Why GPS makes distances bigger than they are. Issue 2 (1st February 2016)

• Record Type: Journal Article
• Authors: Ranacher, Peter; Brunauer, Richard; Trutschnig, Wolfgang; Van der Spek, Stefan; Reich, Siegfried
• Is Part Of: International journal of geographical information science. Volume 30:Issue 2(2016)
• Page Start: 316
• Page End: 333
• Publication Date: 2016-02-01
• Subjects: GPS measurement error -- trajectories -- movement analysis -- autocorrelation -- car movement -- pedestrian movement -- quadratic forms; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2015.1086924
• Languages: English
• ISSNs: 1365-8816

Abstract

ABSTRACT: Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic 'overestimation of distance' becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error ( C ). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.