Correcting temporal bias in mobility data using time-use surveys

TL;DR

This paper addresses temporal bias in GPS mobility data by using time-use surveys to validate behavioral insights and proposes a re-weighting method to improve data accuracy across multiple U.S. cities.

Contribution

It introduces a novel temporal re-weighting technique to correct biases in mobility data, validated against the American Time Use Survey across 11 cities.

Findings

Validation of mobility data with ATUS improves behavioral insights.

Temporal re-weighting reduces bias in mobility analysis.

Method enhances accuracy of economic segregation measures.

Abstract

GPS mobility data is a valuable source of behavioral measurement which is subject to systematic biases including the over- or under-representation of demographic groups, and variations in the quality of location sampling across time. In this paper, we address the challenge of temporal bias in mobility data, which can skew the representation of mobility behaviors due to the event-based nature of location data sampling. We use the American Time Use Survey (ATUS) to assess the accuracy of a place-based measure of economic segregation drawn from large-scale mobility data across 11 U.S. cities. We show that comparisons with high quality time use surveys such as the ATUS can validate behavioral insights from mobility data, while quantifying uncertainty and highlighting areas of relative instability in analytical findings. We also propose a temporal re-weighting method that can complement existing bias-mitigation techniques to improve the accuracy of conclusions drawn from GPS-based mobility data.