Geospatial Data Science to Identify Patterns of Evasion

Abstract

Over the last decade, there has been a significant growth in the availability of cheap raw spatial data in the form of GPS trajectories, activity/event locations, temporally detailed road networks, satellite imagery, etc. These data are being collected, often around the clock, from location-aware applications, sensor technologies, etc. and represent an unprecedented opportunity to study our economic, social, and natural systems and their interactions. For example, finding hotspots (areas with unusually high concentration of activities/events) from activity/event locations plays a crucial role in epidemiology since it may help public health officials prevent further spread of an infectious disease. In order to extract useful information from these datasets, many geospatial data tools have been proposed in recent years. However, these tools are often used as a “black box”, where a trial-error strategy is used with multiple approaches from different scientific disciplines (e.g. statistics, mathematics and computer science) to find the best solution with little or no consideration of the actual phenomena being investigated. Hence, the results may be biased or some important information may be missed. To address this problem, we need geospatial data science with a stronger scientific foundation to understand the actual phenomena, develop reliable and trustworthy models and extract information through a scientific process. Thus, my thesis investigates a wide-lens perspective on geospatial data science, considering it as a transdisciplinary field comprising statistics, mathematics, and computer science. This approach aims to reduce the redundant work across disciplines as well as define scientific boundaries of geospatial data science to distinguish it from being a black box that claims to solve every possible geospatial problem. In my proposed approaches, I used ideas from those three disciplines, e.g. spatial scan statistics from statistical science to reduce chance patterns in the output and provide statistical robustness; mathematical definitions of geometric shapes of the patterns, which maintain correctness and completeness; and computational approaches (along with prune and refine framework and dynamic programming ideas) to scale up to large spatial datasets. In addition, the proposed approaches incorporate domain-specific geographic theories (e.g., routine activity theory in criminology) for applicability in those domains that are interested in specific patterns, which occur due to the actual phenomena, from geospatial datasets. The proposed techniques have been applied to real world disease and crime datasets and the evaluations confirmed that our techniques outperform current state-of-the-art such as density based clustering approaches as well as circular hotspot detection methods.