (CA) is increasingly used to simulate various dynamic courses, e.g. urban spatial growth, forest fire spread and soil desertification. CA simulates these courses with iterative evolvements. Through simple and partial rules to form complex space structures and patterns, CA can express complex systems which are difficult to perform only with mathematical formulas. In combination with GIS Software, such as ArcGIS, it can directly and vividly reflect the geospatial state changes and accomplish many kinds of spatial analysis. This paper introduces CA concept into irrigated agriculture and presents a new CA based GIS (Geographic Information System) approach which incorporates simulation methods and implementation of land suitability evaluation to assess agriculture land use in a GIS environment.

Journal of The Indian Society of Remote Sensing, 2010

In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) ... more In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city. In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data. Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site. Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process. The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model

The evolution of cities at an urban-regional scale reflects complex relationships between ways in... more The evolution of cities at an urban-regional scale reflects complex relationships between ways in which urban structure develops in response to local decisions involving land development which is set within the more aggregate pattern of urban and regional structure. There is a mutual interaction between physical development and the urban hierarchy which is not often accounted for in the new wave of cellular models that have appeared in the last ten years. This chapter describes an implementation of a simulation model that is based on integrating these local and regional dynamics. We call it the Dynamic Settlement Simulation Model (DSSM)and we develop the integration using two different cell-based modelling techniques: cellular automata (CA) and raster GIS. The model is implemented using an object-oriented programming approach, and after we describe its rudiments, albeit briefly, we show its application to real data from Chiang Mai, a major city in Thailand. Finally, this chapter indicates how the model can be used as a part of a spatial decision support system (SDSS) generating predictive outcomes that represent possibilities for implementing predictive and scenario-based applications in urban and regional planning and related fields.

International Journal of Geographical Information Science, 2010

Cellular automata (CA), which are a kind of bottom-up approaches, can be used to simulate urban d... more Cellular automata (CA), which are a kind of bottom-up approaches, can be used to simulate urban dynamics and land use changes effectively. Urban simulation usually involves a large set of GIS data in terms of the extent of the study area and the number of spatial factors. ...

International Journal of Geographical Information Science, 2011

Optimal zoning of protected natural areas is important for conserving ecosystems. It is an NP-har... more Optimal zoning of protected natural areas is important for conserving ecosystems. It is an NP-hard problem which is difficult to solve by using common geographic information system (GIS) functions. Another problem is that existing optimization methods ignore potential land-use dynamics in formulating optimal patterns. This article has developed a new method for solving complicated zoning problems by using ant colony optimization (ACO) techniques. Significant modifications have been made, so that traditional ACO can be extended to the solution of area optimization problems. Two strategies, the single-year coupling strategy and the merging-year coupling strategy, have been proposed to couple urban cellular automata with ACO for zoning protected natural areas under a changing landscape. This proposed method has been tested in the metropolitan region of Guangzhou, China, by using Geographical Simulation and Optimization System (GeoSOS) software. The experiments indicate that the modified ACO can effectively solve this optimization problem without getting stuck in local optima. This method has better performances compared to other traditional methods, such as simulated annealing (SA), iterative relaxation (IR), and density slicing (DS). The use of the best coupling strategy can improve the accumulative utility value of the zoning by 4.3%. Moreover, it is also found that the adoption of the best protection pattern could significantly promote the compactness of future urban forms in the study area.

International Journal of Geographical Information Science, 2011

Cellular automata (CA) models are increasingly used to simulate various dynamic courses, e.g. urb... more Cellular automata (CA) models are increasingly used to simulate various dynamic courses, e.g. urban spatial growth, forest fire spread and soil desertification. CA can express space structures and patterns of complex systems, which are difficult to perform only with mathematical equations. In this study, a new CA-based spatial multi-criteria evaluation (MCE) methodology was developed to conduct land suitability simulation (LSS). The approach incorporated MATLAB to build the analytical hierarchy procedure (AHP) for criteria weighting. The method is implemented as a tool, called AHP–CA–GIS, using C# .NET computer language in ArcGIS environment. It has adjustable parameter values which allow users to rectify model inputs for deriving different scenarios. It is spatial-based, flexible, low-cost and robust, as well as suitable for long-term evaluation. It has increased the scope of GIS application in MCE and makes the application practical for decision-making. The AHP–CA–GIS model has been applied to simulate an evaluation of irrigated cropland suitability in the Macintyre Brook catchment of southern Queensland, Australia. Five suitability scenarios were generated. The resultant land suitability map was compared with present land use. The analysis has clearly revealed the potential for irrigation expansion in the catchment. It has also represented the possible suitability of spatial distribution in the long run. This, in turn, can help the decision-makers optimise land allocation and make better land-use planning decisions.

Cellular automata (CA) can be used to simulate complex urban systems. Calibration of CA is essent... more Cellular automata (CA) can be used to simulate complex urban systems. Calibration of CA is essential for producing realistic urban patterns. A common calibration procedure is based on linear regression methods, such as multicriteria evaluation. This paper proposes a new method to acquire nonlinear transition rules of CA by using the techniques of kernel-based learning machines. The kernel-based approach transforms complex nonlinear problems to simple linear problems through the mapping on an implicit high-dimensional feature space for extracting transition rules. This method has been applied to the simulation of urban expansion in the fast growing city, Guangzhou. Comparisons indicate that more reliable simulation results can be generated by using this kernel-based method.

International Journal of Geographical Information Science, 2012

In recent years, agent-based models (ABMs) have become a prevalent approach for modelling complex... more In recent years, agent-based models (ABMs) have become a prevalent approach for modelling complex urban systems. As a class of bottom-up method, ABMs are capable of simulating the decision-making as well as the multiple interactions of autonomous agents and between agents and the environment. The definition of agents' behaviour is a vital issue in implementing ABMs to simulate urban dynamics. Urban economic theory has provided effective ways to cope with this problem. This theory argues that the formation of urban spatial structure is an endogenous process resulting from the interactions among individual actors that are spatially distributed. However, this theory is used to explain urban phenomena regardless of spatial heterogeneity in most cases. This study combines GIS, ABM and urban economic models to simulate complex urban residential dynamics. The time-extended model is incorporated into an ABM so as to define agents' behaviour on a solid theoretical basis. A spatial variable is defined to address the neighbourhood effect by considering spatial heterogeneity. The proposed model is first verified by the simulation of three scenarios using hypothetical data: (1) single dominated preference; (2) varying preferences on the basis of income level; and (3) spatially heterogeneous environment. Then the model is implemented by simulating the residential dynamics in Guangzhou, China.

This paper studies the development of GIS from a new point of view, which both from the accumulat... more This paper studies the development of GIS from a new point of view, which both from the accumulation of GIS scientific articles and the evolution process of GIS using co-citation analysis. After 10 years' slow development from 1977, the number of GIS scientific literature began to increase rapidly since the early 1990s. By using the newly developed information visualization technology, the authors make Author Co-citation Analysis (ACA) and Document Co-citation Analysis (DCA) for 12,417 GIS scientific articles indexed in SCI-Expanded, SSCI, A&HCI. The result reveals the evolution process of GIS from intellectual base to several application fronts: hydrology, land use planning, landslide hazard, landscape ecology, when these applications in engineering areas also accelerate development of GIS. The pivotal authors and literature in the evolution process of GIS from intellectual base to applied fronts are also detected by the visualization of ACA and DCA of GIS scientific literature.

Agriculture Ecosystems & Environment, 2001

Scientists need a better and larger set of tools to validate land-use change models, because it i... more Scientists need a better and larger set of tools to validate land-use change models, because it is essential to know a model's prediction accuracy. This paper describes how to use the relative operating characteristic (ROC) as a quantitative measurement to validate a land-cover change model. Typically, a crucial component of a spatially explicit simulation model of land-cover change is a map of suitability for land-cover change, for example a map of probability of deforestation. The model usually selects locations for new land-cover change at locations that have relatively high suitability. The ROC can compare a map of actual change to maps of modeled suitability for land-cover change. ROC is a summary statistic derived from several two-by-two contingency tables, where each contingency table corresponds to a different simulated scenario of future land-cover change. The categories in each contingency table are actual change and actual non-change versus simulated change and simulated non-change. This paper applies the theoretical concepts to a model of deforestation in the Ipswich watershed, USA.

Annals of The Association of American Geographers, 2011

In geographical analysis, spatial simulation and optimization are usually separate processes tack... more In geographical analysis, spatial simulation and optimization are usually separate processes tackling different problems. It is, however, increasingly necessary to integrate them. Particularly in a fast developing area, the development to be simulated is seldom inertial (i.e., strictly following the historical trend); instead, it is likely to be interfered by new planning measures. Meanwhile, in such an area an optimization plan might not be even meaningful if it only addresses a snapshot of a highly dynamic landscape. In this study, we explored the possibility of integrating cellular automata (CA), a widely used method for simulating urban development and land use changes, and ant colony optimization (ACO), an advanced technique for solving complex path optimization problems. We named the resulting integrated system the geographical simulation and optimization system (GeoSOS) and applied it to a case study concerning finding the optimal path for a planned expressway in Dongguan, a fast-growing city in one of the most economically active regions of China. In the case study, the CA component of the GeoSOS generated simulations of the industrial land use changes for some years in the next decade. The ACO component of the GeoSOS, which had been revised from the conventional ACO to work on raster surfaces, took the simulations as input and completed raster-based path optimizations. In terms of the cumulative utility, a measurement used to evaluate the performance of the optimization, the coupling method surpasses the noncoupling method by 10.3 percent. En análisis geográfico la simulación y optimización espaciales usualmente son procesos separados que abordan problemas diferentes. Sin embargo, cada vez se hace más necesario integrarlos. En particular en una región que se desarrolle con rapidez, el desarrollo que se deba simular rara vez sigue la inercia (o sea, que siga estrictamente la tendencia histórica); en vez de eso, lo más seguro es que sea interferido por nuevas medidas de planificación. Mientras tanto, en tal tipo de área un plan de optimización podría no ser siquiera significativo si apenas cubriera una fracción de un paisaje altamente dinámico. En este estudio exploramos la posibilidad de integrar autómata celular (AC), un método ampliamente utilizado para simular desarrollo urbano y cambios en el uso del suelo, y optimización de hormiguero (ACO), una técnica avanzada para solucionar problemas de optimización de ruta compleja. Al sistema integrado que resultó lo denominamos sistema geográfico de simulación y optimización (GeoSOS), el cual aplicamos a un estudio de caso dedicado a encontrar la ruta óptima para una supercarretera planificada en Dongguan, una ciudad de rápido crecimiento en una de las regiones económicamente más activas de China. En el estudio del caso, el componente CA del GeoSOS generó simulaciones de los cambios en uso del suelo industrial para algunos años de la próxima década. El componente ACO del GeoSOS, que había sido revisado del ACO convencional para trabajar en superficies raster, tomó las simulaciones como insumos y completó las optimizaciones de ruta de base raster. En términos de la utilidad acumulativa, medida usada para evaluar el desempeño de la optimización, el método de acoplamiento sobrepasa al método sin acople en un 10.3 por ciento.

Journal of The Indian Society of Remote Sensing, 2005

The transition to agricultural sustainability involves difficult choices and an understanding of ... more The transition to agricultural sustainability involves difficult choices and an understanding of the complex trade-offs associated with agricultural activities. Decision support tools and techniques assist in making the informed decisions for a transition to sustainable agriculture. Georgia Basin — Quite Useful Ecosystem Scenario Tool (GB-QUEST) is a computer-based, user-friendly tool that has been developed to look at the future sustainability scenarios of the Georgia Basin in British Columbia. The objective of this paper is to describe the agricultural model that has been developed for implementation in GB-QUEST. We present its framework, spatial methodology for land-use simulation, and the initial results of its application. The agriculture model is a spatial model that examines the social, economic and environmental consequences of user-defined agricultural development strategies. The model simulates changes in the Georgia Basin from the year 2000 to 2040 in decadal steps. User choices of local and global development factors, along with their "worldview" choices, are important inputs in the model that determine the effects on environmental and socio-economic systems. The model has two components — Generation of land-use scenarios, and Development of Indicator models. The first component uses cell-based spatial algorithms to simulate likely changes/conversions in land-use up to the year 2040. The approach used here integrates the functionality of Multi-Criteria Evaluation (MCE) and Cellular Automata (CA) techniques in order to simulate the land-use conversions. It uses Geographic Information Systems (GIS) and remote sensing techniques for creating, storing and deriving the data sets required for the model. The second component develops the indicator models for relating scenario variables to socio-economic and environmental variables such as physical and economic yields, economic operation costs and nutrient surplus per unit area. These indicator models are used to evaluate land-use scenarios generated by the users. The model encourages understanding of sustainability, by allowing one to explore different possible scenarios of the future for their environmental and socio-economic consequences.

It is in great need of identifying the future urban form of Beijing, which faces challenges of ra... more It is in great need of identifying the future urban form of Beijing, which faces challenges of rapid growth in urban development projects implemented in Beijing. We develop Beijing Urban Developing Model (BUDEM in short) to support urban planning and corresponding policies evaluation. BUDEM is the spatio-temporal dynamic model for simulating urban growth in Beijing metropolitan area, using cellular automata (CA) and Multi-agent system (MAS) approaches. In this phase, the computer simulation using CA in Beijing metropolitan area is conducted, which attempts to provide a premise of urban activities including different kinds of urban development projects for industrial plants, shopping facilities, houses. In the paper, concept model of BUDEM is introduced, which is established basing on prevalent urban growth theories. The method integrating logistic regression and MonoLoop is used to retrieve weights in the transition rule by MCE. After model sensibility analysis, we apply BUDEM into three aspects of urban planning practices: (1) Identifying urban growth mechanism in various historical phases since 1986; (2) Identifying urban growth policies needed to implement desired urban form (BEIJING2020), namely planned urban form; (3) Simulating urban growth scenarios of 2049 (BEIJING2049) basing on the urban form and parameter set of BEIJING2020.

International Journal of Geographical Information Science, 2011

Although being increasingly powerful in handling spatial data, geographical information systems (... more Although being increasingly powerful in handling spatial data, geographical information systems (GIS) still lack the powerful functionality for process modeling in terms of simulation and optimization. This article discusses the concepts and methodologies of a geographical simulation and optimization system (GeoSOS). GeoSOS integrates cellular automata (CA), agent-based models (ABMs), and swarm intelligence models (SIMs) for solving process simulation and optimization problems. A general form of the so-called interaction rules is proposed for implementing this integrated system. The GeoSOS software is developed to provide these complementary functions that are unavailable in the current GIS. Experiments have demonstrated that GeoSOS is able to model the reciprocal relationships between urban simulation and spatial optimization (e.g., facility sitting, transport development, and natural protection) in fast-growing regions. Better modeling performances have been achieved using the coupling strategies of GeoSOS.

Applied Spatial Analysis and Policy

This paper presents a conceptual framework for geosimulation of the New-build gentrification proc... more This paper presents a conceptual framework for geosimulation of the New-build gentrification process in an integrated approach. The combination of Multi-Criteria Evaluation (MCE) and Geographic Automata Systems (GAS) facilitates to translate the expert Knowledge into model rules. Analytic Network Process (ANP) is considered as MCE that addresses the relative importance of criteria for New-build gentrification as a complex urban phenomenon. It will be used to determine the different weights of each parameter in every time elapse. GAS which unites Cellular Automata (CA) and Multi-agent Systems (MAS) provides an excellent tool for modeling New-build gentrification. The transition rules are proposed as a combination of land use transformation and residential decision for housing area. The land use change is based on human-agent effects (local authority, neighborhood’s property value and developer) and residential decision is the adaptation of a stress-resistance hypothesis defined by Benenson (2004). The integrated approach is believed to provide a more accurate real-world urban modeling and simulation that can highlight the systematic inequalities of urban context based on the theory of New-build gentrification.

International Journal of Geographical Information Science, 2006

Rule‐based cellular automata (CA) have been increasingly applied to the simulation of geographica... more Rule‐based cellular automata (CA) have been increasingly applied to the simulation of geographical phenomena, such as urban evolution and land‐use changes. However, these models have difficulties and uncertainties in soliciting transition rules for a large complex region. This paper presents an extended cellular automaton in which transition rules are represented by using case‐based reasoning (CBR) techniques. The common k‐NN algorithm

Land use/cover changes are analyzed in the Bindura district of Zimbabwe, Africa through the integ... more Land use/cover changes are analyzed in the Bindura district of Zimbabwe, Africa through the integration of data from a time series of Landsat imagery (1973, 1989 and 2000), a household survey and GIS coverages. We employed a hybrid supervised/unsupervised classification approach to generate land use/cover maps from which landscape metrics were calculated. Population and other household variables were derived from a sample of surveyed villages, while road accessibility and slope were obtained from topographic maps and digital elevation model, respectively. Markov-cellular automata modeling approach that incorporates Markov chain analysis, cellular automata and multi-criteria evaluation (MCE) / multi-objective allocation (MOLA) procedures was used to simulate land use/cover changes. A GIS-based MCE technique computed transition potential maps, whereas transition areas were derived from the 1973-2000 land use/cover maps using the Markov chain analysis. A 5 x 5 cellular automata filter was used to develop a spatially explicit contiguity- weighting factor to change the cells based on its previous state and those of its neighbors, while MOLA resolved land use/cover class allocation conflicts. The kappa index of agreement was used for model validation. Observed trends in land use/cover changes indicate that deforestation and the encroachment of cultivation in woodland areas is a continuous trend in the study area. This suggests that economic activities driven by agricultural expansion were the main causes of landscape fragmentation, leading to landscape degradation. Rigorous calibration of transition potential maps done by a MCE algorithm and Markovian transition probabilities produced accurate inputs for the simulation of land use/cover changes. Overall standard kappa index of agreement ranged from 0.73 to 0.83, which is sufficient for simulating land use/cover changes in the study area. Land use/cover simulations under the 1989 and 2000 scenario indicated further landscape degradation in the rural areas of the Bindura district. Keywords: Zimbabwe, land use/cover changes, landscape fragmentation, GIS, land use/cover change modeling, multi-criteria evaluation/multi-objective allocation procedures, Markov-cellular automata

CA = Cellular Automata LR = Literature Review AM = Analyse available urban CA Models EI = Expert ... more CA = Cellular Automata LR = Literature Review AM = Analyse available urban CA Models EI = Expert Interview TP =Transition Potential of cell NB =Neighbourhood Effect AC =Accessibility Effect SU =Suitability Effect PL =Planning Influence SEco =Social-Economic influence TR =Transition Rule TPR =Transition Potential Rule CRR =Confliction Resolving Rule

International Journal of Geographical Information Science, 2012

This article presents a new method of assimilating process context information into change detect... more This article presents a new method of assimilating process context information into change detection for monitoring land use changes. The accurate information about land use changes is important for implementing many global and regional environmental models. Two types of models have been independently developed to obtain such information, including change detection models (e.g. pixel-to-pixel comparison, post-classification comparison and object-based change analysis) and simulation models (e.g. cellular automata (CA) and agent-based modelling). These models may have limitations in capturing land use dynamics when used alone. In this study, the ensemble Kalman filter is used to obtain the best estimate of land use changes by combining remote-sensing observations with urban simulation. Urban simulation is able to provide process context information such as diffusion and coalescence of urban development. This type of complementary information is useful for improving the performance of change detection. Compared with traditional change detection models, this integrated model has the potential to improve the performance of change detection in terms of accuracies and landscape metrics. For example, the assimilating (MLC + CA) method can show improvement of the total accuracy and the kappa coefficient by 2.5–5.2% and 3.6–7.4%, respectively, in this study.

The user has requested enhancement of the downloaded file. All in-text references underlined in b... more The user has requested enhancement of the downloaded file. All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.

Cellular automata (CA) is increasingly used to simulate various dynamic courses, e.g. urban spati... more Cellular automata (CA) is increasingly used to simulate various dynamic courses, e.g. urban spatial growth, forest fire spread and soil desertification. CA simulates these courses with iterative evolvements. Through simple and partial rules to form complex space structures and patterns, CA can express complex systems which are difficult to perform only with mathematical formulas. In combination with GIS Software, such as ArcGIS, it can directly and vividly reflect the geospatial state changes and accomplish many kinds of spatial analysis. This paper introduces CA concept into irrigated agriculture and presents a new CA based GIS (Geographic Information System) approach which incorporates simulation methods and implementation of land suitability evaluation to assess agriculture land use in a GIS environment.

Journal of The Indian Society of Remote Sensing, 2010

In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) ... more In the study reported in this paper an attempt has been made to develop a Cellular Automata (CA) model for simulating future urban growth of an Indian city. In the model remote sensing data and GIS were used to provide the empirical data about urban growth while Markov chain process was used to predict the amount of land required for future urban use based on the empirical data. Multi-criteria evaluation (MCE) technique was used to reveal the relationships between future urban growth potential and site attributes of a site. Finally using the CA model, land for future urban development was spatially allocated based on the urban suitability image provided by MCE, neighbourhood information of a site and the amount of land predicted by Markov chain process. The model results were evaluated using Kappa Coefficient and future urban growth was simulated using the calibrated model

The evolution of cities at an urban-regional scale reflects complex relationships between ways in... more The evolution of cities at an urban-regional scale reflects complex relationships between ways in which urban structure develops in response to local decisions involving land development which is set within the more aggregate pattern of urban and regional structure. There is a mutual interaction between physical development and the urban hierarchy which is not often accounted for in the new wave of cellular models that have appeared in the last ten years. This chapter describes an implementation of a simulation model that is based on integrating these local and regional dynamics. We call it the Dynamic Settlement Simulation Model (DSSM)and we develop the integration using two different cell-based modelling techniques: cellular automata (CA) and raster GIS. The model is implemented using an object-oriented programming approach, and after we describe its rudiments, albeit briefly, we show its application to real data from Chiang Mai, a major city in Thailand. Finally, this chapter indicates how the model can be used as a part of a spatial decision support system (SDSS) generating predictive outcomes that represent possibilities for implementing predictive and scenario-based applications in urban and regional planning and related fields.

International Journal of Geographical Information Science, 2010

Cellular automata (CA), which are a kind of bottom-up approaches, can be used to simulate urban d... more Cellular automata (CA), which are a kind of bottom-up approaches, can be used to simulate urban dynamics and land use changes effectively. Urban simulation usually involves a large set of GIS data in terms of the extent of the study area and the number of spatial factors. ...

International Journal of Geographical Information Science, 2011

Optimal zoning of protected natural areas is important for conserving ecosystems. It is an NP-har... more Optimal zoning of protected natural areas is important for conserving ecosystems. It is an NP-hard problem which is difficult to solve by using common geographic information system (GIS) functions. Another problem is that existing optimization methods ignore potential land-use dynamics in formulating optimal patterns. This article has developed a new method for solving complicated zoning problems by using ant colony optimization (ACO) techniques. Significant modifications have been made, so that traditional ACO can be extended to the solution of area optimization problems. Two strategies, the single-year coupling strategy and the merging-year coupling strategy, have been proposed to couple urban cellular automata with ACO for zoning protected natural areas under a changing landscape. This proposed method has been tested in the metropolitan region of Guangzhou, China, by using Geographical Simulation and Optimization System (GeoSOS) software. The experiments indicate that the modified ACO can effectively solve this optimization problem without getting stuck in local optima. This method has better performances compared to other traditional methods, such as simulated annealing (SA), iterative relaxation (IR), and density slicing (DS). The use of the best coupling strategy can improve the accumulative utility value of the zoning by 4.3%. Moreover, it is also found that the adoption of the best protection pattern could significantly promote the compactness of future urban forms in the study area.

International Journal of Geographical Information Science, 2011

Cellular automata (CA) models are increasingly used to simulate various dynamic courses, e.g. urb... more Cellular automata (CA) models are increasingly used to simulate various dynamic courses, e.g. urban spatial growth, forest fire spread and soil desertification. CA can express space structures and patterns of complex systems, which are difficult to perform only with mathematical equations. In this study, a new CA-based spatial multi-criteria evaluation (MCE) methodology was developed to conduct land suitability simulation (LSS). The approach incorporated MATLAB to build the analytical hierarchy procedure (AHP) for criteria weighting. The method is implemented as a tool, called AHP–CA–GIS, using C# .NET computer language in ArcGIS environment. It has adjustable parameter values which allow users to rectify model inputs for deriving different scenarios. It is spatial-based, flexible, low-cost and robust, as well as suitable for long-term evaluation. It has increased the scope of GIS application in MCE and makes the application practical for decision-making. The AHP–CA–GIS model has been applied to simulate an evaluation of irrigated cropland suitability in the Macintyre Brook catchment of southern Queensland, Australia. Five suitability scenarios were generated. The resultant land suitability map was compared with present land use. The analysis has clearly revealed the potential for irrigation expansion in the catchment. It has also represented the possible suitability of spatial distribution in the long run. This, in turn, can help the decision-makers optimise land allocation and make better land-use planning decisions.

Cellular automata (CA) can be used to simulate complex urban systems. Calibration of CA is essent... more Cellular automata (CA) can be used to simulate complex urban systems. Calibration of CA is essential for producing realistic urban patterns. A common calibration procedure is based on linear regression methods, such as multicriteria evaluation. This paper proposes a new method to acquire nonlinear transition rules of CA by using the techniques of kernel-based learning machines. The kernel-based approach transforms complex nonlinear problems to simple linear problems through the mapping on an implicit high-dimensional feature space for extracting transition rules. This method has been applied to the simulation of urban expansion in the fast growing city, Guangzhou. Comparisons indicate that more reliable simulation results can be generated by using this kernel-based method.