A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Cellular Automata (CA) Model <s> Since mathematical models came to be applied to problems of architectural and urban form, new concepts based on predicting large-scale structure from local rules have emerged through insights originating in computation and biology. The clearest of these are computer models based on cellular automata (CA) and their recent generalization in evolutionary biology and artificial life. Here we show how such models can be used to simulate urban growth and form, thus linking our exposition to the longer tradition of ideas in studies of built form emanating from the â€˜Cambridge Schoolâ€™. We first review developments of CA in general and then in urban systems in particular. We propose a general class of CA models for urban simulation and illustrate two simple applications, the first a simulation of the development of the historical â€˜cellâ€™ city of Savannah, Georgia, the second, a generic hypothetical application. We then show how this generic model can be used to simulate the growth dynamics of a suburban area of a mid-sized North American city, thus illustrating how this approach provides insights into the way microprocesses lead to aggregate development patterns. <s> BIB001 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Cellular Automata (CA) Model <s> "Prior research developed a cellular automaton model, that was calibrated by using historical digital maps of urban areas and can be used to predict the future extent of an urban area. The model has now been applied to two rapidly growing, but remarkably different urban areas: the San Francisco Bay region in California and the Washington/Baltimore corridor in the Eastern United States. This paper presents the calibration and prediction results for both regions, reviews their data requirements, compares the differences in the initial configurations and control parameters for the model in the two settings, and discuses the role of GIS in the applications." <s> BIB002 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Cellular Automata (CA) Model <s> We use an urban growth model, closely coupled with a land transition model, to simulate future urban growth in the Atlanta metropolitan area, one of the fastest growing metropolises in the United States during the past three decades. We calibrate the model with historical data that are extracted from a time series of satellite images. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the continued growth trend by maintaining unchanged the current conditions. The second scenario is to project the growth trend by taking into consideration road development and environmental protection. The third scenario is to simulate the development trend by slowing down growth and changing growth pattern. The first two scenarios demonstrate that unchecked urban growth would result in the displacement of almost the entire natural vegetation and all of the open space in the metro area. In contrast, the result from the third scenario shows that much more greenness and open space, including buffer zones of large streams and lakes, could be preserved. Accordingly, the last scenario should be the most desirable for the future urban growth of Atlanta. We also examine the model's effectiveness as applied to the Atlanta area and suggest future research directions for more accurate simulations. <s> BIB003 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Cellular Automata (CA) Model <s> Errors and uncertainties are important issues in most geographical analyses and modelling processes. It is well known that errors popularly exist in GIS data. A variety of methods have been developed to measure error propagation in GIS. Cellular automata (CA) have been increasingly used for modelling geographical phenomena, such as urban development. Urban simulation frequently involves the inputs of a large set of spatial variables from GIS. The errors of data source in GIS can propagate through CA modelling processes. Moreover, CA models themselves also have modeling uncertainties because they are just the approximation of reality. These uncertainties also have impacts on the outcome of urban simulation. Identification and evaluation of these errors and uncertainties are crucial for the understanding and utilization of the simulation results of CA. This paper attempts to address these important issues which have not been well dealt with before. This can help urban modelers and planners to understand more clearly the meanings and implications of CA modelling. <s> BIB004 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Cellular Automata (CA) Model <s> Abstract This paper proposes a Cellular Automata (CA) model to evaluate the urbanization patterns arising from the regulation of urban growth on paddy lands in the Colombo Metropolitan Region (CMR). Most of the historic map data available for the CMR before 1990 are temporally sporadic and spatially incomplete. As an alternative to maps, classiﬁed remote sensing data are used to analyze the urbanization process. Logistic regression is applied to derive factors of urbanization and the various relationships among them. The relation between 'urban' and 'non-urban' serves as an explanatory variable. The factors explaining that relationship are calculated by exploratory logistic regression analyses. The probability calculated from the statistical model is used for CA transition with a random number. Several growth patterns are simulated based on a range of transition thresholds to test the CA model. Status quo growth and several growth control scenarios are simulated for the period from 1987 to 2002 based on an optimum threshold. The simulation result of the status quo growth is evaluated with several evaluation methods. The level of agreement between the estimated result from the status quo model and the actual data is 62%, while the multi-scale goodness-of-ﬁt method produces highly accurate values for the given range of resolutions. <s> BIB005 </s> A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Cellular Automata (CA) Model <s> Human population continues to aggregate in urban centers. This inevitably increases the urban footprint with significant consequences for biodiversity, climate, and environmental resources. Urban growth prediction models have been extensively studied with the overarching goal to assist in sustainable management of urban centers. Despite the extensive body of research, these models are not frequently included in the decision making process. This review aims on bringing this gap by analyzing results from a survey investigating developer and user perceptions from the modeling and planning communities, respectively. An overview of existing models, including advantages and limitations, is also provided. A total of 156 manuscripts is identified. Analysis of aggregated statistics indicates that cellular automata are the prevailing modeling technique, present in the majority of published works. There is also a strong preference for local or regional studies, a choice possibly related to data availability. The survey found a strong recognition of the models’ potential in decision making, but also limited agreement that these models actually reach that potential in practice. Collaboration between planning and modeling communities is deemed essential for transitioning models into practice. Data availability is considered a stronger restraining factor by respondents with limited algorithmic experience, which may indicate that model input data are becoming more specialized, thus significantly limiting wide-spread applicability. This review assesses developer and user perceptions and critically discusses existing urban growth prediction models, acting as a reference for future model development. Specific guidelines are provided to facilitate transition of this relatively mature science into decision making activities <s> BIB006
Invented by John Von Neumann, cellular automata are discrete dynamical systems that model complex behaviour based on simple, local rules animating cells on a lattice. In CA, space is represented by a uniform M-dimensional grid of cells (M = 1,2,..) with each cell containing some data. Among all developed Urban Growth Models, CA models perform well in simulating urban development than conventional mathematical models. CA are able to predict urban growth based on the assumption that past urban development affects future patterns through local interactions among land uses (Sante, et al.,2010) . CA simplifies the simulation of complex systems. CA are appropriate for urban modelling as the process of urban spread is entirely local in nature. Thus CA models are outstanding depicters of urban dynamics BIB005 . Models based on CA are impressive due to their technological evolution in connection to urban application. Developing a CA model involves rule definition and calibration to produce results consistent with historical data and future prediction with set rules BIB001 BIB002 BIB003 . Many CA based urban growth models are documented in the literatures BIB006 . CA model reduces spaces into square grids  . CA implements the defined transition rules in recursive form to match the spatial pattern. CA models are usually designed based on individual preference and application requirements with transition rules being defined in an adhoc manner BIB004 . Most of the developed CA models need intensive computation to select the best parameter values for accurate modelling.