Background: Food access is a global issue, and for this reason, a wealth of studies are dedicated to understanding the location of food deserts and the benefits of urban gardens. However, few studies have linked these two strands of research together to analyze whether urban gardening activity may be a step forward in addressing issues of access for food desert residents.

Methods: The Phoenix, Arizona metropolitan area is used as a case to demonstrate the utility of spatial optimization models for siting urban gardens near food deserts and on vacant land. The locations of urban gardens are derived from a list obtained from the Maricopa County Cooperative Extension office at the University of Arizona which were geo located and aggregated to Census tracts. Census tracts were then assigned to one of three categories: tracts that contain a garden, tracts that are immediately adjacent to a tract with a garden, and all other non-garden on-adjacent census tracts. Analysis of variance is first used to ascertain whether there are statistical differences in the demographic, socio-economic, and land use profiles of these three categories of tracts. A maximal covering spatial optimization model is then used to identify potential locations for future gardening activities. A constraint of these models is that gardens be located on vacant land, which is a growing problem in rapidly urbanizing environments worldwide.

Results: The spatial analysis of garden locations reveals that they are centrally located in tracts with good food access. Thus, the current distribution of gardens does not provide an alternative food source to occupants of food deserts. The maximal covering spatial optimization model reveals that gardens could be sited in alternative locations to better serve food desert residents. In fact, 53 gardens may be located to cover 96.4% of all food deserts. This is an improvement over the current distribution of gardens where 68 active garden sites provide coverage to a scant 8.4% of food desert residents.

Conclusion: People in rapidly urbanizing environments around the globe suffer from poor food access. Rapid rates of urbanization also present an unused vacant land problem in cities around the globe. This paper highlights how spatial optimization models can be used to improve healthy food access for food desert residents, which is a critical first step in ameliorating the health problems associated with lack of healthy food access including heart disease and obesity.

There are many different approaches to the analysis of regional economic growth potential. One of the more recent is the theory of the creative class, and its impact on creative centers. Much of the criticism surrounding this theory is in how the creative class is defined and measured. The goal of this thesis is to explore alternate definitions to better understand how these variations impact the ranking of creative centers as well as their location through space and time. This is important given the proliferation of rankings as a benchmarking tool for economic development efforts. In order to test the sensitivity that the creative class has to definitional changes, a new set of rankings of creative centers are provided based on an alternate definition of creative employment, and compared to Richard Florida's original rankings. Findings show that most cities are not substantially affected by the alternate definitions derived in this study. However, it is found that particular cities do show sensitivity to comparisons made to Florida's definition, with the same cities experiencing greater variations in rank over time.

There exist many facets of error and uncertainty in digital spatial information. As error or uncertainty will not likely ever be completely eliminated, a better understanding of its impacts is necessary. Spatial analytical approaches, in particular, must somehow address data quality issues. This can range from evaluating impacts of potential data uncertainty in planning processes that make use of methods to devising methods that explicitly account for error/uncertainty. To date, little has been done to structure methods accounting for error. This research focuses on developing methods to address geographic data uncertainty in spatial optimization. An integrated approach that characterizes uncertainty impacts by constructing and solving a new multi-objective model that explicitly incorporates facets of data uncertainty is developed. Empirical findings illustrate that the proposed approaches can be applied to evaluate the impacts of data uncertainty with statistical confidence, which moves beyond popular practices of simulating errors in data. Spatial uncertainty impacts are evaluated in two contexts: harvest scheduling and sex offender residency. Owing to the integration of spatial uncertainty, the detailed multi-objective models are more complex and computationally challenging to solve. As a result, a new multi-objective evolutionary algorithm is developed to address the computational challenges posed. The proposed algorithm incorporates problem-specific spatial knowledge to significantly enhance the capability of the evolutionary algorithm for solving the model.