In the modern realm of pervasive, frequent, sizable and instant data capturing with advancements in instrumentation, data generation and data gathering techniques, we can benefit new prospects to comprehend and analyze the role of geography in everyday life. However, traditional geographic data analytics are now strictly challenged by the volume, velocity, variety and veracity of the data requiring analysis to extract value. As a result, geographic data science has garnered great interest in the past two decades. Considering that much of data science’s success is formed outside of geography, there is an increased risk within such perspectives that location will remain simply as an additional column within a database, no more or less important than any other feature. Geographic data science combines this data with spatial and temporal components. The spatial and temporal dependence allow us to interpolate and extrapolate to fill gaps in the presence of inadequate data and infer reasonable approximations elsewhere by incorporating information from diverse data types and sources. However, within scientific communities there exist arguments regarding whether geographic data science is a scientific discipline of its own. Because data science is still in its early adoption phases in geography, geographic data science is required to develop its unique concepts, differentiating itself from other disciplines such as statistics or computer science. This becomes possible when geographers, within a community of practice, are enabled to learn and connect the current tools, methods, and domain knowledge to address the existing challenges of geographic data analysis. To take a step toward that purpose, in this dissertation, three knowledge-rich applications of data science in the analysis of geographic spatiotemporal big datasets are studied, and the opportunities and challenges facing this research along the way are explored. The first chapter of this dissertation is allocated to review the challenges and opportunities in the era of spatiotemporal big data, followed by tackling three different problems within geography, one within the subfield of human geography, and two within physical geography. Finally, in the last chapter, some final thoughts on the current state of geographic data science are discussed and the potential for future studies are considered.
Read
