Garden city: A synthetic dataset and sandbox environment for analysis of pre-processing algorithms for GPS human mobility data

TL;DR

This paper introduces a synthetic dataset and sandbox environment for evaluating pre-processing algorithms in GPS human mobility data, addressing the lack of ground-truth data and enabling validation of algorithm robustness.

Contribution

It presents a novel synthetic trajectory simulator and sandbox environment that replicates commercial dataset features, facilitating algorithm validation and comparison.

Findings

Provides a publicly available open-source tool

Enables validation of processing algorithms against ground-truth data

Supports analysis of errors caused by data sparsity

Abstract

Human mobility datasets have seen increasing adoption in the past decade, enabling diverse applications that leverage the high precision of measured trajectories relative to other human mobility datasets. However, there are concerns about whether the high sparsity in some commercial datasets can introduce errors due to lack of robustness in processing algorithms, which could compromise the validity of downstream results. The scarcity of "ground-truth" data makes it particularly challenging to evaluate and calibrate these algorithms. To overcome these limitations and allow for an intermediate form of validation of common processing algorithms, we propose a synthetic trajectory simulator and sandbox environment meant to replicate the features of commercial datasets that could cause errors in such algorithms, and which can be used to compare algorithm outputs with "ground-truth" synthetic trajectories and mobility diaries. Our code is open-source and is publicly available alongside tutorial notebooks and sample datasets generated with it.