BUDEM: an urban growth simulation model using CA for Beijing metropolitan area (original) (raw)
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BUDEM: an urban growth simulation model using CA for Beijing metropolitan area
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008
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, namely planned urban form; (3) Simulating urban growth scenarios of 2049 in different policies.
Applied Geography, 2006
Spatially explicit urban expansion models that can trace urban development in the past and predict the expansion scenarios in the future are indispensable for examining urban planning policies. This paper demonstrates a new urban expansion scenario (UES) model by coupling one ''bottom-up'' cellular automata (CA)-based model and one ''top-down'' system dynamics (SD)-based model. By implementing the UES model in Beijing, the urban evolution from 1991 to 2004 was simulated and the UESs from 2004 to 2020 were predicted. The results suggest that a dilemma of urban expansion versus limited water resource and environment deterioration exists. Dealing with such a dilemma remains a challenge for the local government. r
CELLULAR AUTOMATA -DYNAMIC MODEL FOR URBAN GROWTH BAQUBAH CITY
This paper analyzes land use change in Baqubah city the capital of Iraq's Diyala Governorate, in the period from 2004 to 2010, using a cellular automata model. No previous research about this study in Iraq. The simulation experiment was carried out in the Dinamica EGO platform to 2030 year and the results revealed a constrained urban sprawl. The simulation outputs were validated using a multi-resolution procedure based on a fuzzy similarity index 81.5%, and showed a satisfactory fitness in relation to the historical reference data. The simulation scenario for the year 2030 showed an increase in the medium residential, high residential and road by (23%, 73%, and 11% respectively). As well as a significant decrease the orchard, vegetation, water, open area and mix by (41%, 36%, 20%, 23%, and 12% respectively) INTRODUCTION Cellular automata (CA) were introduced by Ulan and Neumann in 1940 and since 1980 numerous models have been developed for simulating urban growth [1]. CA are defined as discrete dynamics systems, represented by a grid of cells, in which local interconnected relationships exhibit global changes [2]. Generally, the state of each cell depends on the value of the cell on its previous state as well as the values of its neighbors according to some transition rules. These rules affect the urban growth, indicating environmental and socioeconomic support or limitations. Therefore, the bottom up approach implemented in CA relies on the simulation of local actions that progressively create the global emergent structure [3]. CA deals with non-linearity of urban structures and the iterative process leads to produce fractal patterns, which are common characteristics in an urban environment [4]. The applications of CA in urban growth can be classified into: 1) theoretical model developments and 2) applied Urban Growth Prediction Models (UGPMs) in real data. The first category, which developed in early years of CA, includes theoretical developments of CA models in urban simulation [5]. Subsequently, these theoretical approaches found real world implementations. A large number of applications have incorporated CA for Urban Growth Prediction Models UGPM development using real data [6]. A combination of CA with Markov models has also appeared in multiple studies [7]. A Markov model can not only explain the conversion among land uses, but also calculate the transfer rates among different types. Multi-criteria evaluation techniques and weight of evidence [8,9] have been used for estimating the importance of qualitative and quantitative drivers within the CA modelling framework, as shown in Figure (1) [10] .The dynamic transitions from one form of land use to another occur over a period.
Cellular Automata Modelling Approach for Urban Growth
Reviews in Agricultural Science, 2018
The urban expansion is always an inevitable issue in our human history and has become more intensive during a past several decades with explosive population growth of the world. The urban expansion is sometimes praised as a result of economic development, but at the same time, it might induce serious problems such as traffic jams, soaring price of real estate, trash problems, and shortage of natural resources. Thus, it is one of serious concerns many countries are facing. Therefore, a lot of modeling techniques have been introduced, discussed and developed for this problem (Batty, 1971; Forrester, 1969, Makse et al., 1995). Among them, cellular automaton (CA) is one of most dominant techniques, because both of the urban expansion and the CA inherently and similarly include complexity in their behavior. CA was initially introduced by John von Neumann and Stanislaw Ulam as a simple model for biological process such as self-production (Burks, 1971). CA may express any non-ABSTRACT Cellular Automaton (CA) consists of a regular grid cells of which states change according to simple repetitive rules regulated by their contiguous and adjacent cells, which often expresses an unexpected complexity. Thus, CA is one of the major techniques to imitate and/or assess complex behaviors of natural systems. CA can be applied to physical and biological phenomena, such as turbulence in fluid, patterns of biological growth, and wildfire, and also some human-induced phenomena such as urban growth that is the main target of this review. In 1970s, cellular approach was initially adopted in geography, showing the clue to the urban growth application. To overcome the limitation or constraints the conventional standard cell-space models inherently include, alternative formulations were theoretically proposed in 1980s. And the pioneering work applied to realistic cities was conducted in 1990s. Subsequently, numerous models have been presented by relaxing original rules to express reality and by introducing some additional techniques such as geographical information system and system dynamics, thus far. This paper reviews 87 published cellular automata studies on urban growth simulations, urban land use change assessments, urban planning and related information from 18 countries, and examines the characteristics of each relaxation method. In addition, the scale problems are frequently discussed in the validation of the CA model is addressed.
2009
In the context of rapid urbanization, accurate assessment of urban growth has become increasingly necessary for understanding environmental impacts and supporting urban planning toward a sustainable development. In this paper, we present an integrated system dynamics and cellular automata model not only in socio-economic driving forces analysis but also in urban spatial pattern evaluation. Shanghai city in China is selected as a case to fulfill the tasks. The major findings are summarized as follows: (1) the integrated model is proved to be competent in monitoring and projecting the dynamics of urban growth.
Sciprints, 2016
Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use we...
2011
In recent years, simulating urban growth using cellular automata (CA) has attracted extensive attention for investigating the dynamic urban system. Distinguished from the pure CA, the CA model applied for urban growth simulation should not only include the neighbourhood influence, but other factors related with urban developments. We proposed the term 'complex constrained CA' (CC-CA), which integrates constraints of neighbourhood, macro socioeconomic, spatial and institutional types. Particularly, the constructing constrained zoning planning, as an institutional constraint, is introduced into the CC-CA model. We applied the CC-CA model for the Beijing Metropolitan Area. Parameters of constraints are calibrated for various historical phases of Beijing, with which the baseline scenario with current developing trend is build to simulate the long-term urban form of Beijing. In addition, we set the planning controlled scenario via adjusting institutional constraints' parameters, and compare the results with the baseline scenario. The impact of institutional constraints on future urban form is then visualised for policy makers. The CC-CA is proved to have its policy implications for Beijing.
Cellular Automata Based Model of Urban Spatial Growth
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) 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
Cellular Automata in Urban Planning and Development
2012
Urban planning is a complex process as urban system is the resultant of interactions between its subsystems. Knowing future projection of physical urban development can play a vital role in successful urban planning and development. Therefore, it is important to have a good understanding of the interactions between urban system components to correctly predict future urban growth. This paper proposes the Celullar Automata (CA) model to predict the future of urban development. Due to shortcomings of traditional modeling methods which are generally static, linear and are based on simple systems theory, it is expected that the proposed CA model which is dynamic and nonlinear will provide better understanding of the urban system and provide better prediction of urban growth.