NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks

TL;DR

NUMOSIM is a synthetic mobility dataset designed to facilitate benchmarking of anomaly detection algorithms in geospatial mobility analysis, overcoming real-world data collection challenges.

Contribution

We introduce NUMOSIM, a realistic synthetic dataset with embedded anomalies, enabling rigorous evaluation of anomaly detection methods without privacy or bias concerns.

Findings

NUMOSIM accurately replicates real-world mobility patterns.

The dataset includes diverse anomalies for robust testing.

Benchmark results demonstrate effectiveness of existing detection algorithms.

Abstract

Collecting real-world mobility data is challenging. It is often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomalies in large-scale data is nearly impossible, as it demands meticulous effort to distinguish subtle and complex patterns. These challenges significantly impede progress in geospatial anomaly detection research by restricting access to reliable data and complicating the rigorous evaluation, comparison, and benchmarking of methodologies. To address these limitations, we introduce a synthetic mobility dataset, NUMOSIM, that provides a controlled, ethical, and diverse environment for benchmarking anomaly detection techniques. NUMOSIM simulates a wide array of realistic mobility scenarios, encompassing both typical and anomalous behaviours, generated through advanced deep learning models trained on real mobility data. This approach allows NUMOSIM to accurately replicate the complexities of real-world movement patterns while strategically injecting anomalies to challenge and evaluate detection algorithms based on how effectively they capture the interplay between demographic, geospatial, and temporal factors. Our goal is to advance geospatial mobility analysis by offering a realistic benchmark for improving anomaly detection and mobility modeling techniques. To support this, we provide open access to the NUMOSIM dataset, along with comprehensive documentation, evaluation metrics, and benchmark results.