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10.1080/13658816.2015.1086924 http://scihub22266oqcxt.onion/10.1080/13658816.2015.1086924 C4786863!4786863 !27019610     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27019610 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27019610 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Int+J+Geogr+Inf+Sci 2016 ; 30 (2 ): 316-333 Nephropedia Template TP {{tp|p=27019610 |t=2016. Why GPS makes distances bigger than they are |pdf=|usr=}}{{27019610 }} gab.com Text Why GPS makes distances bigger than they are JO: Int J Geogr Inf Sci http://www.kidney.de/pget.php?&tw=1&pmid=27019610 #FOAvip 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. Twit Text FOAVip Why GPS makes distances bigger than they are ????Int J Geogr Inf Sci http://www.kidney.de/pget.php?&tw=1&pmid=27019610 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27019610 #FOAvip English Wikipedia <ref>{{Cite pmid|27019610 }}</ref> Why GPS makes distances bigger than they are #MMPMID27019610 Ranacher P ; Brunauer R ; Trutschnig W ; Van der Spek S ; Reich S Int J Geogr Inf Sci 2016[Feb]; 30 (2 ): 316-333 PMID27019610 show ga 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. äWhy GPS makes distances bigger than they are (epmc).pdf DeepDyve Pubget Overpricing