Dealing with noise in crowdsourced GPS human trajectory logging data

Abstract

As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real-world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non-map-matching approach convex hull-based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories.

A Full Life Data Quality Workflow Research and Project Practice

“…In the VGI research field, the data quality is a buzz word. Many works dedicated to this research topic, such as earth system science data quality management and control (Sun and Wang, 2010), reviewing on measures and indicators of VGI data quality (Antoniou and Skopeliti, 2015;Fonte and Antonio et al 2017;Wu and Clarke et al 2020) and challenge study on crowdsourced geospatial data quality (Basiri et al, 2019;Adhinugraha and Rahayu et al 2020). These work focused on the data overall or relative data quality.…”

Human oriented solutions for intelligent analysis, multimedia and communication systems

“…The article entitled “Dealing with Noise in Crowdsourced GPS Human Trajectory Logging Data” by Adhinugraha et al 5 presents new solutions for classifying the noise that might be found from public GPS traces. More than 5300 trajectories that started in the state of Victoria, Australia, were considered, and noise was classified into four types: spike noise, point noise, track noise, and logical noise.…”