Urban sprawl in the state of Missouri : current trends, driving forces, and predicted growth on Missouri's natural landscape

Human population growth and associated sprawl has rapidly converted open lands to developed use and affected their distinctive ecological characteristics. Missouri reflects a full range of sprawl characteristics that include large metropolitan centers, which led growth in 1980s, and smaller metropolitan and rural areas, which led growth in 1990s. In order to study the historical patterns of sprawl, there is a need to quantitatively and geographically depict the extent and density of impervious surface for three time periods of 1980, 1990, and 2000 for the entire state of Missouri. This research goes beyond the usual hot spots of metropolitan areas to include rural landscapes where negative impact was exerted to the ecosystem due to the low density development and larger affected areas. Mapped impervious surface is the best candidate of ancillary data for dasymetric mapping of population in several comparison studies. The current research examines the performances of dasymetric mapping of population with imperviousness as ancillary data and regression analysis of population using imperviousness as a predictor. In the context of this comparison, this research also examines the performance of imperviousness with xii road network removed versus imperviousness with road network and certain ranges of values removed. The assessment of approach and ancillary data performance is done by comparing estimated population for each block to the original Census block population. Results from this work can be aggregated to any geographical unit (hydrologic boundaries, administrative boundaries, etc.). More importantly, the aggregated population information will be crucial in the modeling of future urban growth. A pilot future urban growth study for the two decades of 1980s and 1990s was done in Missouri. The historical urban growth of the two decades were analyzed then coupled with various predictor variables to investigate the influence of each predictor variables towards the process of urban growth. The knowledge learned from the process is then used to build an urban growth simulation model that is GIS-based with open framework for ease of management and improvement. The complexity of urban systems is making the holistic modeling approach obsolete. Because it is impossible for one omnipotent model to solve urban growth problems of different locations, in this research, we decided to group those problems by different physical and mathematical process to tackle them one by one. Correspondingly, we used multiple sub modules each responsible for different processes related to urban growth. The structure of this model ensures each individual module can be updated and improved, and more sub modules can be added. Pixel level urban growth was simulated for year 2010, 2020 and 2030. This model framework is developed with the ultimate goal of simulating urban growth for the entire