Rapid urbanization is a pressing issue for planners, policymakers, transportation engineers, air quality modelers and others. Due to significant environmental, traffic and other impacts, the process of land development highlights a need for land use models with behavioral foundations. Such models seek to anticipate future settlement and transport patterns, helping ensure effective public and private investment decisions and policymaking, to accommodate growth while mitigating environmental impacts and other concerns. A variety of land use models now exist, but a market-based model with sufficient spatial resolution and defensible behavioral foundations remains elusive. This dissertation addresses this goal by developing and applying such a model. Real estate markets involve numerous interactive agents and real estate with a great level of heterogeneity. In the absence of tractable theory for realistic real estate markets, this research takes a “bottom-up” approach and simulates the behavior of tens of thousands of individual agents based on actual data. Both the supply and demand sides of the market are modeled explicitly, with endogenously determined property prices and land use patterns (including distributions of households and firms). Notions of competition were used to simulate price adjustment, and market-clearing prices were obtained in an iterative fashion. When real estate markets reach equilibrium, each agent is aligned with a single, utility-maximizing location and each allocated location is occupied by the highest bidding agent(s). This approach helps ensure a form of local equilibrium (subject to imperfect information on the part of most agents) along with useroptimal land allocation patterns. The model system was applied to the City of Austin and its extraterritorial jurisdiction. Multiple scenarios reveal the strengths and limitations of the market simulation and available data sets. While equilibrium prices in forecast years are generally lower than observed or expected, the spatial distributions of property values, new development, and individual agents are reasonable. Longer-term forecasts were generated to test the performance the model system. The forecasted households and firm distributions in year 2020 are consistent with expectations, but property prices are forecasted to experience noticeable changes. The model dynamics may be much improved by more appropriate maximum bid prices for each property. More importantly, this work demonstrates that microsimulation of real estate markets and the spatial allocation of households and firms is a viable pursuit. Such approaches herald a new wave of land use forecasting opportunities, for more effective policymaking and planning. / text

The question how a least-cost spatial allocation of sustainable electricity infrastructure may look like using different decision-making procedures (markets, different kinds of land-use and grid regulations) has not yet been analysed explicitly. We measure the sustainability of emerging energy landscapes providing power from renewable energy sources (RES) by an overall welfare function also comprising all kinds of space-related disutilities, i.e. spatial externalities - be they site-specific or related to the distance to a residential area (consumer centre). The presented agent-based model (ABM) concept aims at assessing different policy scenarios to govern the land-use for energetic purposes under the constraint of ensuring the electricity supply for a virtual landscape with RES. To derive optimal spatial allocation an agent-based modelling approach is implemented, which includes a virtual landscape, three settlements as demand centres and profit-oriented producers of renewable power. For the design of the electricity grid and the calculation of grid-related reinforcement costs a load-flow model is applied, being also able to map grid externalities during the RES expansion in space. The model allows RES producers to choose profit-maximising cells for plant installations until the given demand for power of the virtual landscape is met. Different policy scenarios allocate particular costs to agents (e.g. grid reinforcement costs, spatial externalities) or restrict the land-use with respect to ecological or social restraints. Furthermore, consumer centres have the possibility to follow own particular regional strategies, to increase their individual benefit. The overall efficiency of allocation (total cost level) as well as the distributional fairness (regional net costs) are evaluated for the policy scenarios and the regional strategies.

Data from the 2001 and 2003 timber product output (TPO) studies for Georgia were explored to determine new methods for handling missing data and finding suitable sampling estimators. Mean roundwood volume receipts per mill for the year 2003 were calculated using the methods developed by Rubin (1987). Mean receipts per mill ranged from 4.4 to 14.2 million ft3. The mean value of 9.3 million ft3 did not statistically differ from the NONMISS, SINGLE1, and SINGLE2 references means (p=.68, .75, and .76 respectively). Fourteen estimators were investigated to investigate sampling approaches, with estimators being of several means types (simple random sample, ratio, stratified sample, and combined ratio) as well as employing two methods for stratification (Dalenius-Hodges (DH) square root of the Frequency method and a cluster analysis method. Relative efficiency (RE) improved when the number of groups increased and when employing a ratio estimator, particularly a combined ratio. Neither the DH method nor the cluster analysis method performed better than the other. Six bound sizes (1, 5, 10, 15, 20, and 25 percent) were considered for deriving samples sizes for the total volume of roundwood. The minimum achievable bound size was found to be 10 percent of the total receipts volume for the DH-method using a two group stratification. This was true for both the stratified and combined ratio estimators. In addition, for the stratified and combined ratio estimators, only the DH method stratifications were able to reach a 10 percent bound on the total (6 of the 12 stratified estimators). The remaining six stratified estimators were able to achieve a 20 percent bound of the total. Finally, nonlinear repeated measures models were developed to spatially allocate mill receipts to surrounding counties in the event of obtaining only a mill's total receipt volume. A Gompertz model with a power spatial covariance was found to be the best performing when using road distances from the mills to either county center type (geographic or forest mass). These models utilized the cumulative frequency of mill receipts as the response variable, with cumulative frequencies based on distance from the mill to the county. / Ph. D.

The thesis aims to uncover spatial patterns of subsidies from the European Union funds while focusing on the 2007-2013 programming period in the Czech Republic. We take into account demographic, economic and political charac- teristics of municipalities with the extended power to determine their relevance on the size of the obtained subsidy. Furthermore, subsidies may be clustered in the space due to some common regional characteristics that cannot be easily measured, or due to cooperation/competitiveness of the municipalities. Our results from the models estimated by the Ordinary Least Squares sug- gest that municipality's population, the number of businesses, the debt of pre- vious periods, and the mayor's affiliation to the political party are significant in explaining the size of the subsidy. Based on following tests, Moran's I and Lagrange Multiplier test, we incorporate the spatial models estimated by the Maximum Likelihood Estimator. We find that the subsidies managed by the regional council disperse across the given region, indicating a hidden common characteristic or regional council's endogenous interventions. JEL Classification C21, H54, H77, R11, R58 Keywords spatial allocation, European Funds, Cohesion, local government, Czech Republic Author's e-mail pumprlovamagda@gmail.com Supervisor's...

Are decision-makers, and public ones in particular, considering the spatial characteristics of territories when they are making decisions? Which elements should be included in computer-based systems supporting such decisions? These two questions summarise the issues geographic information system scientists are addressing. They also constitute the starting point of this investigation on the theme of errors and uncertainty in spatial decision support systems. While decision theories, methodologies and techniques abound, none are peculiar to spatial issues. At best, they are using spatial data and in a few cases they are applied to helping solve spatial problems. It has been recognised that spatial data need distinct management systems. Should it be the same for spatial decisions? By postulating that spatial decision is a specific field requiring its own treatise, this author initiates an original reflexion. Following a holistic approach, this theoretical work develops a conceptual decision model: the 'decisional fountain'. This model gives a coherent and integrated view on the various fields of decision-making. Based on the postulate and on the model developed, several theories and techniques are revisited in a truly spatial perspective. The ontology of spatial decision has been enriched by a variety of experiences. Dealing with drought at the European level, allocating water resources from the field scale to the country scale, regional spatial planning, performing strategic environmental assessment, conceptualising the parking of a vehicle, developing the spatial abilities of children, all have fed this conceptual research. Some of them are illustrating this manuscript. Taking a spatial information science perspective, this manuscript leads you towards a 'spatial decision' theory.