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.

This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.

This dissertation addresses the research challenge of developing efficient new methods for discovering useful patterns and knowledge in large volumes of electronically collected spatiotemporal activity data. I propose to analyze three types of such spatiotemporal activity data in a methodological framework that integrates spatial analysis, data mining, machine learning, and geovisualization techniques. Three different types of spatiotemporal activity data were collected through different data collection approaches: (1) crowd sourced geo-tagged digital photos, representing people's travel activity, were retrieved from the website Panoramio.com through information retrieval techniques; (2) the same techniques were used to crawl crowd sourced GPS trajectory data and related metadata of their daily activities from the website OpenStreetMap.org; and finally (3) preschool children's daily activities and interactions tagged with time and geographical location were collected with a novel TabletPC-based behavioral coding system. The proposed methodology is applied to these data to (1) automatically recommend optimal multi-day and multi-stay travel itineraries for travelers based on discovered attractions from geo-tagged photos, (2) automatically detect movement types of unknown moving objects from GPS trajectories, and (3) explore dynamic social and socio-spatial patterns of preschool children's behavior from both geographic and social perspectives.