GEO-BLEU: Similarity Measure for Geospatial Sequences

TL;DR

This paper introduces GEO-BLEU, a new similarity measure for geospatial sequences inspired by BLEU, which incorporates spatial proximity to improve evaluation of generated trajectories, outperforming existing methods.

Contribution

The paper proposes GEO-BLEU, a novel similarity metric for geospatial sequences that combines BLEU with spatial proximity, enhancing trajectory evaluation.

Findings

GEO-BLEU outperforms dynamic time warping in similarity assessment.

Crowdsourced data validates GEO-BLEU's effectiveness.

GEO-BLEU provides both quantitative and qualitative improvements.

Abstract

In recent geospatial research, the importance of modeling large-scale human mobility data and predicting trajectories is rising, in parallel with progress in text generation using large-scale corpora in natural language processing. Whereas there are already plenty of feasible approaches applicable to geospatial sequence modeling itself, there seems to be room to improve with regard to evaluation, specifically about measuring the similarity between generated and reference trajectories. In this work, we propose a novel similarity measure, GEO-BLEU, which can be especially useful in the context of geospatial sequence modeling and generation. As the name suggests, this work is based on BLEU, one of the most popular measures used in machine translation research, while introducing spatial proximity to the idea of n-gram. We compare this measure with an established baseline, dynamic time warping, applying it to actual generated geospatial sequences. Using crowdsourced annotated data on the similarity between geospatial sequences collected from over 12,000 cases, we quantitatively and qualitatively show the proposed method's superiority.