A REVIEW ON ACCURACY AND UNCERTAINTY OF SPATIAL DATA AND ANALYSES WITH SPECIAL REFERENCE TO URBAN AND HYDROLOGICAL MODELLING <s> Cellular Automata Modelling <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 Modelling <s> 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. <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 Modelling <s> 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. <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 Modelling <s> 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 develo... <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 Modelling <s> Abstract An emerging branch of geocomputing involves the modelling of spatial processes. A variety of techniques are being used, the most important being traditional regionalized system dynamics approaches, multi-agent systems, and cellular automata (CA). The techniques are frequently combined to model processes operating at different spatial scales. Urban and regional models based on CA give good representations of the spatial dynamics of land use. In a current application, a cellular model of The Netherlands at 500 m resolution is driven by a macro-scale dynamical spatial interaction model defined on 40 economic regions; this model is in turn driven by national planning projections and policy goals. Given the national totals, the macro-scale model generates regional demands for population and a number of economic activities. These demands are translated into demands for cell space, which the CA then attempts to locate. In turn, information on conditions at the cellular level, such as the quantity and quality of land available to various activities and actual densities at the cellular scale, are returned to the regional model to modify parameter values there. Linking the two models operating at the two scales improves the performance of both. The results of high-resolution modelling of spatial dynamics raise several methodological issues. One of the most pressing concerns evaluation of the results. Another issue concerns predictability. To the extent that these models capture the evolving nature of real cities and regions, they cannot be strictly predictive. <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 Modelling <s> Preface. Acknowledgements. Foreword. 1. Introduction to urban Geosimulation. 1.1 A new wave of urban geographic models is coming. 1.2 Defining urban Geosimulation. 1.3 Automata as a basis of Geosimulation. 1.4 High-resolution GIS as a driving force of Geosimulation. 1.5 The origins of support for Geosimulation. 1.6 Geosimulation of complex adaptive systems. 1.7 Book layout. 2. Formalizing Geosimulation with Geographic Automata Systems (GAS). 2.1 Cellular Automata and Multi-Agent Systems - Unite! 2.2 Geographic Automata Systems (GAS). 2.3 GAS as a tool for modelling Complex Adaptive Systems. 2.4 From Gas to software environment for urban modelling. 2.5 Object Based Environment for Urban Simulation (OBEUS) - a minimal implementation of GAS. 2.6 Universality of GAS. 2.7 Verifying GAS models. 3. System Theory, Geography, and Urban Modelling. 3.1 The basic notions of system theory. 3.2 The 1960s, geography meets system theory. 3.3 'Stocks and flows' urban modelling. 3.4 Critics of comprehensive modelling. 3.5 What next? Geosimulation of collective dynamics! 4. Modelling urban land usage with cellular automata. 4.1 Introduction. 4.2 Cellular Automata as a framework for modelling complex spatial systems. 4.3 Urban Cellular Automata. 4.4 From Markov models to urban Cellular Automata. 4.5 Integration of the CA and Markov approaches at a regional level. 4.6 Conclusions. 5. Modelling Urban Dynamics with Multi-Agent Systems. 5.1 Introduction. 5.2 MAS as a tool for modelling complex human-driven systems. 5.3 Interpreting agency. 5.4 Urban agents, urban agency, and multi-agent cities. 5.5 Agent behaviour in urban environments. 5.6 General models of agents' collectives in urban interpretation. 5.7 Abstract MAS models of urban phenomena. 5.8 Real-world agent-based simulations of urban phenomena. 5.9 MAS models as planning and assessment tools. 5.10 Conclusions. 6. Finale: Epistemology of Geosimulation. 6.1 Universal questions. 6.2 The future of Geosimulation. Bibliography. Index. <s> BIB006
Earlier CA models viewed space as a lattice of identical cells in the 1960's and the 1970's in modelling urban land uses. Cities were considered as spatial distributed systems. BIB001 developed life CA models. The model was born on the idea of Conway's game of life. BIB002  . CA consists of five basic elements namely, cell space, cell state, cell neighbourhood, transition rules and time. Several approaches had been identified in defining the basic elements of a CA to simulate the process of urban development  BIB004 Takeyama & Couelelis, 1997; BIB003 . The space in which an automaton exists is called a cell space or lattice. Cell states most commonly represent land use but may be used to represent any spatially distributed variable for the purpose of modelling its spatial dynamics BIB005 . Each cell in CA can take only one state from a set of state at any one time and it can be a number that represents a property  . Transition rules define how the state of one cell changes in response to its current state and the state of its neighbour  . These rules determine how CA states adapt over time and can be designed using any combination of conditional statements or mathematic operators BIB006 . The cellular neighbourhood of a cell consists of the surrounding (adjacent) cells. The neighbourhood is outlined by the grid (cell space) in which the CA are located.  defined time as the temporal dimension in which a CA exists. Thus the states of all cells are updated simultaneously at all iterations over time.