You will be tasked with building an interactive tool that discovers and visualizes Origin-Destination-Time (ODT) patterns from transportation data. The core idea is to identify significant passenger flows between regions at varying granularities of space and time. For example, discovering that there is heavy passenger flow from Financial District to Midtown during morning rush hours, or that residential areas see increased traffic to dining districts in the evenings.You will work with publicly available taxi trip data from NYC (or similar datasets). Your task is to implement a pattern mining algorithm that identifies ODT patterns where origins and destinations can be generalized to larger regions (based on geographic adjacency), and time slots can be grouped into longer periods. The focus is on creating clear, interactive visualizations that help users explore movement patterns at different granularities rather than optimizing for large-scale performance.Furthermore, you will design an interface where users can: (1) select regions and time periods of interest, (2) view discovered patterns on an interactive map, (3) drill down into pattern details to see which atomic trips contribute to each pattern, and (4) compare patterns across different times of day or days of the week.