Platial Science: A multi-dimensional data signatures approach to modeling Place

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Historically, Geographic Information Science (GIScience) has focused on methods and techniques for quantifying geospatial relationships between entities, be they cadastral surveys or human mobility patterns. As recent progress in big data analytics and ambient intelligence is met with sensor-enabled mobile technology and the social media boom, GIScience has expanded from traditional focus on spatial data analysis, to incorporating non-geospatial contextual information. The inclusion of these data shifts GIScience away from traditional Space-based approaches towards the burgeoning discipline of "Place-based Information Science." The application domain of much of my work is on identifying patterns in human activities, e.g., where and when people eat dinner, shop, etc. From this perspective, geospatial data provides valuable information about a person's location, but rarely offers insight into the activities in which the individual is participating. In this way, spatial location is only one dimension in the complex multi-dimensional concept of Place. For instance, knowledge of the place that an individual is visiting (e.g., Mel's diner in Montreal) is of greater value than just the geographic coordinates of the individual. The place location of someone provides considerably more detail related to type of place, activities of the individual, demographics, etc. By identifying a person's "platial" locations, rather than just their spatial locations, we gain a better understanding of their activities, mobility trends, etc. Consequently, computational models of place are increasingly important in both geoinformatics and information science as they play a dominant role in topics ranging from geoinformation retrieval and decision support systems to transport modeling and urban planning. Traditionally, this concept of place has been approached from a highly qualitative perspective. In my research program I propose to model place computationally by exploiting the heterogeneity of user-contributed data. These data, manifest as online reviews, social media posts and geosocial check-ins, to name a few. Specifically, I pull apart this concept of place and approach it from three different dimensions, 1. a spatial dimension- concerned with the geographic location of places and their spatial relationship to other geographic features, 2. a temporal dimension- demonstrating how places change depending on time (e.g., A park in the day is a different than a park at night) and 3. a thematic dimension- that describes the type of place and activities that are afforded by the place. Through the identification of unique patterns within these dimensions, my ongoing work demonstrates that places can be quantified, inviting the assessment of differences and similarities. In collaboration with my students, I plan to continue on this research thread and intend to demonstrate that these novel place-based signatures can be used to answer a range of informatics-based questions.