Developing Data-Driven Methods for Movement Pattern Analysis using Geographic Context

The role of movement data is essential to understanding how geographic context influences movement patterns in urban areas. Owing to the growth in ubiquitous data collection platforms like smartphones, fitness trackers, and health monitoring apps, researchers are now able to collect movement data at increasingly fine spatial and temporal resolution. Despite the surge in volumes of fine-grained movement data, there is a gap in the availability of quantitative and analytical tools to extract actionable insights from such big datasets and tease out the role of context in movement pattern analysis. As cities aim to be safer and healthier, policymakers require methods to generate efficient strategies for urban planning utilizing high-frequency movement data to make targeted decisions for infrastructure investments without compromising the safety of its residents. The objective of this Ph.D. dissertation is to develop quantitative methods that combine big spatial-temporal data from crowdsourced platforms with geographic context to analyze movement patterns over space and time. Knowledge about the role of context can help in assessing why changes in movement patterns occur and how those changes are affected by the immediate natural and built environment. In this dissertation I contribute to the rapidly expanding body of quantitative movement pattern analysis research by 1) developing a bias-correction framework for improving the representativeness of crowdsourced movement data by modeling bias with training data and geographical variables, 2) understanding spatial-temporal changes in movement patterns at different periods and how context influences those changes by generating hourly and monthly change maps in bicycle ridership patterns, and 3) quantifying the variation in accuracy and generalizability of transportation mode detection models using GPS (Global Positioning Systems) data upon adding geographic context. Using statistical models, supervised classification algorithms, and functional data analysis approaches I develop modeling frameworks that address each of the research objectives. The results are presented as street-level maps and predictive models which are reproducible in nature. The methods developed in this dissertation can serve as analytical tools by policymakers to plan infrastructure changes and facilitate data collection efforts that represent movement patterns for all ages and abilities.