Why GPS makes distances bigger than they are

Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are among 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 error. 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 is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and we 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 strong autocorrelation any two consecutive position estimates have very similar error. This error cancels out when average speed, distance or direction are 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 a set of pedestrian and a set of car trajectories. We find that the measurement error in the data is strongly spatially and temporally autocorrelated and give a quality estimate of the data. Finally, we want to emphasize that all our findings are not limited to GPS alone. The systematic bias and all its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.Comment: 17 pages, 8 figures, submitted to IJGI

A space division multiplexed free-space-optical communication system that can auto-locate and fully self align with a remote transceiver

Free-Space Optical (FSO) systems offer the ability to distribute high speed digital links into remote and rural communities where terrain, installation cost or infrastructure security pose critical hurdles to deployment. A challenge in any point-to-point FSO system is initiating and maintaining optical alignment from the sender to the receiver. In this paper we propose and demonstrate a low-complexity self-aligning FSO prototype that can completely self-align with no requirement for initial manual positioning and could therefore form the opto-mechanical basis for a mesh network of optical transceivers. The prototype utilises off-the-shelf consumer electrical components and a bespoke alignment algorithm. We demonstrate an eight fibre spatially multiplexed link with a loss of 15 dB over 210 m

Error sources in the analysis of crowdsourced spatial tracking data

Governments are increasingly interested in the use of crowdsourced spatial tracking data to gain information on the travel behaviour of their citizens. To improve the reliability of reporting in such mobility studies, this paper systematically analyses the propagation of errors from low level operations to high level indicators, such as the modal split and travelled distances. We find that most existing metrics in literature are insufficient to fully quantify this evolution of data quality. The propagation channels are presented schematically and a new approach to quantify the spatial data quality at the end of each processing stage is proposed. This procedure, within the context of Smart Cities, ensures that the data analytics and resulting changes in policy are sufficiently substantiated by credible and reliable information

Analysis of geospatial behaviour of visitors of urban gardens: is positioning via smartphones a valid solution?

Tracking locations is practical and speditive with smartphones, as they are omnipresent devices, relatively cheap, and have the necessary sensors for positioning and networking integrated in the same box. Nowadays recent models have GNSS antennas capable of receiving multiple constellations. In the proposed work we test the hypothesis that GNSS positions directly recorded by smartphones can be a valid solution for spatial analysis of people's behaviour in an urban garden. Particular behaviours can be linked to therapeutic spots that promote health and well-being of visitors. Three parts are reported: (i) assessment of the accuracy of the positions relative to a reference track, (ii) implementation of a framework for automating transmission and processing of the location information, (iii) analysis of preferred spots via spatial analytics. Different devices were used to survey at different times and with different methods, i.e. in the pocket of the owner or on a rigid frame. Accuracy was estimated using distance of each located point to the reference track, and precision was estimated with static multiple measures. A chat-bot through the Telegram application was implemented to allow users to send their data to a centralized computing environment thus automating the spatial analysis. Results report a horizontal accuracy below ~2.3 m at 95% confidence level, without significant difference between surveys, and very little differences between devices. GNSS-only and assisted navigation with telephone cells also did not show significant difference. Autocorrelation of the residuals over time and space showed strong consistency of the residuals, thus proving a valid solution for spatial analysis of walking behaviour

Accuracy of Distance Recordings in Eight Positioning-Enabled Sport Watches: Instrument Validation Study

Elite athletes and recreational runners rely on the accuracy of global navigation satellite system (GNSS)-enabled sport watches to monitor and regulate training activities. However, there is a lack of scientific evidence regarding the accuracy of such sport watches.; The aim was to investigate the accuracy of the recorded distances obtained by eight commercially available sport watches by Apple, Coros, Garmin, Polar, and Suunto when assessed in different areas and at different speeds. Furthermore, potential parameters that affect the measurement quality were evaluated.; Altogether, 3 × 12 measurements in urban, forest, and track and field areas were obtained while walking, running, and cycling under various outdoor conditions.; The selected reference distances ranged from 404.0 m to 4296.9 m. For all the measurement areas combined, the recorded systematic errors (±limits of agreements) ranged between 3.7 (±195.6) m and -101.0 (±231.3) m, and the mean absolute percentage errors ranged from 3.2% to 6.1%. Only the GNSS receivers from Polar showed overall error

Mobility Data Mining for Rural and Urban Map-Matching

Ajalis-ruumiliste andmete kogumine on hoogustunud erinevates rakendustes ja seadmetes. Globaalne positsiooneerimise süsteem (GPS) on kõige populaarsem viis asukoha teave saamiseks. Kaardipunktide vastavusse seadmine on konseptsioon, mis püüab GPS andmeid trajektooris viia vastavusse reaalse teedevõrguga. GPS andmete suurim probleem tuleneb andmete mõõtmis-ja kogumisvigadest ja nende parandamine on suur väljakutse. Käesoleva lõputöö eesmärk on arendada andmete töötlusvoo ja visualiseerimise raamistik muutmaks GPS punktid loogilisteks trajektoorideks ja vigaste GPS punktide asukohtade parandamiseks. Selle eesmärgi saavutamiseks tutvustatakse uut lähenemist trajektooride mustrite leidmiseks.The functionality of gathering spatio-temporal data has seen increasing usage in various applications and devices. The Global Positioning System (GPS) is a satellite navigation system which is mostly used for gathering location information. Map-matching is the procedure of matching trajectories from a sequence of raw GPS data points to the appropriate road networks. GPS data errors are one of the biggest problems and correcting them is a big challenge. The main goal of this thesis work is to build a data pipeline and visualization framework for turning raw GPS data to trajectories and correcting erroneous GPS points by new map-matching approach. For achieving the goal a new approach for trajectory pattern mining is introduced

Theoretical analysis of REM-based handover algorithm for heterogeneous networks

© 2013 IEEE. Handover has been a widely studied topic since the beginning of the mobile communications era, but with the advent of another generation, it is worth seeing it with fresh eyes. Data traffic is expected to keep growing as new use cases will coexist under the same umbrella, e.g., vehicle-to-vehicle or massive-machine-type communications. Heterogeneous networks will give way to multi-tiered networks, and mobility management will become challenging once again. Under the current approach, based uniquely on measurements, the number of handovers will soar, so will the signaling. We propose a handover algorithm that employs multidimensional radio-cognitive databases, namely radio environment maps, to predict the best network connection according to the user's trajectory. Radio environment maps have been extensively used in spectrum-sharing scenarios, and recently, some advances in other areas have been supported by them, such as coverage deployment or interference management. We also present a geometric model that translates the 3GPP specifications into geometry and introduce a new framework that can give useful insights into our proposed technique's performance. We validate our framework through Monte Carlo simulations, and the results show that a drastic reduction of at least 10% in the ping-pong handovers can be achieved, thus reducing the signaling needed

Recording fine‐scale movement of ground beetles by two methods: Potentials and methodological pitfalls

Movement trajectories are usually recorded as a sequence of discrete movement events described by two parameters: step length (distance) and turning angle (bearing). One of the most widespread methods to record the geocoordinates of each step is by a GPS device. Such devices have limited suitability for recording fine movements of species with low dispersal ability including flightless carabid beetles at small spatio‐temporal scales. As an alternative, the distance‐bearing approach can avoid the measurement error of GPS units since it uses directly measured distances and compass azimuths. As no quantification of measurement error between distance‐bearing and GPS approaches exists so far, we generated artificial fine‐scale trajectories and in addition radio‐tracked living carabids in a temperate forest and recorded each movement step by both methods. Trajectories obtained from distance‐bearing were compared to those obtained by a GPS device in terms of movement parameters. Consequently, both types of trajectories were segmented by state‐switching modeling into two distinct movement stages typical for carabids: random walk and directed movement. We found that the measurement error of GPS compared to distance‐bearing was 1.878 m (SEM = 0.181 m) for distances and 31.330° (SEM = 2.066°) for bearings. Moreover, these errors increased under dense forest canopy and rainy weather. Distance error did not change with increasing distance recorded by distance‐bearing but bearings were significantly more sensitive to error at short distances. State‐switching models showed only slight, not significant, differences in movement states between the two methods in favor of the random walk in the distance‐bearing approach. However, the shape of the GPS‐measured trajectories considerably differed from those recorded by distance‐bearing caused especially by bearing error at short distances. Our study showed that distance‐bearing could be more appropriate for recording movement steps not only of ground‐dwelling beetles but also other small animals at fine spatio‐temporal scales

Recording fine‐scale movement of ground beetles by two methods: Potentials and methodological pitfalls

Movement trajectories are usually recorded as a sequence of discrete movement events described by two parameters: step length (distance) and turning angle (bearing). One of the most widespread methods to record the geocoordinates of each step is by a GPS device. Such devices have limited suitability for recording fine movements of species with low dispersal ability including flightless carabid beetles at small spatio‐temporal scales. As an alternative, the distance‐bearing approach can avoid the measurement error of GPS units since it uses directly measured distances and compass azimuths. As no quantification of measurement error between distance‐bearing and GPS approaches exists so far, we generated artificial fine‐scale trajectories and in addition radio‐tracked living carabids in a temperate forest and recorded each movement step by both methods. Trajectories obtained from distance‐bearing were compared to those obtained by a GPS device in terms of movement parameters. Consequently, both types of trajectories were segmented by state‐switching modeling into two distinct movement stages typical for carabids: random walk and directed movement. We found that the measurement error of GPS compared to distance‐bearing was 1.878 m (SEM = 0.181 m) for distances and 31.330° (SEM = 2.066°) for bearings. Moreover, these errors increased under dense forest canopy and rainy weather. Distance error did not change with increasing distance recorded by distance‐bearing but bearings were significantly more sensitive to error at short distances. State‐switching models showed only slight, not significant, differences in movement states between the two methods in favor of the random walk in the distance‐bearing approach. However, the shape of the GPS‐measured trajectories considerably differed from those recorded by distance‐bearing caused especially by bearing error at short distances. Our study showed that distance‐bearing could be more appropriate for recording movement steps not only of ground‐dwelling beetles but also other small animals at fine spatio‐temporal scales