In the mid-1970s, social scientists began observing marital dyad conversations in laboratory settings with the hope of determining which observable features best discriminate couples who report being either satisfied or unsatisfied with their relationship. These studies continued until about a decade ago when, in addition to increasing laboratory costs slowing the pace of new data collection, researchers realized that distressed couples were easier to quantitatively describe than nondistressed couples. Specifically, distressed couples exhibit rigid patterns of negativity whereas couples who report being maritally satisfied show minimal rigidity in the opposite direction \u2014 positivity. This was, and is, a theoretical dilemma: how can clinicians understand and eventually modify distressed relationships when the behavior of satisfied couples are less patterned, less predictable and more diverse? A recent study by Griffin and Li (2015), using contemporary machine learning techniques, reanalyzed existing marital interaction data and found that, contrary to expectation and existing theory, nondistressed couples should be further subdivided into two groups \u2014 those who are predictably positive or neutral and those who interact using diverse and varying levels of positive and negative behaviors. The latter group is the focus of this thesis. Using these recent findings as discussion points, I review how the unexpected behaviors in this novel group can maintain and possibly perpetuate marital satisfaction.

In today's world, unprecedented amounts of data of individual mobile objects have become more available due to advances in location aware technologies and services. Studying the spatio-temporal patterns, processes, and behavior of mobile objects is an important issue for extracting useful information and knowledge about mobile phenomena. Potential applications across a wide range of fields include urban and transportation planning, Location-Based Services, and logistics. This research is designed to contribute to the existing state-of-the-art in tracking and modeling mobile objects, specifically targeting three challenges in investigating spatio-temporal patterns and processes; 1) a lack of space-time analysis tools; 2) a lack of studies about empirical data analysis and context awareness of mobile objects; and 3) a lack of studies about how to evaluate and test agent-based models of complex mobile phenomena. Three studies are proposed to investigate these challenges; the first study develops an integrated data analysis toolkit for exploration of spatio-temporal patterns and processes of mobile objects; the second study investigates two movement behaviors, 1) theoretical random walks and 2) human movements in urban space collected by GPS; and, the third study contributes to the research challenge of evaluating the form and fit of Agent-Based Models of human movement in urban space. The main contribution of this work is the conceptualization and implementation of a Geographic Knowledge Discovery approach for extracting high-level knowledge from low-level datasets about mobile objects. This allows better understanding of space-time patterns and processes of mobile objects by revealing their complex movement behaviors, interactions, and collective behaviors. In detail, this research proposes a novel analytical framework that integrates time geography, trajectory data mining, and 3D volume visualization. In addition, a toolkit that utilizes the framework is developed and used for investigating theoretical and empirical datasets about mobile objects. The results showed that the framework and the toolkit demonstrate a great capability to identify and visualize clusters of various movement behaviors in space and time.