Sustainable urbanism offers a set of best practice planning and design prescriptions intended to reverse the negative environmental consequences of urban sprawl, which dominates new urban development in the United States. Master planned developments implementing sustainable urbanism are proliferating globally, garnering accolades within the planning community and skepticism among social scientists. Despite attention from supporters and critics alike, little is known about the actual environmental performance of sustainable urbanism. This dissertation addresses the reasons for this paucity of evidence and the capacity of sustainable urbanism to deliver the espoused environmental outcomes through alternative urban design and the conventional master planning framework for development through three manuscripts. The first manuscript considers the reasons why geography, which would appear to be a natural empirical home for research on sustainable urbanism, has yet to accumulate evidence that links design alternatives to environmental outcomes or to explain the social processes that mediate those outcomes. It argues that geography has failed to develop a coherent subfield based on nature-city interactions and suggests interdisciplinary bridging concepts to invigorate greater interaction between the urban and nature-society geographic subfields. The subsequent chapters deploy these bridging concepts to empirically examine case-studies in sustainable urbanism. The second manuscript utilizes fine scale spatial data to quantify differences in ecosystem services delivery across three urban designs in two phases of Civano, a sustainable urbanism planned development in Tucson, Arizona, and an adjacent, typical suburban development comparison community. The third manuscript considers the extent to which conventional master planning processes are fundamentally at odds with urban environmental sustainability through interviews with stakeholders involved in three planned developments: Civano (Tucson, Arizona), Mueller (Austin, Texas), and Prairie Crossing (Grayslake, Illinois). Findings from the three manuscripts reveal deep challenges in conceptualizing an empirical area of inquiry on sustainable urbanism, measuring the outcomes of urban design alternatives, and innovating planning practice within the constraints of existing institutions that facilitate conventional development. Despite these challenges, synthesizing the insights of geography and cognate fields holds promise in building an empirical body of knowledge that complements pioneering efforts of planners to innovate urban planning practice through the sustainable urbanism alternative.

Decision makers contend with uncertainty when working through complex decision problems. Yet uncertainty visualization, and tools for working with uncertainty in GIS, are not widely used or requested in decision support. This dissertation suggests a disjoint exists between practice and research that stems from differences in how visualization researchers conceptualize uncertainty and how decision makers frame uncertainty. To bridge this gap between practice and research, this dissertation explores uncertainty visualization as a means for reframing uncertainty in geographic information systems for use in policy decision support through three connected topics. Initially, this research explores visualizing the relationship between uncertainty and policy outcomes as a means for incorporating policymakers' decision frames when visualizing uncertainty. Outcome spaces are presented as a method to represent the effect of uncertainty on policy outcomes. This method of uncertainty visualization acts as an uncertainty map, representing all possible outcomes for specific policy decisions. This conceptual model incorporates two variables, but implicit uncertainty can be extended to multivariate representations. Subsequently, this work presented a new conceptualization of uncertainty, termed explicit and implicit, that integrates decision makers' framing of uncertainty into uncertainty visualization. Explicit uncertainty is seen as being separate from the policy outcomes, being described or displayed separately from the underlying data. In contrast, implicit uncertainty links uncertainty to decision outcomes, and while understood, it is not displayed separately from the data. The distinction between explicit and implicit is illustrated through several examples of uncertainty visualization founded in decision science theory. Lastly, the final topic assesses outcome spaces for communicating uncertainty though a human subject study. This study evaluates the effectiveness of the implicit uncertainty visualization method for communicating uncertainty for policy decision support. The results suggest that implicit uncertainty visualization successfully communicates uncertainty in results, even though uncertainty is not explicitly shown. Participants also found the implicit visualization effective for evaluating policy outcomes. Interestingly, participants also found the explicit uncertainty visualization to be effective for evaluating the policy outcomes, results that conflict with prior research.