Geographical visualizations are critical for multi-criteria analysis, optimization, and decision making, where the translation of spatial data into a visual form allows analysts to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena. However, several critical challenges arise when visualizing large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex, requiring new approaches for multiclass map visualization and exploration. In this thesis, novel visual analytics methods and frameworks are proposed to support multiclass map analysis. An interactive conservation portfolio development system that combines visualization, multicriteria analysis, optimization, and decision making is developed that showcases a novel visualization and interaction design to compare different purchasing profiles under various optimization constraints. Such multiclass map analysis is then extended using concepts from scalar field topology for hotspot analysis including the introduction of a novel visualization construct combining Merge Trees and Streamgraphs.

The rapid advances in wireless communications and networking have given rise to a number of emerging heterogeneous wireless and mobile networks along with novel networking paradigms, including wireless sensor networks, mobile crowdsourcing, and mobile social networking. While offering promising solutions to a wide range of new applications, their widespread adoption and large-scale deployment are often hindered by people's concerns about the security, user privacy, or both. In this dissertation, we aim to address a number of challenging security and privacy issues in heterogeneous wireless and mobile networks in an attempt to foster their widespread adoption. Our contributions are mainly fivefold. First, we introduce a novel secure and loss-resilient code dissemination scheme for wireless sensor networks deployed in hostile and harsh environments. Second, we devise a novel scheme to enable mobile users to detect any inauthentic or unsound location-based top-k query result returned by an untrusted location-based service providers. Third, we develop a novel verifiable privacy-preserving aggregation scheme for people-centric mobile sensing systems. Fourth, we present a suite of privacy-preserving profile matching protocols for proximity-based mobile social networking, which can support a wide range of matching metrics with different privacy levels. Last, we present a secure combination scheme for crowdsourcing-based cooperative spectrum sensing systems that can enable robust primary user detection even when malicious cognitive radio users constitute the majority.