Identifying Local Spatiotemporal Autocorrelation Patterns of Taxi Pick-ups and Drop-offs
Thursday Sep 29 03:30 PM to 05:30 PM (2 hours)
Marriott Chateau Champlain (Main event) - Viger A
spatiotemporal autocorrelation taxi trajectory data space-time visualization point patterns urban transportation
Analyzing spatiotemporal autocorrelation would be helpful to understand the underlying dynamic patterns in space and time simultaneously. In this work, we aim to extend the conventional spatial autocorrelation statistics to a more general framework considering both spatial and temporal dimensions. Specifically, we focus on the spatiotemporal version of Getis-Ord's G*. The proposed indicator STG* can quantify the local association of adjacent features in space and time. As a proof of concept, the proposed method is then applied in a large-scale GPS-enabled taxi dataset to identify local spatiotemporal autocorrelation patterns of taxi pick-ups and drop-offs in New York City.