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On the (In-)Accuracy of GPS Measures of Smartphones: A Study of Running Tracking Applications

Abstract and Figures

Sports tracking applications are increasingly available on the market, and research has recently picked up this topic. Tracking a user’s running track and providing feedback on the performance are among the key features of such applications. However, little attention has been paid to the accuracy of the applications’ localization measurements. In evaluating the nine currently most popular running applications, we found tremendous differences in the GPS measurements. Besides this finding, our study contributes to the scientific knowledge base by qualifying the findings of previous studies concerning accuracy with smartphones’ GPS components.
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On the (In-)Accuracy of GPS Measures of Smartphones:
A Study of Running Tracking Applications
Christine Bauer
Vienna University of Economics and
Business
Welthandelsplatz 1, D2
1020 Vienna, Austria
+43-1-31336-4420
chris.bauer@wu.ac.at
ABSTRACT
Sports tracking applications are increasingly available on the
market, and research has recently picked up this topic. Tracking a
user’s running track and providing feedback on the performance
are among the key features of such applications. However, little
attention has been paid to the accuracy of the applications’
localization measurements. In evaluating the nine currently most
popular running applications, we found tremendous differences in
the GPS measurements. Besides this finding, our study contributes
to the scientific knowledge base by qualifying the findings of
previous studies concerning accuracy with smartphones’ GPS
components.
Categories and Subject Descriptors
H.3.4 Systems and Software: Performance evaluation (efficiency
and effectiveness)
General Terms
Performance
Keywords
Location-based System, GPS, Global Positioning System,
Accuracy, Smartphone, Sports Tracking, Running Tracking,
Localization, Positioning
1. INTRODUCTION
Globally, the number of smartphone users topped one billion at
the end of 2012 and is estimated to double in less than three years
[18]. Spurred by Apple’s and Google’s platform concept, the
development of mobile applications is booming; about 1.7 million
total applications had been created by the end of 2012 [2]. The use
of smartphones is far beyond its classic domain of application, as
applications for all purposes have turned the smartphone into a
multi-functional device that pervades everyday life [12]. There are
applications for every flavor: games, news, guitar tuners, wine
guides, maps, messengers, travel booking, etc. This trend is also
visible in the sports domain, in particular for runninga topic that
is also increasingly getting attention in research [e.g., 1, 4, 15, 17].
Among other features, most running applications include tracking
a user’s running track and providing feedback on the performance
with respect to the distance run and altitude differences. Typically
these figures are visualized with route mapping and diagrams
(Figure 1). The basis for this feature is rooted in location
technologies, which can be considered standard components in
today’s smartphones: Cell Identifier (Cell ID) based positioning,
localization via Wireless Local Area Network (WLAN), and
localization via Global Positioning System (GPS) [13, 16, 26]. For
sports trackingin particular, when in competition with other
athletes or for benchmarking, a feature that most running
applications offer the expectation of users is to receive accurate
tracking information [7]. As a result, WLAN or Cell ID based
tracking are not sufficient for these applications’ purposes because
their positioning results are not fine-granular. Due to its precision,
and worldwide availability [13] without the need for additional
infrastructure [7], GPS is considered the most suitable candidate
for sports tracking applications [7].
29 Comparison of the TOP-10 jogging-applications with GPS-tracking on Android
Accuracy-Test
Sports Tracker took 13 seconds to get a signal, but at least it informs you visually if it has
found a signal already or not. The accuracy yielded mixed results. Total distance of the run
was at 0.99 km, sharing rank 2 with Noom Cardio Trainer & Orux Maps. Unfortunately, total
ascent was 31m, while total descent amounted to 32m. The track on the map would have
been accurate in general, had the start not been off by about 10 meters.
Figure 13 Sports Tracker Web-Interface & map
+ -
Fancy design with a lot of data Fancy design that is very hardware-demanding
Distance accurate Elevation inaccurate
Limited features
Table 7 Sports Tracker Pro/Contra-Summary
Figure 1. Visualized Running Track (left) and Altitude
Differences (right).
However, current smartphones contain very basic GPS receivers
[26]. As a result, the deviations may be larger compared to high-
quality GPS receivers [6]. As smartphones are equipped with
different chip sets, accuracy levels also depend on the respective
devices [25]. Research on measurement accuracy has been
performed to analyze differences between smartphone devices
[14, 25], between platforms [7], between localization technologies
[26], or between GPS based tracking with a smartphone and a
high-quality GPS receiver [16].
In an explorative study analyzing the features of running
applications, we found that identical hardware and platform setup,
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as well as positioning method (using GPS that is embedded into
the smartphone), delivered different tracking results for different
GPS based applications. Having delved into detail on this finding,
this paper presents the results on the measurement accuracy of the
nine currently most popular running applications.
The next section provides background information on location
technologies embedded in smartphones and discusses related
work, comparing different positioning modalities with respect to
method and hardware. Section 3 outlines the procedures of our
comparison study. In Section 4, we present the results concerning
deviations in lengths of distance and altitude differences as
reported by the analyzed applications. Section 5 discusses our
findings and concludes with an outlook on future research.
2. BACKGROUND AND RELATED WORK
2.1 Location Technologies in Smartphones
Location based services (LBS) are services that are accessible
through mobile networks and use the geographical position of a
mobile device [24]. The initial attempts in the field context-aware
computingproduced a plethora of LBS services [21], mainly due
to the fact that devices, at that time, had limited physical-sensing
capabilities and were largely confined to a device’s location. With
the miniaturization of hardware components and proliferation of
smartphones, LBS have become ubiquitously available to
consumers [7] and emerged as an important component of mobile
commerce [19].
Considering the vast availability of LBS applications, we observe
different degrees of granularity and accuracy, and different
requirements on how frequently to refresh location information to
serve the applications’ purposes. While for some applications a
rough estimate may be sufficient, others require more accurate
information [25]. For instance, using the application Foursquare,
people can communicate their current location and their location
history in terms of ‘business premises’, ‘restaurants’, ‘bars’, etc.
The granularity of data required is rather low, as is the needed
frequency for updating localization information. In contrast, other
applications, such as those for car navigation, require high
accuracy and frequent location updates to ensure reliable routing.
In events such as sports competitions, very precise and accurate
positioning information is necessary, which also requires frequent
location updates [7]. To illustrate, Dobson [5] provides a non-
exhaustive list of 18 different ways to gather location information
with different grades of accuracy, depending on the need and
purpose of localization.
Three main location technologies are integrated as standard
components in today’s smartphones: Cell ID, WLAN, and GPS
[13]. Also note that other sensors can be creatively deployed to
sense location, going beyond their primary sensing purposesfor
instance, using a smartphone’s microphone for indoor positioning
[e.g., 20].
Cell ID based localization works as long as there is mobile
network coverage that the smartphone can connect to. The
position of the device is derived from the coordinates of the
serving base station [26]. It is accurate within hundreds of meters
of deviation only. Cell ID information may be combined with a
rough estimation of the round trip time between the device and
base station (so-called ‘timing advance value’), from which the
range between them can be derived. With this combination,
position fixes of higher accuracies can be achieved [13].
The localization with WLAN is based on measuring the proximity
of a mobile device to wireless access points via the intensity of the
received signal (received signal strength; RSS). RSS patterns that
are received at several access points are compared with a table of
predetermined RSS patterns collected at various positions; the
location in the comparison that fits best is adopted as the device’s
position [13, 26]. Based on this mechanism, WLAN based
positioning is accurate within 30 to 50 meters deviation and works
indoors and outdoors [26]. However, as this technology requires
the availability of a wireless hotspot to register in, it is limited to
densely populated areas [13, 26].
The GPS is a satellite based navigation system developed by the
U.S. Department of Defense for military purposes [6]. It allows
locating mobile devices at any place, any time on Earth using
trilateration with range measurements between an observer and a
few visible satellites [6]. At least four independent measurements
(satellites) are required for computing a fixed position [10]. While
the military GPS version is able to provide more accurate
positioning data, its public version is limited to an accuracy of up
to 5 to 10 meters [8, 13]. This accuracy level can also be achieved
with low-cost GPS receivers, such as the ones integrated in
smartphones, when locked onto the minimum number of satellites
[10]. The functionality of GPS is, though, limited to outdoor
positioning as line of sight to the satellites is required [13].
Although the prevalence of GPS receivers has dramatically driven
down cost, size, and power requirements [6], GPS sensing is,
compared to other location technologies, rather resource intensive
[3, 9].
2.2 Studies on Smartphone Tracking
Measurement accuracy using GPS that is embedded in
smartphones has been the subject of many research endeavors.
Menard, Miller, Nowak and Norris [14] analyzed capacity
differences in GPS based positioning in smartphones (using three
different smartphones: Samsung Galaxy S, Motorola Droid X, and
iPhone 4) compared to a vehicle tracker. They concluded that the
three smartphones were an acceptable alternative to other tracking
devices in vehicles. They showed that each smartphone was
accurate within 10 meters about 95 percent of the time.
The differences in positioning accuracy among different Apple
devices (iPhone, iPod Touch, and iPad) have been researched by
von Watzdorf and Michahelles [25]. They found significant
differences in accuracy. However, the different devices under
study used different location technologies, too (WLAN versus a
combination of WLAN, GPS, and Cell ID). In contrast,
Zandbergen [26] evaluated the differences between GPS, WLAN,
and Cell ID based positioning by using only one device
(iPhone 3G) and one application. He found that the WLAN
method has potential for indoor positioning, but outdoors it lags
behind compared to GPS based localization.
Using a self-developed LBS application, Hess, Farahani,
Tschirschnitz and von Reischach [7] compared different
smartphones with different operating systems (Android 2.3.3,
Android 2.3.6, iOS 4.2.1, iOS 4.3.5, and Windows Phone 7) with
different GPS chipsets. They concluded that GPS measurement
accuracy heavily depends on the respective smartphone.
Kost and Brčić [11] analyzed GPS positioning errors based on
weather conditions on two different smartphone devices for one
fixed position, concluding that the research community should
consider that positioning accuracy depends on various factors,
such as space weather related error components.
Mok, Retscher and Wen [16] investigated whether a combination
of different sensors that are embedded into smartphones could
assist GPS positioning to increase the accuracy level. They found
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that, assisted by an accelerometer and digital compass, GPS
positioning accuracy could be improved.
To the best of our knowledge, though, different tracking
applications run on a single device (and having the same setup for
each application) has not yet been researched.
3. RESEARCH DESIGN
In this work, we analyze nine currently popular running
applications that use GPS based localization in real time while
moving (running). The objective is to compare them with respect
to the accuracy of localization measurements.
3.1 Sample
We chose Android for our study for being the platform with the
highest market share (approximately 80 percent in 2013 [23]). In
order to reflect the highest quality and most popular applications
available at the time of our study, we considered those
applications with the highest download rates and user ratings in
the Google Play Store.
As the Google Play Store does not offer a specific running or
sports category, we had to manually search for applicable
applications in the ‘health and fitnesscategory and compare their
download rates and ratings.
We chose the top nine free applications (minimum of one million
downloads by 31-May-2013) as a representative sample for this
evaluation (Table 1). The user rating of all applications in the
sample was high (on average 4.3 stars and above). We had to
exclude the application ‘Nike+ Running’, which was among the
top ten, because it was not compatible with our available testing
device (HTC Desire Bravo).
Finally, we made sure from their descriptions that the applications
relied solely on GPS for tracking the user (i.e., they do not
combine it with further localization technology).
Table 1. Sample description
Application
Downloads
in millions
Last
actualization
Endomondo
5-10
21-May-2013
Runtastic
5-10
26-Apr-2013
Noom Cardio Trainer
5-10
11-Jan-2012
MyTracks
5-10
17-Apr-2013
Runkeeper
1-5
23-May-2013
Sports Tracker
1-5
16-May-2013
MapMyRun GPS
Running
1-5
10-May-2013
Adidas miCoach
1-5
10-May-2013
Orux Maps
1-5
21-Apr-2013
3.2 Testing for Accuracy
All applications were tested with the smartphone model HTC
Desire Bravo’, following the same procedure for each application.
For applications that allowed using other means for localization
(e.g., Sports Tracker), the respective technology was disabled to
ensure that only GPS data is considered.
First, a distance of exactly 500 meters was measured in a highly
populated (city) location, so that running back and forth along this
track in a straight line would result in a total distance of exactly
one kilometer. As the starting and ending points were the same,
the altitude difference between them was ensured to be zero.
Second, a test person ran the measured track back and forth in a
straight line, with each of the applications in the sample. Before
the start of every run, the GPS signal was ensured to be good
enough for adequate measurement (which is a feature of most
running applications). After a run with an application, the
application itself and the Web interface that extended the
application (if available) were checked for the total distance of the
run (the result of which should have been one kilometer) and any
altitude differences (which should have amounted to zero).
Additionally, we analyzed the visualization of the tracked routes
in the application, as these gave good indications about the
accuracy of the tracking measurements.
4. RESULTS
4.1 Distance
Table 2 shows the measurement data of all applications for
distance in alphabetical order. Figure 2 visualizes the distance
inaccuracies, sorted according to deviation.
Table 2. Accuracy measurements for distance
Application
Distance in
meters
Deviation
in meters
Rank
Adidas miCoach
1000
0
1
Endomondo
940
60
8
MapMyRun GPS Running
1030
30
6
MyTracks
1030
30
6
Noom Cardio Trainer
1010
10
2
Orux Maps
1010
10
2
Runkeeper
980
20
5
Runtastic
940
60
8
Sports Tracker
990
10
2
0
10 10 10
20
30 30
60 60
0
10
20
30
40
50
60
70
Adidas miCoach
Noom Cardio Trainer
Sports Tracker
Orux Maps
Runkeeper
MyTracks
MapMyRun GPS Running
Endomondo
Runtastic
meters
Distance inaccuracies in meters
Figure 2. Distance inaccuracies.
Only one application (Adidas miCoach) measured a total distance
of one kilometer. Noom Cardio Trainer, Orux Maps, and Sports
Tracker showed deviations of 10 meters each. Runkeeper was 20
meters off. MyTracks and MapMyRun GPS Running were off by
30 meters. In comparison, Endomondo and Runtastic showed a
MoMM2013 Papers Proceedings of MoMM2013
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deviation in terms of distance that was more than double those of
the other applications (60 meters). As is illustrated in Figure 3,
Endomondo’s measurement was far from a straight-line running
track.
19 Comparison of the TOP-10 jogging-applications with GPS-tracking on Android
Accuracy-Test
The testing experience of Endomondo yielded mixed results. Once the application was
started, ittook9secondstogeta“GPSexcellent”sign.Inthose9seconds,theapplication
did not give any feedback on how the signal-search was progressing. Once the signal was
found, the start-button was hit and the running began. The tracked route showed a starting-
point that differentiated 13 meters from the true starting point, although it took about 60
meters until the app actually showed any data on-screen, which would explain why the total
distance measured did come out at 940 meters. The elevation-measurements were very
inaccurate though. According to Endomondo, total ascent amounted to 10 meters, while
total descent amounted to 23 meters, with a minimum altitude of 196m and a maximum
altitude of 219 meters. Needless to say, these measurements are far from even being
realistic, with the starting point being on the same spot as the end point of the run and a
difference in ascent and descent of 13 meters. The highly inaccurate measurement stats are
shown in Table 2, along with other positive and negative standout-factors. Another nasty
byproduct was the fact that the track of the run was not very accurate on the map. The track
showed a zigzag-course and that the street was crossed multiple times, when in fact it was
run right beside the street the whole time.
Figure 7 Endomondo-track shown on Google Maps
+ -
High range of features Elevation & distance inaccurate
Different types of sports Many features are pro-only or require subscription
Broad network in Europe Track on the map inaccurate
Table 2 Endomondo Pro/Contra-Summary
Figure 3. Endomondo’s tracking data shown on Google Maps.
4.2 Altitude Differences
The measurements for altitude differences had to be unified in
order to be comparable. Some applications divided into ascent and
descent measurements; accordingly these figures were added up in
order to get the total deviation. Other applications only provided
figures for total elevation gained (ascent measurements), which
meant negative altitude differences were not shown in the
applications. Assuming that ascent and descent measurements
must be equal when a track is run back and forth, we compensated
the missing data for descent with the equivalent for ascent. (We
are aware that ascent and descent measurements may be prone to
different measurement problems; since some of the applications
did not report negative altitude differences, we had to act on
assumptions.)
Table 3 shows the measurement data of all applications for
altitude differences in alphabetical order; estimates are given in
italics. Figure 4 visualizes the ascent and descent data, sorted by
total altitude deviation.
Runtastic and Noom Cardio Trainer tracked the elevation
correctly. Runkeeper, Adidas miCoach, MapMyRun GPS
Running, and MyTracks were slightly inaccurate, with deviations
ranging from 8 meters to 14.58 meters. Orux (27 meters) and
Endomondo (33 meters) delivered measurement inaccuracies that
were more than double those of other applications measured. And
worst of all, Sports Tracker doubled those inaccuracy
measurements, reporting 63 meters of deviation.
Table 3. Accuracy measurements for altitude differences
Application
Total
ascent in
meters
Total
descent in
meters
Total
deviation
in meters
Rank
Adidas miCoach
5
5
10
4
Endomondo
10
23
33
8
Noom Cardio
Trainer
0
0
0
1
MapMyRun GPS
Running
6
6
12
5
MyTracks
7.29
7.29
14.58
6
Orux Maps
13
14
27
7
Runkeeper
4
4
8
3
Runtastic
0
0
0
1
Sports Tracker
31
32
63
9
Estimates are given in italics.
0 0 4 5 6 7.29 13 10
31
0 0
4 5 6 7.29
14 23
32
0
10
20
30
40
50
60
70
Runtastic
Noom Cardio Trainer
Runkeeper
Adidas miCoach
MapMyRun GPS Running
MyTracks
Orux Maps
Endomondo
Sports Tracker
meters
Elevation inaccuracies in meters
Total ascent in meters Total descent in meters
Figure 4. Elevation inaccuracies.
4.3 Total Deviation
Summing up the distance and altitude inaccuracies for each
application, Figure 5 represents a clear picture of the potential for
improving tracking accuracy.
The deviation levels of Noom Cardio Trainer and Adidas
miCoach show only slight deviations (10 meters each), while
Endomondo presents the highest total deviation measurement with
93 meters.
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10 10
28
37 42 44.58
60
73
93
0
10
20
30
40
50
60
70
80
90
100
Adidas miCoach
Noom Cardio Trainer
Runkeeper
Orux Maps
MapMyRun GPS Running
MyTracks
Runtastic
Sports Tracker
Endomondo
meters
Total deviation in meters
Figure 5. Total deviation in meters.
5. DISCUSSION AND CONCLUSION
In this paper we presented a study that compares the GPS based
measurements from nine different running applications on a single
smartphone, measured on the same track of one kilometer in an
urban area. The deviations between the distances and altitude
differences measured by the applications were tremendously high.
While the different accuracy levels firstly indicate a quality
ranking of the analyzed applications, this study additionally
contributes to the scientific knowledge base by qualifying the
findings of previous studies in the field.
Our study results show that GPS delivers different results on the
same device. Accordingly, the findings of Menard, Miller, Nowak
and Norris [14] have to be handled with care. The differences that
they found in their study cannot be directly attributed to the
different GPS components used in the analyzed devices.
Moreover, the differences in positioning accuracy found by von
Watzdorf and Michahelles [25] may basically be due to the
different location technologies used for each device setting
(WLAN versus a combination of WLAN, GPS, and Cell ID). This
implies that their setup varied by the involved sensing hardware
and employed technology, causing biased results because their
findings on accuracy can neither be directly attributed to a specific
technology nor to a specific hardware setup.
Zandbergen [26] used a single device research design to analyze
differences between GPS, WLAN, and Cell ID based positioning.
In our study, however, GPS delivered different results for each
analyzed application. Accordingly, the ‘ranking’ of these
technologies’ accuracy levels has to be handled with care.
Hess, Farahani, Tschirschnitz and von Reischach [7] conclude in
their study that GPS measurement accuracy depends on the
respective smartphone. Similar to the study by von Watzdorf and
Michahelles [25], the study setup mixing hardware (GPS
chipsets) and platforms does not allow for conclusions
concerning the devices, as the operating system may also have
influenced the results. In addition, we show in our study that GPS
measurements on a given device may vary with the application.
Although we show that previous work has limitations, we
appreciate these studies’ contributions to the knowledge base, as
they provided first indications in the field and triggered further
research with respect to combining location technologies to
deliver more accurate results [e.g., 16, 22].
Our work also faces limitations. Firstly, we did not control for
crowdedness and traffic when tracking the locations. Also, the
phones’ internal activity (lowering read out frequency) as well as
temporary surrounding influences, such as the reflection of signals
disturbing GPS reception, cannot be excluded as influencing
factors. Differences with respect to these issues may have
influenced the GPS measurement results, causing more deviations
for some applications and less for others. Future work should
control for this; it may, for instance, be addressed by running the
track several times with each application. Alternatively, one
runner could wear 9 phones of the same type, each running one of
the apps. Secondly, we did not control for space weather
influence, such as Kos and Brčić [11] did rigidly. Thirdly, we
tracked a rather short distance of one kilometer. It is still unclear
how measurements develop over long distances. If, for instance,
Endomondo would keep its deviations per kilometer, a marathon
(42.195 kilometers) would result in a deviation of 2531.7 meters.
For a runner that maintains a pace of 5 minutes per kilometer, that
would distort his/her performance by 12.6585 minutes. For casual
runners and short distance tracks, that might not be an important
issue. On the other hand, for runners preparing for a marathon,
such a deviation is not acceptable as it can cause severe health
problems. As a result, longer distances should be evaluated in
future investigations.
In conclusion, this paper provided an overview of the currently
most popular running applications for smartphones and evaluated
them for measurement accuracies with GPS. We identified
deviating results for each application, which implies influencing
factors on GPS accuracy apart from GPS component and location
of the track, which were held constant for the study. As previous
studies did not emphasize this issue, we suggest that future work
should pay more attention to these influencing factors.
6. ACKNOWLEDGEMENTS
We thank Dominik Günsberg for his contribution of running the
track with each application of the sample.
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Proceedings of MoMM2013 MoMM2013 Papers
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... Especially Global Positioning System (GPS) sensors, which were integrated into smartphones, enabled endurance runners to track their performance. Based on the GPS data, the distance, pace, and altitude profile of workouts could be analyzed [31]. Additionally, more and more people started tracking their heart rate using either chest straps or Photoplethysmography (PPG) sensors [32,33]. ...
Thesis
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Body-worn sensors, so-called wearables, are getting more and more popular in the sports domain. Wearables offer real-time feedback to athletes on technique and performance, while researchers can generate insights into the biomechanics and sports physiology of the athletes in real-world sports environments outside of laboratories. One of the first sports disciplines, where many athletes have been using wearable devices, is endurance running. With the rising popularity of smartphones, smartwatches and inertial measurement units (IMUs), many runners started to track their performance and keep a digital training diary. Due to the high number of runners worldwide, which transferred their data of wearables to online fitness platforms, large databases were created, which enable Big Data analysis of running data. This kind of analysis offers the potential to conduct longitudinal sports science studies on a larger number of participants than ever before. In this dissertation, both studies showing how to extract endurance running-related parameters from raw data of foot-mounted IMUs as well as a Big Data study with running data from a fitness platform are presented.
... Contrarily, the GNSS device had an accuracy of under one meter. The low accuracy of the smartphone GPS chip is known and was shown before (Mok et al. 2012;Bauer 2013). Hence, a GNSS receiver is preferred if a highly accurate localization is needed or in areas with limited smartphone GPS accuracy, respectively. ...
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The use of explosives has led to a widespread distribution of 2,4,6-trinitrotoluene (TNT) and its by- and degradation products in the soil on former production and testing sites. The investigation of those large contaminated sites is so far based on a few selected soil samples, due to high costs of conventional HPLC and GC analysis, although huge differences in concentrations can already be found in small areas and different collection depths. We introduce a novel high-throughput screening system for those areas, which combines a smartphone-based collection of GPS data and soil characteristics with a fast MALDI-TOF MS quantification of explosives in soil sample extracts and finally a heatmap visualization of the explosives’ spread in soil and an analysis of correlation between concentrations and soil characteristics. The analysis of a 400 m ² area presented an extensive contamination with TNT and lower concentrations of the degradation and by-products aminodinitrotoluenes (ADNT) and dinitrotoluenes (DNT) next to a former production facility for TNT. The contamination decreased in deeper soil levels and depended on the soil type. Pure humus samples showed significantly lower contaminations compared to sand and humus/sand mixtures, which is likely to be caused by an increased binding potential of the humic material. No correlation was found between the vegetation and the concentration of explosives. Since the results were obtained and visualized within several hours, the MALDI-TOF MS based comprehensive screening and heatmap analysis might be valuable for a fast and high-throughput characterization of contaminated areas.
... Recently, volunteered geographic information (VGI) data have fundamentally enriched the information content of geospatial research and raised concerns about data quality (Basiri et al. 2019). Bauer (2013) compared different running programs and showed how GPS in smartphones generates low-quality traces. Mooney et al. (2016) proposed that problems such as non-professional volunteers and the absence of protocols can lead to low data quality and missing data. ...
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Geospatial studies must address spatial data quality, especially in data-driven research. An essential concern is how to fill spatial data gaps (missing data), such as for cartographic polylines. Recent advances in deep learning have shown promise in filling holes in images with semantically plausible and context-aware details. In this paper, we propose an effective framework for vector-structured polyline completion using a generative model. The model is trained to generate the contents of missing polylines of different sizes and shapes conditioned on the contexts. Specifically, the generator can compute the content of the entire polyline sample globally and produce a plausible prediction for local gaps. The proposed model was applied to contour data for validation. The experiments generated gaps of random sizes at random locations along with the polyline samples. Qualitative and quantitative evaluations show that our model can fill missing points with high perceptual quality and adaptively handle a range of gaps. In addition to the simulation experiment , two case studies with map vectorization and trajectory filling illustrate the application prospects of our model.
... A partir del desarrollo de los servicios LBS (Location based services 29 ) en los teléfonos inteligentes, se incorporaron en ellos los receptores GPS (Bauer, 2013), pero además están equipados usualmente de un sistema asistido (A-GPS 30 ). Este es un mecanismo que emplea la red de telefonía celular de forma combinada a las señales satelitales para determinar las posiciones con mayor velocidad (Vallina-Rodriguez et al., 2013), reducir el tiempo de adquisición de los GPS tradicionales y contrarrestar los errores de multi-recorrido en zonas citadinas (Bierlaire et al., 2013), aunque se sacrifica precisión (Massad y Dalyot, 2018;Zandbergen, 2009;Zandbergen y Barbeau, 2011). ...
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La aparición de los Sistemas Globales de Navegación Satelital o GNSS en 1974, con el sistema GPS han conducido a un fuerte cambio en los métodos de trabajo de campo y actualmente, los receptores GNSS son herramientas básicas para muchos investigadores. A pesar de ello, muchas veces se ignoran los aspectos básicos de funcionamiento de estos sistemas de posicionamiento satelital a pesar de que, en investigaciones enfocadas al análisis de la biodiversidad u otros aspectos ambientales, estos pueden tener repercusiones metodológicas. Elementos relacionados con las nuevas constelaciones satelitales, la precisión, las fuentes de errores o los tipos de receptores deben considerarse para tomar datos de campo o identificar el equipo más apropiado para los objetivos de un estudio específico. Por esta razón, en la presente comunicación se dan a conocer los aspectos básicos de la tecnología de los GNSS actuales, que deben ser conocidos por los investigadores de campo que los utilizan. Se ofrecen los conceptos básicos de estos sistemas y se discuten aspectos relacionados a sus precisiones y fuentes de error que deben ser tenidos en cuenta cuando se utilizan en investigaciones científicas. coordenadas geográficas, GNSS, GPS, mapear, sistemas de ubicación The emerging of Global Navigation Satellite Systems or GNSS in 1974, with the first GPS system, has led to a strong change in field work methods. Currently, GNSS receivers are basic tools for many researchers. Despite this, many people ignore the basic aspects of the operation of these satellite positioning systems, despite the fact that in researches focused on the analysis of biodiversity or other environmental aspects, these may have methodological repercussions. Issues related to new satellite constellations, accuracy, error sources or receiver types must be taken into account when collecting field data or identifying the most appropriate equipment for the objectives of a specific study. For this reason, this communication presents the basic aspects of current GNSS technology, which should be known by the field researchers who use them. The basic concepts of this field and elements about its precisions and sources of error are offered. These elements should be considered when using these systems in scientific research.
... Los GPS se incorporaron a los celulares para desarrollar los llamados "servicios basados en ubicación" (Location based services -LBS), que permiten al teléfono inteligente acceder a información espacial personalizada en tiempo real por medio de las redes informáticas (Bauer 2013). Los celulares modernos, además de los receptores para estas señales, están equipados usualmente de un sistema GPS asistido (A-GPS) (Vallina-Rodriguez & al. 2013). ...
Article
Full-text available
Actualmente, la tecnología de los celulares o teléfonos inteligentes ha resultado en equipos electrónicos, altamente sofisticados y con una amplia gama de sensores integrados, entre los que se encuentran los receptores GPS/GNSS. Estos se incorporaron a los celulares para desarrollar los servicios basados en ubicación, que permiten acceder a información espacial personalizada, en tiempo real, por medio de las redes informáticas. Todavía existe desconfianza sobre el valor de estos sensores para la actividad científica, pero existe un número creciente de publicaciones que los han validado para este uso. En este trabajo se hace una revisión de la literatura científica en el campo de la Ecología y las investigaciones medioambientales a nivel mundial y la postura de sus autores en relación al empleo de los sensores de ubicación presentes en los celulares inteligentes. Además de los sistemas GPS asistidos, los modelos más recientes tienen receptores multi-constelación y de doble frecuencia con precisiones similares a las de otros GPS comerciales, a nivel de pocos metros, aunque varios factores deben ser considerados, como el modelo del celular, la aplicación empleada, el lugar donde se toman las mediciones y el objetivo del trabajo. Si se siguen protocolos apropiados de validación y se selecciona cuidadosamente la aplicación para tomar los datos, se ha demostrado que estos sensores de los telé-fonos inteligentes modernos pueden ser alternativas razonables y de calidad suficiente para la mayoría de los trabajos de campo en Ecología. ABSTRACT: Currently smartphones technology results in small portable devices, highly sophisticated and with a wide range of integrated sensors, among which are the GPS/GNSS receivers. This were incorporated to smartphones to developed location based services, which allows access to personalized spatial information, by informatics networks. There is still some distrust on the value of these sensors for scientific research but a growing number of papers had validated their use. In the current review we present the position of the general scientific literature in ecology and environmental fields worldwide in relation to location sensors at smartphones. Besides assisted GPS system, modern models incorporate multi-constellation and double frequency receivers with spatial precision similar to other commercial GPS, around few meters. Several factors should be taken into account such as smartphone model, measurement places and the goal of the research. By following appropriated validation protocols and carefully selecting the app to gather data, it has been demonstrated that this sensors in modern smartphones can be reasonable alternatives with enough quality for most of the field work uses in Ecology.
... This would alter a runner's performance by 12.6585 minutes if he or she runs at a 5 minute per kilometre speed. [15] The beacon stations use maritime radio beacon frequencies. In real time, the corrective data often offers 1-to 5-meter accuracy. ...
Preprint
Vehicle security refers to securing the vehicle against potential thieves. Vehicle makers have been experimenting with various approaches in order to give improved security systems. The usage of GPS and GSM technology can provide security for the car. The security of a vehicle is ensured in this project by tracking its whereabouts. For the integration of the global positioning system, the EKF is widely utilised (GPS) When the system's non-linearity grows or the measurement co-variance is erroneous, the EKF’s performance deteriorates. An extended state observer is introduced for the first time to improve loosely coupled GPS navigation performance utilizing accurate measurement modeling. The RMS values of positional errors are reduced by 52.57 percent, 48.56 percent, and 34.16 percent in the north, east, and vertical directions, respectively.
... El tercero de los sensores más extendidos entre los dispositivos inteligentes son los receptores GPS (Global Positioning System) (aunque se debe generalizar al sistema GNSS: Global Navigation Satellite System). Los GPS son uno de los servicios satelitales que se ha convertido en herramientas de uso diario y fueron incorporados a los celulares para desarrollar los servicios basados en ubicación (Location based services -LBS), que permiten al celular acceder a información espacial personalizada en tiempo real por medio de las redes informáticas (Bauer 2013). Pero cuando las capacidades de generación de datos de los teléfonos se acoplan a los GPS, puede obtenerse información georreferenciada con la cual crear mapas de variación espacio temporal de muchos factores ambientales (Sicard & al. 2015). ...
Article
Full-text available
Los teléfonos celulares han irrumpido en todos los aspectos de la vida de la mayor parte de la humanidad, incluyendo las actividades profesionales y científicas. Numerosas aplicaciones apoyan al investigador en el seguimiento de protocolos experimentales, manejo de bibliografía y como vía de conexión inalámbrica con otros equipos. Pero la amplia gama de sensores miniaturizados integrados que poseen, de alta precisión y que actúan en aspectos ocultos del funcionamiento del equipo, no ha sido aún lo suficientemente explotada. Los celulares modernos contienen potentes cámaras digitales, micrófonos, receptores GPS/GNSS, acelerómetros, giroscopios, sensores de magnetismo, luxómetros, barómetros, termómetros, sensores de humedad, sensores biométricos y muchos otros, que tienen el potencial de convertirse en importantes aliados para la recolecta de datos durante el trabajo de un investigador. A partir de ellos han aparecido las aplicaciones de brújulas, altímetros, escáneres, lectores de códigos de barras o QR, identificadores de rostros, sonidos o especies, detectores de metales, de movimientos o de vibraciones, podómetros, colorímetros, espectrómetros y muchas más. Todas estas herramientas están impactando un amplio espectro de campos científicos como la medicina, las ciencias sociales, el monitoreo ambiental, el transporte y la industria. Sin embargo, aún existe desconocimiento de sus ventajas y posibilidades, por lo cual, en este trabajo, se hace una revisión de las potencialidades que brindan estos sensores y sus aplicaciones en las investigaciones biológicas. En condiciones donde el equipamiento tecnológico es limitado, los celulares, sus sensores y las aplicaciones correspondientes pueden ser alternativas eficientes para sobrellevar la brecha tecnológica y aumentar la calidad de las investigaciones.
Chapter
Unlike previous texts that have focussed on migratory patterns of tourists and new mobilities in tourism, Tracking Tourists: Movement and mobility is the first text to address tourist movement in from a methodological angle in the post-digital era. It assesses how movement and migration has been recorded in the past, how it may be recorded and assessed now and the possibilities for exploring movement in the future. Using international case studies that are both current and historical, it explores the range of options that exist for assessing tourists’ movement, along with the relative merits of each method. It will give a special focus to new technologies that facilitate our understanding of movement, such as the use of big data, hashtag scraping, Wi-Fi tracking, farming data from mobile phone towers and cutting-edge GPS tracking. It discusses the positive and negative consequences of the use of these new technologies and tackles issues such as ethical dilemmas and future trends and technology needs.
Conference Paper
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In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This paper presents an approach that uses GPS to derive relative location information for multiple receivers. Nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. The system has been implemented using a network of Android phones equipped with a custom Bluetooth headset and integrated GPS chip to provide raw measurement data. Our evaluation shows that centimeter-scale tracking accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is more than an order of magnitude more accurate than simply taking the difference of reported absolute node coordinates or other simplistic approaches due to the presence of uncorrelated measurement errors.
Conference Paper
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Handheld GPS devices are nowadays increasingly replaced by smartphones with Android-based GPS applications. Moreover, it becomes a matter of concern, given that the position derived with this application is taken for granted, with over- reliance from users not aware of potential misleadings and errors the position can befall. For this purpose, two mobile devices were fitted at fixed point in south Croatia, previously determinated by differential means. At this obstruction-free location, positioning applications are launched and received positions have been recorded during hours. After several recordings and data post-processing, analysis have been carried out in order to determine position deviations in all directions. Here, the results obtained are presented, giving an insight in GPS positioning accuracy at the time of observation. The paper represents the basis for further work, where the same cell phone application will be elaborated in various, more critical situations.
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We report on our research on GymSkill, a smartphone system for com- prehensive physical exercising support, from sensor data logging, activity recognition to on-top skill assessment, using the phones built-in sensors. In two iterations, we used principal component breakdown analysis (PCBA) and criteria-based scores for individualized and personalized automated feedback on the phone, with the goal to track training quality and success, as well as to engage and motivate regular exercising. Qualitative feedback was col- lected in a user study, and the system showed good evaluation results in an evaluation against manual expert assessments of video-recorded trainings.
Conference Paper
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It is a natural predisposition of humans to respond to the rhythmical qualities of music. Now, we turn the setting around: The music responds to the user’s behavior. So-called ‘reactive music’ is a non-linear format of music that is able to react to the listener and her or his environment in real-time. Giant Steps is an iPhone application that implements such reactive music in correspondence to a jogger’s movements and the sounds in her or his environment. We hope that our approach contributes to a better understanding of ‘machine to user’ adaption, and to mobile sports applications in particular.
Conference Paper
Smartphones with their GPS capabilities allow tracking in numerous scenarios at low costs. Whereas most scenarios need only coarse tracking, real-time tracking of competitors in sport events require fine-granular localization with high refresh frequencies. This work is conducted in the context of the sailing sports and tests the applicability of scenarios with fine-granularity requirements to today's available smartphones. We first describe our methodology to test smartphones for their suitability for fine-granular tracking. We execute a comparative study involving six modern smartphones running on three different mobile platforms and an additional dedicated GPS tracker. The GPS performance metrics accuracy, battery life, integrity and continuity were tested in four experimental setups, which were chosen with the intent to make the results applicable to real-world sports tracking scenarios. Our results show that with many of today's smartphones it is possible to fulfill fine-granularity requirements. But they also point out some devices' deficiency in integrity and continuity. Our results lead to guidelines relevant for GIS: tracking performance measurement, mobile platform and device selection, tracking application development and operation.
Conference Paper
We present RoomSense, a new method for indoor positioning using smartphones on two resolution levels: rooms and within-rooms positions. Our technique is based on active sound fingerprinting and needs no infrastructure. Rooms and within-rooms positions are characterized by impulse response measurements. Using acoustic features of the impulse response and pattern classification, an estimation of the position is performed. An evaluation study was conducted to analyse the localization performance of RoomSense. Impulse responses of 67 within-rooms positions from 20 rooms were recorded with the hardware of a smartphone. In total 5360 impulse response measurements were collected. Our evaluation study showed that RoomSense achieves a room-level accuracy of > 98% and a within-rooms positions accuracy of > 96%. Additionally, the implementation of RoomSense as an Android App is presented in detail. The RoomSense App enables to identify an indoor location within one second.
Conference Paper
Modern smartphones normally incoporate a high sensitivity GPS receiver, Wi-Fi card, and sensors such as accelerometer, digital compass and digital barometer. These components are low-cost, and are designed mainly for leisure and gaming applications. This study aims to investigate the combination of the built-in GPS and sensor data of smartphones for localization in dense urban environments, where very often satellite signals are obstructed by tall buildings or large structures, causing insufficient number of GNSS measurement data for successful position determination. This paper is the continuation of our investigations on the characteristics of data outputs from a digital compass and accelerometer in relation to the orientation and phone movements presented in Mok et al. (2011) [7]. In the following, we will focus our discussions on the integration of GPS, digital compass, and accelerometer for vehicle tracking applications. Our investigation results show that the distance and orientation data derived from the outputs of the accelerometer and digital compass is generally sufficient to provide the shape of the path that the vehicle has travelled, with a varying scalar error. Magnetic to grid north correction, however, is necessary to improve the heading. By reducing the data sampling period from 30 seconds to 1 second, the scalar error can be significantly reduced. Moreover, correction for the gravity effect on the x-, y- and z-axes of the smartphone's local coordinate system is the key to correct the determination of accelerometer-derived distance travelled.
Book
This thoroughly updated second edition of an Artech House bestseller brings together a team of leading experts who provide a current and comprehensive treatment of the Global Positioning System (GPS). The book covers all the latest advances in technology, applications, and systems. The second edition includes new chapters that explore the integration of GPS with vehicles and cellular telephones, new classes of satellite broadcast signals, the emerging GALILEO system, and new developments in the GPS marketplace. This single-source reference provides a quick overview of GPS essentials, an in-depth examination of advanced technical topics, and a review of emerging trends in the GPS industry.
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The recent boom of GPS (Global Positioning System) as a universal method of location has meant that most people in developed countries have already used this technology sometime in their lives. However, this system suffers from an ever-increasing problem: energy expenditure. GPS receivers have been integrated into increasingly smaller devices such as the latest generation of mobiles, thereby making battery-saving a priority in the use of this technology. This article lays out a series of ideas which, through the use of auxiliary technologies, are able to maximize energy saving. By means of outdoor exit detection, it will be possible to automatically disconnect the GPS while the user stays indoors and later reconnect it on leaving the building.
Article
The presence of 3D acceleration sensors in mobile devices has already raised a new range of context-aware applications, in particular in the sports and wellness sector. In this paper, we present an accelerometer-based step counter middleware for J2ME-enabled smartphones to simplify the development of activity aware applications, creating an abstraction layer between the client and the signal processing algorithms and raw sensor access. The service provides information about the step count, stop detection and changes in the phone's orientation, independently of the phone's location on the human body. The software package runs natively on Symbian S60 phones, providing an interface to J2ME applications and has been validated experimentally on a Nokia's N95 smartphone.