Theoretical modeling and simulation are two common approaches to the study of land use change. While theoretical models are often limited by the simplicity in their assumptions, simulations are mostly low in generalization due to their specificity in applications. The integration of these two approaches will thus help us to model more closely the reality by having a firm theoretical foundation for real-life simulation. In the present research, a theory-based cellular automata (CA) simulation is proposed to simulate the changes of land uses. The land use change generally involves urban development and urban redevelopment processes. Rent gap theory is selected as the theoretical model to describe the mechanism of urban redevelopment. On the other hand, relevant conditions related to urban development are incorporated into the simulation of new development because rent gap theory is inadequate in accounting for all mechanisms that are working together to shape the pattern of land use change. In order to perform CA simulation under random fluctuation in time, stochastic formulation of the rent gap theory is first made in the form of a build up value equation to seek for the time of appearance of rent gap. The time of urban redevelopment depends on the appearance of rent gap while the time of urban development depends on the conditions of local characteristics and government policy. To substantiate the theoretical arguments, the land use change of Hong Kong in the period of 1966--2001 is selected as a case study. For the pattern of future development of Hong Kong, the simulation is carried out up to 2016. It also serves as a basis for the evaluation of the integrative utilization of a theoretical model and a simulation method. This research demonstrates that the appropriate integration of a theoretical model and a simulation method is beneficial to the study of land use change. / Tsang Shun Wai. / "July 2007." / Advisers: Yee Leung; Tung Fung. / Source: Dissertation Abstracts International, Volume: 69-01, Section: A, page: 0334. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 215-241). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

In recent years there has been large increase in the amount of digital mapping data of landscapes and urban environments available through satellite imaging. This digital information can be used to develop wind flow simulators over large cities or regions for various purposes such as pollutant transport control, weather forecasts, cartography and other topographical analysis. It can also be used by architects for city planning or by game programmers for virtual reality and similar applications. But this data is massive and contains a lot of redundant information such as trees, cars, bushes, etc. For many applications, it is beneficial to reduce these huge amounts of data through elimination of unwanted information and provide a good approximate model of the original dataset. The resultant dataset can then be utilized to generate surface grids suitable for CFD purposes or can be used directly for real-time rendering or other graphics applications. Digital Elevation Model, DEM, is the most basic data type in which this digital data is available. It consists of a sampled array of elevations for ground positions that are regularly spaced in a Cartesian coordinate system. The purpose of this research is to construct and test a simple and economical prototype which caters to image procesing and data reduction of DEM images through noise elimination and compact representations of complex objects in the dataset. The model is aimed at providing a synergy between resultant image quality and its size through the generation of various levels of detail. An alternate approach using the concepts of standard deviation helps in achieving the desired goal and the results obtained by testing the model on Salt Lake City dataset verify the claims. Thus, this thesis is aimed at DEM image processing to provide a simple and compact representation of complex objects encountered in large scale urban environment datasets and reduce the size of the dataset to accommodate efficient storage, computation, fast transmission across networks and interactive visualization.

This study used two modelling approaches to predict future urban landscape for the Baltimore-Washington metropolitan areas. In the first approach, we implemented traditional SLEUTH urban simulation model by using publicly available and locally-developed land cover and transportation data. Historical land cover data from 1996, 2001, 2006, and 2011 were used to calibrate SLEUTH model and predict urban growth from 2011 to 2070. SLEUTH model achieved 94.9% of overall accuracy for a validation year of 2014. For the second modelling approach, we predicted future county-level population (e.g., 2050) using historical population data and time-series forecasting. We then used future population projection of 2050, aided by strong population-imperviousness statistical relationship (R2, 0.78-0.86), to predict total impervious surface area for each county. These population-predicted total impervious surface areas were compared to SLEUTH model output, at the county-aggregated spatial scale. For most counties, SLEUTH generated substantially higher number of impervious pixels. An annual urban growth rate of 6.24% for SLEUTH model was much higher than the population-based approach (1.33%), suggesting a large discrepancy between these two modelling approaches. The SLEUTH simulation model, although achieved high accuracy for 2014 validation, may have over-predicted urban growth for our study area. For population-predicted impervious surface area, we further developed a lookup table approach to integrate SLEUTH out and generated spatially explicit urban map for 2050. This lookup table approach has high potential to integrate population-predicted and SLEUTH-predicted urban landscape, especially when future population can be predicted with reasonable accuracy. / Master of Science

Urban development planning efforts are challenged to enhance coevolving spatial and socioeconomic systems that exist and interact at multiple scales. While network and simulation sciences have created new tools and theories suitable for urban studies, models of development are not yet suitable for local and regional development planning. A case study of the City of Roanoke, Virginia, grounded network development theories of scaling, engagement, and collective perception function, as well as network forms. By advancing urban development network theory, frameworks for urban simulation like agent-based models take more coherent shape. This in turn better positions decision-making and planning practitioners to adapt, transform, or renew local network-oriented development systems, and conceptualize a framework for computational urban development planning for regions and localities. / Ph. D.

O desenvolvimento das cidades freqüentemente propicia o aparecimento de empreendimentos de grande porte que, juntamente com o aumento da população e dos veículos, causam impactos nos sistemas de transportes. Dado que há fortes indícios que a localização e o porte destes empreendimentos, aqui denominados de pólos geradores de viagens (ou PGVs), seja influenciado pela presença de outros PGVs, o objetivo deste trabalho foi avaliar esta hipótese em uma cidade média brasileira, através de duas vertentes da análise espacial: a estatística espacial e a modelagem espacial baseada na comparação de informações oriundas de diferentes entidades espaciais. Foram utilizados conceitos de análise exploratória de dados espaciais, tais como o índice e o diagrama de espalhamento de Moran, para a delimitação de regiões similares, divididas de modo a indicar pontos de associação espacial, em relação a uma variável analisada. Em seguida, a modelagem espacial foi feita com uma adaptação da técnica de cellular automata, usando redes neurais artificiais. Dois modelos foram propostos e aplicados em um estudo de caso na cidade de Campinas, São Paulo, com base em diferentes variáveis. No primeiro modelo, somente variáveis representativas da ocupação comercial foram consideradas, enquanto que, no segundo modelo, foram introduzidas também variáveis caracterizando a infra-estrutura viária. Os resultados permitem observar um bom desempenho dos modelos, em particular na fase de validação. As projeções futuras obtidas com os modelos, no entanto, não parecem caracterizar adequadamente o surgimento, que seria naturalmente esperado, de novos pólos comerciais ao redor dos shopping centers estudados. Essa limitação foi surpreendentemente mais evidente no caso do modelo com variáveis de infra-estrutura viária. / The development of cities often leads to the development of large business areas. Those areas, together with the growth of population and vehicles figures in the entire urban area, produce several impacts in the transportation systems. Given the evidences that the location and size of new trip generators (TG\'s) may be strongly influenced by existing TG\'s, the objective of this study was to evaluate that hypothesis in a brazilian medium-sized city. Two branches of spatial analysis were explored in the present study while dealing with different sources of spatial information. They were: spatial statistics and spatial modeling. Firstly, concepts of exploratory spatial data analyses (ESDA), like Moran\'s I index and scatterplot, were used to characterize regions of similar behavior in terms of particular variables. Secondly, spatial modeling was carried out using cellular automata concepts and artificial neural networks. Two models were proposed and applied in a case study carried out in the city Campinas, São Paulo, based on different variables. In the first model, only variables representing commercial land use were considered, while in the second model variables representing the transportation infrastructure supply were also added. The results found indicate a good performance of the models, particularly in the validation process. Their future projections, however, are apparently not able to represent the development of new commercial activities that would be naturally expected around the shopping malls investigated. Although that limitation was observed in both models, it was surprisingly more evident in the case of the model with variables of transportation infrastructure.

O desenvolvimento das cidades freqüentemente propicia o aparecimento de empreendimentos de grande porte que, juntamente com o aumento da população e dos veículos, causam impactos nos sistemas de transportes. Dado que há fortes indícios que a localização e o porte destes empreendimentos, aqui denominados de pólos geradores de viagens (ou PGVs), seja influenciado pela presença de outros PGVs, o objetivo deste trabalho foi avaliar esta hipótese em uma cidade média brasileira, através de duas vertentes da análise espacial: a estatística espacial e a modelagem espacial baseada na comparação de informações oriundas de diferentes entidades espaciais. Foram utilizados conceitos de análise exploratória de dados espaciais, tais como o índice e o diagrama de espalhamento de Moran, para a delimitação de regiões similares, divididas de modo a indicar pontos de associação espacial, em relação a uma variável analisada. Em seguida, a modelagem espacial foi feita com uma adaptação da técnica de cellular automata, usando redes neurais artificiais. Dois modelos foram propostos e aplicados em um estudo de caso na cidade de Campinas, São Paulo, com base em diferentes variáveis. No primeiro modelo, somente variáveis representativas da ocupação comercial foram consideradas, enquanto que, no segundo modelo, foram introduzidas também variáveis caracterizando a infra-estrutura viária. Os resultados permitem observar um bom desempenho dos modelos, em particular na fase de validação. As projeções futuras obtidas com os modelos, no entanto, não parecem caracterizar adequadamente o surgimento, que seria naturalmente esperado, de novos pólos comerciais ao redor dos shopping centers estudados. Essa limitação foi surpreendentemente mais evidente no caso do modelo com variáveis de infra-estrutura viária. / The development of cities often leads to the development of large business areas. Those areas, together with the growth of population and vehicles figures in the entire urban area, produce several impacts in the transportation systems. Given the evidences that the location and size of new trip generators (TG\'s) may be strongly influenced by existing TG\'s, the objective of this study was to evaluate that hypothesis in a brazilian medium-sized city. Two branches of spatial analysis were explored in the present study while dealing with different sources of spatial information. They were: spatial statistics and spatial modeling. Firstly, concepts of exploratory spatial data analyses (ESDA), like Moran\'s I index and scatterplot, were used to characterize regions of similar behavior in terms of particular variables. Secondly, spatial modeling was carried out using cellular automata concepts and artificial neural networks. Two models were proposed and applied in a case study carried out in the city Campinas, São Paulo, based on different variables. In the first model, only variables representing commercial land use were considered, while in the second model variables representing the transportation infrastructure supply were also added. The results found indicate a good performance of the models, particularly in the validation process. Their future projections, however, are apparently not able to represent the development of new commercial activities that would be naturally expected around the shopping malls investigated. Although that limitation was observed in both models, it was surprisingly more evident in the case of the model with variables of transportation infrastructure.