Digital transformation can be defined as, “the acceleration of business activities, processes, competencies, and models to fully leverage the changes and opportunities of digital technologies and their impact in a strategic and prioritized way,” (Edmead, Mark, and IDG Contributor Network, 2016). Following the industrial revolution, digital transformation has taken shape as the current revolution and innovative process. When industry’s and businesses engage in digital transformation, they create disruption and pave the way for enhanced customer value, efficient operational processes, and innovative business models. The prospect of this thesis is to: (1) understand how digital transformation strategy helps to propel innovation for the self-driving car, (2) understand how this innovation will create value in the grand schema for digital transformation, (3) develop a GIS-based (location analytics) study to understand the market opportunity for such technology and innovation. We outline how digital transformation as a whole represents a modern form of creative destruction, that is rewarding to businesses who engage in transformation for efficiency and innovation, and addresses the implications of those that do not. We discuss how digital transformation has affected the auto industry to invest in innovating self-driving cars. Finally, we perform location analytics to develop an opportunity analysis in five big markets around the Phoenix Metropolitan area in the State of Arizona to identify the potential markets for self-driving cars. We conclude this study with a discussion on how technology strategy is transforming the world.

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.