A Sensor-Based Simulation Method for Spatiotemporal Event Detection

TL;DR

This paper introduces a sensor-based simulation approach using the Discrete Empirical Interpolation Method to detect spatiotemporal events in urban environments by comparing observed and simulated data from key locations.

Contribution

It presents a novel method for event detection that identifies key sensors and uses simulation to detect deviations indicating events, improving urban activity analysis.

Findings

Effective detection of when and where events occur in NYC.

Method leverages key location identification for accurate simulation.

Demonstrates applicability to real-world urban data.

Abstract

Human movements in urban areas are essential to understand human-environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as 'sensors' , which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key lo-cations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur.