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Modelling Urban Growth Using Cellular Automata: A case study of Sydney, Australia
... Despite its dynamism and having been the object of different strategic plans in the last decades, only a few urban growth model applications have been developed for Sydney. These include the extensive work carried out by Yan Liu (Liu, 2009;Liu & Feng, 2012;Liu & Phinn, 2004 and the Land Use Cover Change (LUCC) model application set up by Lahti (2008). Both authors carried out model applications that were modest, with a coarse spatial resolution (250 m). ...
... Since its foundation, the city has been rapidly growing and sprawling, especially along the 20th century ( Fig. 10.1). In the first part of the century, the city mostly has grown following the rail lines (Greater Sydney Commission, 2017;Lahti, 2008). Later, the road network has been the main driver guiding the expansion of the urban footprint (Greater Sydney Commission, 2017). ...
... Metronamica is a stable and tested modelling framework, already applied to many urban areas across the world (Hewitt et al., 2014;Kim & Batty, 2011;Lahti, 2008;Páez & Escobar, 2017;. There is plenty of technical information available (RIKS, 2012;, as well as a community of users able to help with the model usage. ...
This study presents a state of the art and a systematic review of literature that identifies the driving forces of land use/cover change (LUCC) and aims to move the discussion forward on the role of social actors in the direct and indirect drivers of land use change in the drylands of South America. Specifically, this review focuses on the characterization of how LUCC studies have addressed the factors driving territorial transformations in drylands, and their main related physical-biological and socioeconomic consequences. In this regard, there are on the one hand studies focused on describing the processes of land use changes from frameworks that are generally qualitative and poorly spatialised. On the other hand—particularly in South America—there are studies that delve into LUCC with very precise descriptions in the spatial context, but do not always manage to articulate a social and cultural approach that incorporates the qualitative explanations that we find in the first type of studies.
... Some scholars have found zoning laws to be largely irrelevant to the nature of urban expansion and agricultural conversion (Kuminoff & Sumner, 2001), or not statistically significant in explaining the location of such growth (Batisani & Yarnal, 2009). Lahti (2008) claims that CA models such as SLEUTH do not best represent top-down phenomena such as zoning, but instead are more effective at capturing bottom-up ecological processes (Lahti, 2008). Other studies acknowledge or emphasize the importance of zoning to land use change processes. ...
... Some scholars have found zoning laws to be largely irrelevant to the nature of urban expansion and agricultural conversion (Kuminoff & Sumner, 2001), or not statistically significant in explaining the location of such growth (Batisani & Yarnal, 2009). Lahti (2008) claims that CA models such as SLEUTH do not best represent top-down phenomena such as zoning, but instead are more effective at capturing bottom-up ecological processes (Lahti, 2008). Other studies acknowledge or emphasize the importance of zoning to land use change processes. ...
... Although critics of SLEUTH have pointed to its lack of policy variables (Goetz et al., 2007;Lahti, 2008;Torrens & O'Sullivan, 2001), it is still possible to integrate such knowledge into the model, provided that these policy variables can be understood, rendered and quantified appropriately. Though Poelmans and Rompaey (2009) suggested that including local conditions, such as policy decisions, in a computational model is "impossible", spatially explicit land use policies, such as zoning regulations, are, when applying appropriate methods, particularly amenable to greater integration into cellular automata modeling (Poelmans & Rompaey, 2009). ...
Landscape change is a key feature of social-ecological change, and is especially marked in, urbanizing regions. Planning institutions use land use zoning to control and direct such changes. Urban growth models are commonly used to better understand past landscape changes as well as, forecast and plan for future landscape changes. Many of these models, however, do not utilize zoning, information in their deployment because many model designers do not believe zoning to be a relevant, criterion for the prediction of urban growth. This research offers a novel methodology for integrating, zoning information into a cellular automaton urban growth model, SLEUTH. It additionally tests the, utility of such information by comparing metrics of fit with past data under different zoning inclusion, conditions in a community of Miami-Dade County, Florida. These conditions include one scenario, where zoning is ignored and three others where it is included. The latter three test different methods, of including zoning data for three generalized zoning categories - arbitrarily guessing, measuring urban, growth in each zoning category for the entire study area, and measuring urban growth in each zoning, category only in those areas more likely to experience growth. Results indicate that this final condition, generates the highest model performance metric and creates a more fair comparison since remote, areas in the study area, less likely to experience growth, exaggerate differences in urban growth rates, across the different zoning categories. We conclude that zoning information, when utilized, appropriately, improves model performance and is therefore relevant for landscape change.
... Despite its dynamism and having been the object of different strategic plans in the last decades, only a few urban growth model applications have been developed for Sydney. These include the extensive work carried out by Yan Liu (Liu, 2009;Liu and Feng, 2012;Liu andPhinn, 2005, 2004) and the Land Use Cover Change (LUCC) model application set up by Lahti (2008). Both authors carried out model applications that were modest, with a spatial resolution of their models being 250m. ...
... Metronamica is a stable and tested modelling framework, already applied to many urban areas across the world (Hewitt et al., 2014;Kim and Batty, 2011;Lahti, 2008;Páez and Escobar, 2017;. There is plenty of technical information available (RIKS, 2012;, as well as a community of users able to help with the model usage. ...
This chapter presents a Land Use Cover Change (LUCC) model application developed for Greater Sydney. It aims to help decision making in the context of the strategic and spatial planning of Greater Sydney. To this end, the model simulates the dynamics of industrial, low density residential and medium-high density residential areas at spatial resolution of 100x100 m.A series of three Land Use Maps at 30m were specifically developed for the modelling exercise. They determine part of the exercise´s limitations, such as the model simplicity and the short timeframe of the simulation (2006-2011-2016). Future efforts should focus on the simulation of population and job growth, exchanges between regions and the application of the model for scenario analysis and impact assessment.All data of the modelling exercise are openly available at the CityData portal of the City Futures Research Centre.
... LULC change process involves many factors like as various socio-economic, physical and ecological components at diverse spatial and temporal scales, which make this process complex and dynamic (Cheng, 2003). LULC change model is a tool to understand the pattern of this complex process However, such models require improved understanding about the factors of LULC change at different magnitude in the different regions (Lahti, 2008;Pijanowski et al., 2002;Thekkudan, 2008;Turner et al., 1995) due to high geographic variability in land-cover types, biophysical and socio-economic drivers (Serneels & Lambin, 2001). Turner et. ...
... Li & Li, 2015;X. Li & Yeh, 2002) and LR-CA (Arsanjani et al., 2013;Lahti, 2008;Liu & Feng, 2011) gain popularity for these model's improved performance in terms of accuracy of LULC prediction. ...
... Among these, the most popular methods are cellular automata (CA) (Leao et al., 2004;Marshall and Randhir, 2008), artificial neural network (ANN) (Maithani, 2009;Pijanowski et al., 2002; Thekkudan, 2008), agent-based model (ABM) (Bharath et al., 2016;Entwisle et al., 2008), logistic regression (LR) (Hu and Lo, 2007;Wu et al., 2009), and support vector machine (SVM) Samardžić-Petrović et al., 2015;Xie, 2006). Recently, integrated models such as ANN-CA (Li and Li, 2015;Li and Yeh, 2002) and LR-CA (Arsanjani et al., 2013;Lahti, 2008;Liu and Feng, 2011) are gaining popularity for their improved performance in terms of accuracy of LULC prediction. However, researchers are continuously trying to improve the predictive accuracy of LULC models with more robust methods and techniques. ...
Land use and land cover (LULC) changes have significant consequences on habitat and the environment. Past studies developed several LULC change models to identify the factors behind the changes and to simulate future LULC scenarios. However, the accuracy of these models remained contentious and a matter of ongoing debate and research. Most of these studies used a training dataset to train the model and a validation dataset to validate the prediction accuracy, both of which are a part of the original training dataset. However, to evaluate the model's actual predictive capability in terms of spatial data modeling, it is necessary to test the model's performance on the real-world dataset. In this study, we presented an XGBoost model aiming at improving the prediction accuracy while used a separate test dataset to test the model's actual predictive capacity. We applied the method to predict the land cover change of the Khulna City Corporation (KCC) area in Bangladesh. The study reveals that the KCC area experienced rapid urban development during the 2002–2018 period while the agricultural and vacant land declined at a similar rate. The major factors contributing to this substantial change of the city's land covers are-proximity to existing built-up areas and proximity to major roads. Our study indicates that agricultural areas and wetlands closer to the major roads and existing urban areas have a greater probability of converting into built-up areas. Our experiment demonstrates that the XGBoost model can predict the city's land cover change with greater accuracy and outperforms the benchmark models such as the LR-CA and ANN-CA. The finding assures the reliability of the XGBoost model while predicting future land-cover scenarios.
... The collected data were compiled and analyzed systematically by keeping in view of the objectives of the study. In this study, multispectral sensors, Thematic Mapper, Enhanced Thematic Mapper Plus of Landsat 1,2, 5, & 8 multispectral digital data (spatial resolution 60m) of winter season of October-1987, November-1987, December-1972, Thematic Mapper data (Spatial resolution 30m), Enhanced Thematic mapper Plus data (Spectral resolution 30m) of starting of summer from March-1977, 2015, April-2000, 2008. The identity and site of several the land cover types like urban, agriculture, wetland, are referred to as prior through a mixture of field work and their experiences. ...
Remote sensing and Geographic Information System (GIS); plays a vital role for studying Land Use Land Cover (LULC) and identifying the main factors for useful outcomes. Assessment of the urban growth pattern is extremely essential as sprawl is seen as one of the potential threats for urban planning. The project has been carried out for the Land Use Land Cover classification of Gandhinagar district of Gujarat state. Gandhinagar city has experienced wide change in LULC in last few decades. It is located at 23.2156° N & 72.6369° E in Gujarat. LULC mapping of Gandhinagar was carried out using LANDSAT Multispectral, TM, ETM+, and OLI/TIRS images for the years 1972, 1977, 1987, 1994, 2000, 2008, 2015 and 2019. Landsat data covers Gandhinagar’s vegetation, Water Bodies, Open Area, Agriculture, and Settlement. The area of interest of Gandhinagar was generated from Landsat data using the digitized boundary of Gandhinagar district. The main objective of this project is to generate LULC using different classification method of remotely sensed data of LANDSAT. In this study Supervised classification method was used to generate level 1 classification. It was done on remotely sensed data in ERDAS Imagine 2014 using semi-automatic classification which includes several classes like Settlement, Agriculture, Vegetation, Water Bodies, Open Area, etc. Moreover, after LULC one new thing was done i.e. accuracy assessment which was necessary to do for accurate result. The study result reveals an increasing and decreasing trend in Land use and Land cover respectively.
... These spots are emergence signs of leaf frog development that must be managed properly. Leaf frog development has a bad influence on urban development due to land fragmentation [37]. ...
Land use and land cover (LULC) changes through built-up area expansion always increases linearly with land demand as a consequence of population growth and urbanization. Cirebon City is a center for Ciayumajakuning Region that continues to grow and exceeds its administrative boundaries. This phenomenon has led to peri-urban regions which show urban and rural interactions. This study aims to analyze (1) the dynamics of LULC changes using cellular automata (CA), artificial neural network (ANN), and ANN-CA; (2) the influential factors (drivers); and (3) change probability in the period 2030 and 2045 for Cirebon’s peri-urban. We used logistic regression as quantitative approach to analyze the interaction of drivers and LULC changes. The LULC data derived from Landsat series satellite imagery in 1999-2009 and 2009-2019, validation of dynamic spatial model refers to 100 LULC samples. This research shows that LULC changes are dominated by built-up area expansion which causes plantations and agricultural land to decrease. The drivers have a simultaneous effect on LULC changes with r-square of 0.43, where land slope, distance from existing built-up area, distance from CBD, and accessibility are significant triggers. LULC simulation of CA algorithm is the best model than ANN and ANN-CA based on overall accuracy and overall accuracy (0.96, 0.75, 0.73 and 0.95, 0.66, 0.66 respectively), it reveals urban sprawl through the ribbon and compact development. The average probability of built-up area expansion is 0.18 (2030) and 0.19 (2045). If there is no intervention in spatial planning, this phenomenon will decrease productive agricultural lands in Cirebon's peri-urban.
... volcano, earthquake, tsunami, snow avalanches, etc.) could be forecasted through computer programs based on cellular technology [69,70,71,72]. Modeling wildlife propagation and urban in relation with food, environmental variables are also research fields for CA [73,74,75]. ...
Long-range radio transmissions open new sensor application fields, in particular for environment monitoring. For example, the LoRa radio protocol enables to connect remote sensors at distance as long as ten kilometers in a line-of-sight. However, the large area covered also brings several difficulties, such as the placement of sensing devices in regard to topology in geography, or the variability of communication latency. Sensing the environment also carries constraints related to the inlerest of sensing points in relation with a physical phenomenon. Thus criteria for designs are evolving a lot from the existing methods, especially in complex terrains. This thesis describes simulation techniques based on geography analysis to compute long-range radio coverages and radio characteristics in these situations. As radio propagation is just a particular case of physical phenomena, it is shown how a unified approach also allows to characterize the behavior of potential physical risks. The case of heavy rainfall and flooding is investigated. Geography analysis is achieved using segmentation tools to produce cellular systems which are in turn translated into code for high-þerformance computations. The thesis provides results from practical complex terrain experiments using LoRa which confirm the accuracy of the simulation, and scheduling characteristics for sample networks. Performance tables are produced for these simulations on current Graphics Processing Units (GPUs).
... Many natural disasters (e.g., volcano, earthquake, tsunami, snow avalanches, etc.) could be forecasted through computer programs based on cellular technology [18][19][20][21]. Modeling wildlife propagation and urban in relation to food, environmental variables are also research fields for CA [22][23][24]. ...
Long-range radio transmissions open new sensor application fields, in particular for environment monitoring. For example, the LoRa radio protocol enables connecting remote sensors at a distance as long as ten kilometers in a line-of-sight. However, the large area covered also brings several difficulties, such as the placement of sensing devices in regards to topology in geography, or the variability of communication latency. Sensing the environment also carries constraints related to the interest of sensing points in relation to a physical phenomenon. Thus, criteria for designs are evolving a lot from the existing methods, especially in complex terrains. This article describes simulation techniques based on geography analysis to compute long-range radio coverages and radio characteristics in these situations. As radio propagation is just a particular case of physical phenomena, it is shown how a unified approach also allows for characterizing the behavior of potential physical risks. The case of heavy rainfall and flooding is investigated. Geography analysis is achieved using segmentation tools to produce cellular systems which are in turn translated into code for high-performance computations. The paper provides results from practical complex terrain experiments using LoRa, which confirm the accuracy of the simulation, and scheduling characteristics for sample networks. Performance tables are produced for these simulations on current Graphics Processing Units (GPUs).
... Despite its advantages, zoning has 100 rarely been incorporated in urban modeling practices because its ability to significantly affect the 101 modeling outcomes has been generally disregarded or considered too difficult to demonstrate 102 (Onsted and Chowdhury, 2014). For example, in a study by Lahti (2008) the SLEUTH model, a 103 cellular automaton-based dynamic urban model, was successful in capturing bottom-up ecological 104 processes but could not adequately reproduce top-down phenomena due to its difficulty in 105 establishing a connection between bottom-up-oriented conversion rules and top-down urban SLEUTH model can simulate complex urban growth dynamics. The model can be calibrated using 117 historical urban expansion data to obtain the best possible coefficient combinations. ...
Dynamic spatial models are being increasingly used to explore urban changes and evaluate the social and environmental consequences of urban growth. However, inadequate representation of spatial complexity, regional differentiation, and growth management policies can result in urban models with a high overall prediction accuracy but low pixel-matching precision. Correspondingly, improving urban growth prediction accuracy and reliability has become an important area of research in geographic information science and applied urban studies. This work focuses on exploring the potential impacts of zoning on urban growth simulations. Although the coding of land-use types into distinct zones is an important growth management strategy, it has not been adequately addressed in urban modeling practices. In this study, we developed a number of zoning schemes and examined their impacts on urban growth predictions using a cellular automaton-based dynamic spatial model. Using the city of Jinan, a fast-growing large metropolis in China, as the study site, five zoning scenarios were designed: no zoning (S0), zoning based on land-use type (S1), zoning based on urbanized suitability (S2), zoning based on administrative division (S3), and zoning based on development planning subdivision (S4). Under these scenarios, growth was simulated and the respective prediction accuracies and projected patterns were evaluated against observed urban patterns derived from remote sensing. It was found that zoning can affect prediction accuracy and projected urbanized patterns, with the zoning scenarios taking spatial differentiation of planning policies into account (i.e., S2–4) generating better predictions of newly urbanized pixels, better representing urban clustered development, and boosting the level of spatial matching relative to zoning by land-use type (S1). The novelty of this work lies in its design of specific zoning scenarios based on spatial differentiation and growth management policies and in its insight into the impacts of various zoning scenarios on urban growth simulation. These findings indicate opportunities for the more accurate projection of urban pattern growth through the use of dynamic models with appropriately designed zoning scenarios.
... Also, the models are based on the application of single science like political or economical, etc. whereas cities can be better understood by a multi-disciplinary approach. Thus to sum up traditional models had limitations of dealing with a centralised structure, bad at handling dynamics, lack of detail, little usability, no flexibility and realism (cited in [9]). ...
... The developed countries where information pertaining to details variables and parameters are available, stochastic model is always the choice such as minimum density (MD), ordinary least square (OLS) and geographically weighted regression (GWR) are used for urban spatial structure, commuting and growth in US metropolitan areas [17]. Moreover, CA with fuzzy set [18] are applied for Sydney [19], and Neural Networks (NN) [20] for Gorgan City [9]. The deterministic models are seldom to be used unless limited information is gathered and simple approaches are selected [6]. ...
Urban growth always relates to the combination of natural increase in urban population and immigration of people to urban areas. Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. The process of urbanization exhibits a pattern in which the rate rises steeply in the early stages of industrialization, and tapers off gradually when the proportion reaches a saturation point. Finally, as most of populations become urbanized, urbanization falls to keep pace continuously with the economic development. In this study, “model” means how the data are manipulated by regression technique based on deterministic or stochastic technique. This study aims to compare these two different regression techniques for their spatial structure models on urban growth at the same specific study area. Moreover, both techniques will use the same datasets and their results will be analyzed to determine any similarity or difference between them. The work starts by producing an urban growth model by using the stochastic technique through geographical weighted regression (GWR). Later, the deterministic technique will be used based on radial basis functions (RBF). In general, the urban growth changes for both models are very similar. Thus, the deterministic technique can be considered as an alternative when details information are lacking.
This chapter presents a Land Use Cover Change (LUCC) model application developed for Greater Sydney. It aims to help decision making in the context of the strategic and spatial planning of Greater Sydney. To this end, the model simulates the dynamics of industrial, low density residential and medium to high density residential areas at spatial resolution of 100×100 m.
A series of three Land Use Maps at 30 m were specifically developed for the modelling exercise. They determine part of the exercise’s limitations, such as the model simplicity and the short time frame of the simulation (2006–2011–2016). Future efforts should focus on the simulation of population and job growth, exchanges between regions and the application of the model for scenario analysis and impact assessment.
All data of the modelling exercise are openly available at the CityData portal of the City Futures Research Centre.
1 Abstract
This study provides a model for the assessment of land use/cover change (LUCC) process in the district 9th of Mashhad, Iran, during 1986–1996. The problem is of high importance due to the rapid development and growth of the city both in size and population. However, the model is applicable for other cities, especially in developing countries. In this model, bee colony optimization (BCO) and cellular automata (CA) are used to manage the spatial distribution of land uses and Markov chain (MC) is applied to model the amount of land use changes. In addition, due to the importance of the cellular neighborhood in CA, a function was added to the model to take the role of neighborhood into consideration. By comparing the simulated map of 1996 with the actual one and by measuring the overall accuracy and kappa coefficient, it is concluded that the model is suitable to simulate LUCC. The proposed model is compared with the BCO‐CA model. The results of comparisons illustrated that the proposed BCO‐MC‐NDCA model outperforms the BCO‐CA model in forecasting the spatial distribution and the amount of land use changes in the studied area. The overall accuracy and kappa coefficient, compared with those of the BCO‐CA model, were improved by 7.64% and 0.11, respectively.
2 Recommendations for Resource Managers
BCO algorithm is presented in this paper for rule learning in the CA framework to model LUCC. In addition, Markov chain is applied to measure and control the amount of land use conversions. Furthermore, a function of neighboring effect is added to take advantage of the distance between cells in LUCC modeling. The experiments showed that the proposed approach is effective in predicting urban changes. The following implications could be realized based on the observations:
• While CA is a good model for cell conversion in urban maps, integrating that with MC could result in a more precise prediction of land use conversion.
• Since the neighboring cells are in close relation with each other, use of a neighboring decay effect, as a key factor for managers, is suggested to improve the predictions and decisions, significantly.
Es evidente en los estudios sobre procesos que se desenvuelven en el espacio urbano que el modelado emergente se ha afianzado como una metodología idónea para abordar procesos complejos que caracterizan a la dinámica socio-espacial. La mayoría de los antecedentes desde este enfoque han aplicado satisfactoriamente autómatas celulares para simular procesos tales como desplazamientos peatonales y automotor, cambios en el uso del suelo, surgimiento de nuevas centralidades, expansión urbana y segregación socioespacial. Sin embargo, existen escasos antecedentes de abordajes basados en esta metodología al espacio urbano latinoamericano y prácticamente inexistentes los desarrollos propios de aplicaciones informáticas que permitan ejecutar modelos de simulación emergente, es por ello que el objetivo del presente trabajo es presentar una aplicación computacional compatible con Sistemas de Información Geográfica
Summary
In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on:
? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity;
? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling;
? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth;
? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling;
? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process.
First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems.
Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades.
Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems.
Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro).
Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes.
Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery.
Summary
In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on:
? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity;
? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling;
? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth;
? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling;
? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process.
First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems.
Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades.
Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems.
Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro).
Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes.
Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery.
Summary
In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on:
? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity;
? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling;
? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth;
? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling;
? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process.
First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems.
Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades.
Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems.
Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro).
Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes.
Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery.
Summary
In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on:
? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity;
? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling;
? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth;
? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling;
? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process.
First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems.
Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades.
Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems.
Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro).
Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes.
Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery.
In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on:
1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity;
2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling;
3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth;
4. Developing and demonstrating an interpretable method for urban growth pattern modelling;
5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process.
First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems.
Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades.
Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems.
Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro).
Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes.
Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery.
Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and thus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network has been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand for urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001–2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty.
We explore the simulation of urban growth using complex systems theory and cellular automata (CA). The SLEUTH urban CA model was applied to two different metropolitan areas in Portugal, with the purposes of allowing a comparative analysis, of using the past to understand the dynamics of the regions under study, and of learning how to adapt the model to local characteristics in the simulation of future scenarios. Analysis of the two case studies show the importance of SLEUTH's self-modification rules in creating emergent urban forms. This behavior can help build an understanding of urban social systems through this class of CA.
This paper demonstrates a flexible implementation of rules to control the simulation of urban development of Sydney from 1971 to 1996 using a cellular automata model. Five key factors, including the self propensity for development and neighbourhood support, slope constraint, transportation support, terrain and coastal proximity attractions and urban planning support are introduced into the model in a spatially explicit format, which generated a realistic estimation of the extent and timing of Sydney's urban development. With the flexibility of rule implementation within the model, more rules can be added as new 'If-Then' statements to fine-tune the model, provided that a good understanding of the rule is maintained and accurate data are collected.
Cellular automata provide a high-resolution representation of urban spatial dynamics.Consequently they give the most realistic predictions of urban structural evolution, and in particular they are able to replicate the various fractal dimensionalities of actual cities. However, since these models do not readily incorporate certain phenomena like density measures and long-distance (as opposed to neighbourhood) spatial interactions, their performance may be enhanced by integrating them with other types of urban models.Cellular automata based models promise deeper theoretical insights into the nature of cities as self-organizing structures.
An understanding of the dynamic process of urban growth is a prerequisite to the prediction of land-cover change and the support of urban development planning and sustainable growth management. The spatial and temporal complexity inherent in urban growth requires the development of a new simulation approach, which should be process-oriented and have a strong interpretive element. In this paper the authors present an innovative methodology for understanding spatial processes and their temporal dynamics on two interrelated scales -- the municipality and project scale -- by means of a multistage framework and a dynamic weighting concept. The multistage framework is aimed at modelling local spatial processes and global temporal dynamics by the incorporation of explicit decisionmaking processes. It is divided into four stages: project planning, site selection, local growth, and temporal control. These four stages represent the interactions between top-down and bottom-up decisionmaking involved in land development in large-scale projects. Project-based cellular automata modelling is developed for interpreting the spatial and temporal logic between various projects that form the whole of urban growth. Use of dynamic weighting is an attempt to model local temporal dynamics at the project level as an extension of the local growth stage. As nonlinear function of temporal land development, dynamic weighting can link spatial processes and temporal patterns. The methodology is tested with reference to the urban growth of a fast growing city -- Wuhan, in the People's Republic of China -- from 1993 to 2000. The findings from this research suggest that this methodology can be used to interpret and visualise the dynamic process of urban growth temporally and transparently, globally and locally.
In this paper we describe a cellular automaton (CA) simulation model developed to predict urban growth as part of a project for estimating the regional and broader impact of urbanization on the San Francisco Bay area's climate. The rules of the model are more complex than those of a typical CA and involve the use of multiple data sources, including topography, road networks, and existing settlement distributions, and their modification over time. In addition, the control parameters of the model are allowed to self-modify: that is, the CA adapts itself to the circumstances it generates, in particular, during periods of rapid growth or stagnation. In addition, the model was written to allow the accumulation of probabilistic estimates based on Monte Carlo methods. Calibration of the model has been accomplished by the use of historical maps to compare model predictions of urbanization, based solely upon the distribution in year 1900, with observed data for years 1940, 1954, 1962, 1974, and 1990. The complexity of this model has made calibration a particularly demanding step. Lessons learned about the methods, measures, and strategies developed to calibrate the model may be of use in other environmental modeling contexts. With the calibration complete, the model is being used to generate a set of future scenarios for the San Francisco Bay area along with their probabilities based on the Monte Carlo version of the model. Animated dynamic mapping of the simulations will be used to allow visualization of the impact of future urban growth.
New forms of representation at a fine spatial scale, where units of space are
conceived as cells and populations as individual agents, are currently changing
the way we are able to simulate the evolution of cities and related systems. In
this paper, we review progress to date in this field. We show how these new
approaches are consistent with traditional urban models that have gone before
with the emphasis no longer being on spatial interaction but on the dynamics of
development and local movement. We first introduce a generic structure for
urban simulation based on ideas about spatial evolution as reaction and diffusion,
and then show how problems conceived in terms of cells, or agents, or both
enable new implementations of this generic model. We sketch the rudiments of
cellular automata (CA) which emphasises rules of development, and agent-based
models which focus on how agents respond to attributes of their environment
often encoded in cellular landscapes. We develop various exemplars based on
residential location to impress the way these approaches work. Three
applications are then presented at very different spatial scales: first pedestrian
movement at the building scale, then the evolution of systems of cities at a
country scale, and finally urban growth at the city scale. In developing these
approaches, we show how cellular and agent-based models have the potential for
explicitly incorporating spatial interaction and transportation which is their
current weakness. We conclude with proposals that formal policy analysis in this
domain should always be informed by more than one approach.
The Prisoner's Dilemma has long been considered the paradigm for studying the emergence of cooperation among selfish individuals. Because of its importance, it has been studied through computer experiments as well as in the laboratory and by analytical means. However, there are important differences between the way a system composed of many interacting elements is simulated by a digital machine and the manner in which it behaves when studied in real experiments. In some instances, these disparities can be marked enough so as to cast doubt on the implications of cellular automata-type simulations for the study of cooperation in social systems. In particular, if such a simulation imposes space-time granularity, then its ability to describe the real world may be compromised. Indeed, we show that the results of digital simulations regarding territoriality and cooperation differ greatly when time is discrete as opposed to continuous.
In this paper we present a Decision Support System developed to assist urban planners and policy makers to simulate and analyse alternative urban layouts, land uses, and growth patterns. The core of the system is a modelling and simulation shell allowing the user to specify Constrained Cellular Automata models of urban and regional systems. Unlike conventional Cellular Automata, these are defined with a relatively large neighbourhood and a large number of states, representing both human and natural land-uses. They forecast changes in land-use for small parcels on the basis of both the activities present in the local neighbourhood and the specific characteristics of the parcels themselves. Since each parcel affects every other within its neighbourhood, complex dynamics emerge. More traditional dynamic models --ideally spatial interaction based models-- control the overall dynamics of the cellular automata. In the DSS, a custom-built GIS is integrated in the modelling shell. It stores th...
Sustainable Urban Planning introduces the principles and practices behind urban and regional planning in the context of environmental sustainability. • This timely text introduces the principles and practice behind urban and regional planning in the context of environmental sustainability. • Reflects a growing recognition that cities, where the majority of humans now live, need to be developed in a sustainable way. • Weaves together the concerns of planning, capitalism, development, and cultural and environmental preservation. • Helps students and planners to marry the needs of the environment with the need for financial gain.
Forest remnants are vital for the overall heterogeneity and health of rural landscapes. However, deforestation is a significant process afflicting large numbers of agroforested regions of the world. The Maskoutains regional county municipality (RCM) in southern Quebec, Canada, experiences intense deforestation that has reached critical levels. The goal of this study is to develop a geographic cellular automata (GCA) to model land-use change in this region and test the influence of different management scenarios on the fate of the forested remnants. The GCA was built using a 100m cell size, a Moore neighborhood configuration, a 3 years time step resolution and probabilistic transition rules derived from the comparison of two land-use maps for the years 1999 and 2002. Four groups of management scenarios were tested: (1) status quo (SQ), (2) reduced deforestation (RD), (3) promotion of ligniculture (L), and (4) protection of forest connectivity (CONN). Results indicate that none of the scenarios succeed in maintaining the actual levels of forest area. However, certain scenarios (amongst the RD and CONN), significantly alter the loss of forest areas in the short to mid-term and delay the fragmentation, reduction, and isolation of forest patches.
This article presents a comprehensive model on conflict resolution that can be used for both solving actual water management conflicts and for guiding further research. The model is based on a literature study and integrates the several approaches found to help overcome the limitations of the individual approaches. The model consists of four parts. First, three possible sources of conflicts are inventoried and their interrelations are discussed. Second, the “basic mechanisms” for addressing the individual sources of conflict are presented. The third part consists of a short overview of the different conflict resolution methods and procedures that can be applied to make these mechanisms operational in practice. The fourth part of the model is a discussion of the contextual factors influencing conflicts and conflict resolution, with special emphasis on cultural factors. The article concludes with a discussion of the implications for the conflict resolution practice and for research.
The gradual filling in of communities—scatteration—provides flexibility in urban development. A quick filling in—compaction, as exemplified in the Year 2000 Plan—loads the community with the fashions of today, the obsolescences of tomorrow. It reduces the amount and interspersal of uncommitted space: vacant or containing removable (relatively old) structures. This chokes off the possibilities for adaptive reconstruction essential in our world of furious but unforeseeable change, and produces long-run inefficiencies, unwarranted blight, and segregation of the poor. A city which incorporates, channels, and increases scatteration encourages efficient adaptation to change. It also offers a supply of housing in which residential mixing of poor and middle class is feasible and large-scale segregation is not feasible.
In this research, a constrained cellular automata (CA) model based on Artificial Neural Network (ANN) is developed to simulate and forecast urban growth. As we know, many factors impact urban growth, and relationships among them are complex and non-linear. In geographic CA research, models with different rules and symbology have been developed to simulate those relationships. Yet, in previous done research, it is extremely time-consuming to find proper values of the parameters for CA models through general calibration procedures. As a solution in this research, a neural network can learn from the available urban land-use geospatial data, and deal with redundancy, inaccuracies, and noise. Knowledge and experiences can be easily learnt and stored for further simulation. In this ANN-Urban-CA model, a two-layer Back-Propagation (BP) neural network is integrated into a CA model to seek suitable parameters or weights that match the historical data. The parameters or weights required by CA simulation are automatically determined by the training/learning procedure of the neural network instead of by users, which is a very subjective process. Then each pixel's probability of urban transformation is determined by the neural network during simulation. Furthermore, a macro-scale socio-economic model is integrated in the CA model to generate the proper demand for urban space in each period in the future. Population is considered as the main factor impacting the demand of urban space. Using population forecasts as exogenous demands, the total number of new urban cells generated by the CA model is constrained. Beijing City is taken as a case study of this ANN-Urban-CA model. Urban growth in the period of 1980-2000 is simulated, and long-term (2001-2015) growth is forecast based on multiple socio-economic scenarios. In conclusion, this ANN-Urban-CA model can simulate and forecast the complex and non-linear spatial-temporal process of urban growth in a reasonably short computational time, with less subjective uncertainty.
Suburban sprawl, a relatively recent phenomenon, is among the most important urban policy issues facing contemporary cities. To date, a well-accepted rationale has not been settled on for explaining and managing the causes of sprawl. Our contention is that consideration of geography is essential—that geographical explanations offer much potential in informing the debate about sprawl. Similarly, spatial simulation could support sprawl-related research, offering what-if experimentation environments for exploring issues relating to the phenomenon. Sprawling cities may be considered as complex adaptive systems, and this warrants use of methodology that can accommodate the space-time dynamics of many interacting entities. Automata tools are well-suited to representation of such systems, but could be better formulated to capture the uniquely geographical traits of phenomena such as sprawl. By means of illustrating this point, the development of a model for simulating the geographic dynamics of suburban sprawl is discussed. The model is formulated using geographic automata and is used to develop three sprawl simulations. The implications of those applications are discussed in the context of exploring geographic explanations of sprawl formation and the potential for managing sprawl by geographic means.
Recent approaches to modeling urban growth use the notion that urban development can be conceived as a self-organizing system in which natural constraints and institutional controls (land-use policies) temper the way in which local decision-making processes produce macroscopic patterns of urban form. In this paper a cellular automata (CA) model that simulates local decision-making processes associated with fine-scale urban form is developed and used to explore the notion of urban systems as self-organizing phenomenon. The CA model is integrated with a stochastic constraint model that incorporates broad-scale factors that modify or constrain urban growth. Local neighborhood access rules are applied within a broader neighborhood in which friction-of-distance limitations and constraints associated with socio-economic and bio-physical variables are stochastically realized. The model provides a means for simulating the different land-use scenarios that may result from alternative land-use policies. Application results are presented for possible growth scenarios in a rapidly urbanizing region in south east Queensland, Australia.
One of the most potentially useful applications of cellular automata (CA) from the point of view of spatial planning is their use in simulations of urban growth at local and regional level. Urban simulations are of particular interest to urban and regional planners since the future impacts of actions and policies are critically important. However, urban growth processes are usually difficult to simulate.This paper addresses from a theoretical point of view the question of why to use CA for urban scenario generation. In the first part of the paper, complexity as well as other properties of digital cities are analysed. The role of the urban land use allocation factors is also studied in order to propose a bottom-up approach which integrates the land use factors with the dynamic approach of the CA for modelling future urban land use scenarios.The second part of the paper presents an application of an urban CA in the city of Dublin. A simulation for 30 years has been produced using a CA software prototype. The results of the model have been tested by means of the fractal dimension and the comparison matrix methods. The simulation results are realistic and relatively accurate confirming the effectiveness of the proposed urban CA approach.
To support the different phases of a policy making process aimed at changing land use, distinct types of land use studies are required. This paper focuses on exploratory land use studies and their role in the phase of formulating strategic policy objectives. Exploratory land use studies contribute to a transparent discussion on policy objectives by showing ultimate technical possibilities and consequences of imposing different priorities to agro-technical, food security, socio-economic and environmental objectives. A methodology is presented in which science-driven technical information is confronted with value-driven objectives under given values of exogenous variables (e.g. regarding population growth and requirements for agricultural produce). Land use scenarios are generated showing consequences of different priorities for objectives by using natural resources and technical possibilities in different ways. Applications of such an approach are given for the global, regional and farm level, each addressing specific questions and target groups. The paper focuses on the type of results these studies produce and their role in the societal and political debate on strategic land use policy and planning. It is concluded that if exploratory land use studies are carried out in true interaction with target groups, they may well contribute to the debate and learning on sustainable land use options and a purposeful identification of effective policy instruments in a next phase of the policy making process.
Assessing the economic impacts of urban land use transformation has become complex and acrimonious. Although community planners are beginning to comprehend the economic trade-offs inherent in transforming the urban fringe, they find it increasingly difficult to analyze and assess the trade-offs expediently and in ways that can influence local decision making. New and sophisticated spatial modeling techniques are now being applied to urban systems that can be used for assessing the probable spatial outcomes of given communal policies. Applying an economic impact assessment to the probable spatial patterns can provide to planners the tools needed to quickly assess scenarios for policy formation that might ultimately help inform decision makers.This paper focuses on the theoretical underpinnings and the practical application of an economic impact analysis submodel developed within the Land Use Evolution and Impact Assessment Modeling (LEAM) environment. The conceptual framework of LEAM is described, followed by an application of the model to the assessment of the cost of urban sprawl in Kane County, Illinois. The high spatial resolution of the approach allows for discerning the macro-level implications of micro-level behaviors. The results show that spatially explicit dynamic modeling has various conceptual advantages over other approaches to modeling urban dynamics, both from a theoretical and a practical point of view. However, model validation and the assessment of the uncertainty of large-scale spatial dynamic models deserve considerable future attention. The implications of land use change decisions on individual and communal costs are discussed and ways to improve the methodology are outlined.
The Land Transformation Model (LTM), which couples geographic information systems (GIS) with artificial neural networks (ANNs) to forecast land use changes, is presented here. A variety of social, political, and environmental factors contribute to the model's predictor variables of land use change. This paper presents a version of the LTM parameterized for Michigan's Grand Traverse Bay Watershed and explores how factors such as roads, highways, residential streets, rivers, Great Lakes coastlines, recreational facilities, inland lakes, agricultural density, and quality of views can influence urbanization patterns in this coastal watershed. ANNs are used to learn the patterns of development in the region and test the predictive capacity of the model, while GIS is used to develop the spatial, predictor drivers and perform spatial analysis on the results. The predictive ability of the model improved at larger scales when assessed using a moving scalable window metric. Finally, the individual contribution of each predictor variable was examined and shown to vary across spatial scales. At the smallest scales, quality views were the strongest predictor variable. We interpreted the multi-scale influences of land use change, illustrating the relative influences of site (e.g. quality of views, residential streets) and situation (e.g. highways and county roads) variables at different scales.
This paper introduces a hybrid automata model for testing ideas and hypotheses relating to urban gentrification dynamics. We focus on the agency of relocating households in dynamic property markets as the theoretical basis for construction of the model. The methodology employed makes use of hybridized cellular- and agent-automata that allow for representation of co-interaction among fixed and mobile entities in urban settings across multiple scales. Simulations run with the model are based on various hypotheses from gentrification theory and these hypotheses are tested in simulation by running the model through theory-informed scenarios. The usefulness of this scheme is demonstrated through application of the model to a historically under-invested area of Salt Lake City in Utah that is undergoing recent transformation. Our results show that the hybrid approach is useful in representing human behavior in complex adaptive urban systems. Moreover, our model proves to be a useful test-bed for studying gentrification.
The model presented, for the first time, in this paper can predict the spreading of fire in both homogeneous and inhomogeneous forests and can easily incorporate weather conditions and land topography. An algorithm has been constructed based on the proposed model and was used for the determination of fire fronts in a number of hypothetical forests, which were found to be in good agreement with the experience on fire spreading in real forests.
This paper reports on an attempt to combine neo-classical urban economic theory with complex systems methods. The innovative feature of our model from the point of view of conventional economic theory lies in its explicit treatment of spatial relationships and time sequence. From the perspective of raster or cellular GIS models of urban processes, the work is innovative in that it replaces the more usual heuristic cell-transition rules with micro-economic theory. The mix of modelling paradigms is not unproblematic, however, and we discuss the challenges encountered at this research frontier. These notwithstanding, our hybrid model has the potential to be used as a GIS-based laboratory for exploring micro-economic propositions, particularly those relating to urban processes that are path dependent. The version of the model reported simulates spatially equilibriated path dependent futures of a city governed by local development decisions that are at partial equilibria in the neo-classical sense. Two simulations are described which permit visual and economic exploration of (a) an explicitly spatial version of the economic theory of externalities and (b) a new theory of densification. The dual paradigm (Cellular Automata-neo-classical economics) leads to an interesting class of simulations in terms of stability. Economically our simulated cities become increasingly efficient, in terms of private and social product. The long-run economic equilibrium is achieved by many individually efficient negotiations based only on local information. There is no parallel long-run spatial equilibrium however. The spatial configuration of land uses is constantly shifting as a result of randomness in the land use bidding process. The spatial instability is, however, limited by the self-organised drive for greater overall economic efficiency. In economic terms, the model's spatial instability represents random re-allocation of land-use within a set of Pareto-efficient spatial configurations - an intriguing result that we intend to follow up in future work.
Cellular Automata (CA) have attracted growing attention in urban simulation because their capability in spatial modelling is not fully developed in GIS. This paper discusses how cellular automata (CA) can be extended and integrated with GIS to help planners to search for better urban forms for sustainable development. The cellular automata model is built within a grid-GIS system to facilitate easy access to GIS databases for constructing the constraints. The essence of the model is that constraint space is used to regulate cellular space. Local, regional and global constraints play important roles in affecting modelling results. In addition, 'grey' cells are defined to represent the degrees or percentages of urban land development during the iterations of modelling for more accurate results. The model can be easily controlled by the parameter k using a power transformation function for calculating the constraint scores. It can be used as a useful planning tool to test the effects of different urban development scenarios.
This paper presents a new method to simulate the evolution of multiple land uses based on the integration of neural networks and cellular automata using GIS. Simulation of multiple land use changes using cellular automata (CA) is difficult because numerous spatial variables and parameters have to be utilized. Conventional CA models have problems in defining simulation parameter values, transition rules and model structures. In this paper, a three-layer neural network with multiple output neurons is designed to calculate conversion probabilities for competing multiple land uses. The model involves iterative looping of the neural network to simulate gradual land use conversion processes. Spatial variables are not deterministic because they are dynamically updated at the end of each loop. A GIS is used to obtain site attributes and training data, and to provide spatial functions for constructing the neural network. The parameter values for modelling are automatically generated by the training procedure of neural networks. The model has been successfully applied to the simulation of multiple land use changes in a fast growing area in southern China.
As urban planning moves from a centralized, top-down approach to a decentralized, bottom-up perspective, our conception of urban systems is changing. In Cities and Complexity, Michael Batty offers a comprehensive view of urban dynamics in the context of complexity theory, presenting models that demonstrate how complexity theory can embrace a myriad of processes and elements that combine into organic wholes. He argues that bottom-up processes - in which the outcomes are always uncertain -- can combine with new forms of geometry associated with fractal patterns and chaotic dynamics to provide theories that are applicable to highly complex systems such as cities.
Batty begins with models based on cellular automata (CA), simulating urban dynamics through the local actions of automata. He then introduces agent-based models (ABM), in which agents are mobile and move between locations. These models relate to many scales, from the scale of the street to patterns and structure at the scale of the urban region. Finally, Batty develops applications of all these models to specific urban situations, discussing concepts of criticality, threshold, surprise, novelty, and phase transition in the context of spatial developments. Every theory and model presented in the book is developed through examples that range from the simplified and hypothetical to the actual. Deploying extensive visual, mathematical, and textual material, Cities and Complexity will be read both by urban researchers and by complexity theorists with an interest in new kinds of computational models.
There are indications that the current generation of simulation models in practical,
operational uses has reached the limits of its usefulness under existing specifications.
The relative stasis in operational urban modeling contrasts with simulation efforts in
other disciplines, where techniques, theories, and ideas drawn from computation and
complexity studies are revitalizing the ways in which we conceptualize, understand,
and model real-world phenomena. Many of these concepts and methodologies are
applicable to operational urban systems simulation. Indeed, in many cases, ideas from
computation and complexity studies—often clustered under the collective term of
geocomputation, as they apply to geography—are ideally suited to the simulation of
urban dynamics. However, there exist several obstructions to their successful use in
operational urban geographic simulation, particularly as regards the capacity of these
methodologies to handle top-down dynamics in urban systems.
This paper presents a framework for developing a hybrid model for urban geographic
simulation and discusses some of the imposing barriers against innovation in this
field. The framework infuses approaches derived from geocomputation and
complexity with standard techniques that have been tried and tested in operational
land-use and transport simulation. Macro-scale dynamics that operate from the topdown
are handled by traditional land-use and transport models, while micro-scale
dynamics that work from the bottom-up are delegated to agent-based models and
cellular automata. The two methodologies are fused in a modular fashion using a
system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of
residential location has been developed with a view to hybridization. The model
mixes cellular automata and multi-agent approaches and is formulated so as to
interface with meso-models at a higher scale.
Curso de: Ecología (01PT029) Introducción a los sistemas dinámicos utilizados como modelos de simulación en el área de estudios medioambientales. Además de presentar los conceptos básicos, ilustra al lector sobre la mecánica para la construcción de modelos, así como un gran volumen de ejercicios.
Despite the recognition of cellular automata (CA) as a flexible and powerful tool for urban growth simulation, the calibration of CA had been largely heuristic until recent efforts to incorporate multi-criteria evaluation and artificial neural network into rule definition. This study developed a stochastic CA model, which derives its initial probability of simulation from observed sequential land use data. Furthermore, this initial probability is updated dynamically through local rules based on the strength of neighbourhood development. Consequentially the integration of global (static) and local (dynamic) factors produces more realistic simulation results. The procedure of calibrated CA can be applied in other contexts with minimum modification. In this study we applied the procedure to simulate rural-urban land conversions in the city of Guangzhou, China. Moreover, the study suggests the need to examine the result of CA through spatial, tabular and structural validation.
The contemporary structure of scientific activity, including the publication of papers in academic journals, citation behaviour, the clustering of research into specialties and so on has been intensively studied over the last fifty years. A number of quantitative relationships between aspects of the system have been observed. This paper reports on a simulation designed to see whether it is possible to reproduce the form of these observed relationships using a small number of simple assumptions. The simulation succeeds in generating a specialty structure with 'areas' of science displaying growth and decline. It also reproduces Lotka's Law concerning the distribution of citations among authors. The simulation suggests that it is possible to generate many of the quantitative features of the present structure of science and that one way of looking at scientific activity is as a system in which scientific papers generate further papers, with authors (scientists) playing a necessary but incidental role. The theoretical implications of these suggestions are briefly explored.
We propose a multiagent model in which developers and suppliers negotiate and generate proposals for developing a site, given multiple candidate locations in an urban area. In the model, the developer takes the initiative and coordinates the negotiation process with the aim of obtaining commitments from suppliers to open an outlet at a location. In this process, suppliers indicate their preferences and make yes - no decisions to participate in a proposal. We formulate and investigate two alternative negotiation protocols and various possible decision strategies for the developer and supplier agents. The model is applied to a hypothetical study area to investigate long-term dynamics as a function of the choice of protocol and strategies. We show that the model is capable of reproducing the typical hierarchical structure of real retail systems. Furthermore, it appears that the choice of protocol and strategy has an impact on the degree of spatial clustering of outlets as well as on the performance of each individual supplier. The choice of strategy is particularly critical for relatively weak suppliers. We conclude that the multiagent model is useful for planners, developers, and suppliers to explore the impacts their choices have on outcomes, and we identify promising avenues of future research.
Most cellular automata (CA) urban models assume densities to be uniform for all cells. This is not true in real cities because densities vary substantially from city to city and from urban center to periphery areas. Development density, which affects urban form, is an important factor in urban planning. The authors present a CA model that incorporates density gradient in the simulation of urban development for different urban forms. Development density is obtained from density-decay functions and assigned to the cells when they are converted into developed cells according to CA transition rules. The model, which is based on the concept of 'grey cells', can be used as a planning model to explore various combinations of urban forms and development densities. The authors also evaluate and compare the development patterns generated by different density gradients. It is found that development scenarios with high-density development can significantly reduce encroachment on agricultural land and other important environmentally sensitive areas.
A cellular automaton is specified to give a spatially detailed represenation of the evolution of urban land-use patterns. Cell states represent land uses, and transition rules express the likelihood of a change from one state to another as a function both of existing land use in the 113-cell neighbourhood of the cell and of the inherent suitability of the cell for each possible use. The model is used to simulate the land-use pattern of Cincinnati, Ohio. The simulation results are realistic and sensitivity analysis shows that the predictions of the model are relatively accurate and reproducible, thus suggesting that cellular automata - based models may be useful in a planning context.
This paper presents a new cellular automata (CA) model which uses artificial neural networks tlsb>for both calibration and simulation. A critical issue for urban CA simulation is how to determine parameter values and define model structures. The simulation of real cities involves the use of many variables and parameters. The calibration of CA models is very difficult when there is a large set of parameters. In the proposed model, most of the parameter values for CA simulation are automatically determined by the training of artificial neural networks. The parameter values from the training are then imported into the CA model which is also based on the algorithm of neural networks. With the use of neural networks, users do not need to provide detailed transition rules which are difficult to define. The study shows that the model has better accuracy than traditional CA models in the simulation of nonlinear complex urban systems.
Urban sprawl in Europe : the ignored challenge
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