Towards the Identification of Consumer Trajectories in Geo-Located Search Data

Modern geo-positioning system (GPS) enabled smart phones are generating an increasing volume of information about their users, including geo-located search, movement, and transaction data. While this kind of data is increasingly rich and offers many grand opportunities to identify patterns and predict behaviour of groups and individuals, it is not immediately obvious how to develop a framework for extracting plausible inferences from these data. In our case, we have access to a large volume of real user data from the Point smart phone application, and we have developed a generic and layered system architecture to incrementally find aggregate items of interest within that data. This includes time and space correlations, e.g., are people searching for dinner and a movie, distributions of usage patterns and platforms, e.g., geographic distribution of Android, Apple, and BlackBerry users, and clustering to identify relatively complex search and movement patterns we call "consumer trajectories." Our pursuit of these kinds of patterns has helped guide our development of information extraction, machine learning, and visualization methods that provide systematic tools for investigating the geo-located data, and for the development of both conceptual tools and visualization tools in aid of finding both interesting and useful patterns in that data. Included in our system architecture is the ability to consider the difference between exploratory and explanatory hypotheses on data patterns, as well as the deployment of multiple visualization methods that can provide alternatives to help expose interesting patterns. In our introduction to our framework here, we provide examples of formulating hypotheses on geo-located behaviour, and how a variety of methods including those from machine learning and visualization, can help confirm or deny the value of such hypotheses as they emerge. In this particular case, we provide an initial basis for identifying semantically motivated data - rtifacts we call geo-located consumer trajectories. We investigate their plausibility with a variety of time and space series clustering and visualization models.