Spatiotemporal Clustering

Spatiotemporal clustering analyzes data points possessing both spatial and temporal characteristics, aiming to identify meaningful groups or patterns in their movement and distribution over time. Current research focuses on developing efficient algorithms, such as variations of k-means and methods leveraging graph representations, to handle large datasets and complex dynamics, often incorporating Bayesian approaches for improved uncertainty quantification. These advancements find applications in diverse fields, including transportation systems, environmental monitoring (e.g., bushfire tracking), and even financial crime detection by analyzing blockchain transactions, demonstrating the broad utility of spatiotemporal clustering techniques.