Human mobility and urban dynamics are the keys to understanding diversity and complexity in cities. With advancement of technologies, a significant amount of geospatial data is generated, shared, and analyzed. Big geospatial data analytics highlights the importance of geography in data-driven knowledge discovery. The goal of this dissertation is to progress the fundamental understanding of human mobility and urban dynamics by developing novel methodological frameworks and models that utilize micro-scale spatiotemporal big data and encourage knowledge creation. To achieve this goal, this dissertation includes three studies that focus on developing new methods to understand human mobility, urban dynamics, and their interactions. In the first study (Chapter 2), I propose a novel index measuring similarities between human mobility patterns from different data sources such as social media and traditional survey. The second study (Chapter 3) shifts focus to urban dynamics. Applying a series of spatiotemporal exploratory studies, an efficient method for examining spatiotemporal patterns at a micro-scale in restaurants is proposed. The third study (Chapter 4) investigates the relationships between human mobility and urban dynamics with a novel explainable deep learning approach enhancing predictivity and interpretability of neural network models within geographic context. Throughout this dissertation, a comprehensive framework for understanding complexity of human mobility and urban dynamics is suggested through incorporating detailed spatial data into big data analytic models from geographic perspectives. This will provide individuals and governments with fundamental knowledge for better decision-making associated with economic growth and development.