Journal articles: 'GPS location data'

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Cochrane, Madaline, Donald Brown, and Ron Moen. "GPS Technology for Semi-Aquatic Turtle Research." Diversity 11, no. 3 (March 1, 2019): 34. http://dx.doi.org/10.3390/d11030034.

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Global positioning system (GPS) telemetry units are now small enough to be deployed on terrestrial and semi-aquatic turtles. Many of these GPS units use snapshot technology which collects raw satellite and timestamp data during brief periods of data recording to minimize size. We evaluated locations from snapshot GPS units in stationary tests and on wood turtles (Glyptemys insculpta) in northeastern Minnesota. Stationary GPS units were placed in wood turtle habitat to evaluate location accuracy, fix success rate, and directional bias. The GPS fix success rate and accuracy were reduced in closed canopy conditions and when the stationary GPS unit was placed under a log to simulate wood turtle hiding behavior. We removed GPS location outliers and used a moving average calculation to reduce mean location error in stationary tests from 27 m (SD = 38) to 10 m (SD = 8). We then deployed GPS units and temperature loggers on wood turtles and collected 122,657 GPS locations and 242,781 temperature readings from 26 turtles from May to September 2015 and 2016. Location outliers accounted for 12% of locations when the GPS receiver was on a turtle. We classified each wood turtle location based on the GPS location and by comparing temperature profiles from river, sun, and shaded locations to the temperature logger on the turtle. We estimated that wood turtles were on land 68% (SD = 12) of the time from May to September. The fix success rate for land locations was 38% (SD = 9), indicating that wood turtles often use habitats with obstructed views of the sky. Mean net daily movement was 55 m (SD = 192). Our results demonstrate that snapshot GPS units and temperature loggers provide fine-scale GPS data useful in describing spatial ecology and habitat use of semi-aquatic turtles.

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Peng, X., C. Wang, K. Liu, A. Quinones, S. Li, and J. Shan. "EXPLORATION OF MUNICIPAL MOBILITY USING SMARTPHONE GPS DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2020 (August 3, 2020): 209–15. http://dx.doi.org/10.5194/isprs-annals-v-4-2020-209-2020.

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Abstract. Due to the advances in location-acquisition techniques, smartphone GPS location data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study human behaviour through exploring patterns from these data. In this paper, we use smartphone GPS location data to investigate municipal mobility. Kernel density estimation and emerging hot spot analysis are used to the GPS dataset to demonstrate GPS user (point) distribution across space and time. Flow maps are capable of tracking clustering behaviours and direction maps drawn upon the orientation of vectors can precisely identify location of the events. Case study with Indianapolis metropolitan area traffic rush hour verifies the effectiveness of these methods. Furthermore, we identify smartphone GPS data of vehicles and develop a concise and effective method for traffic volume estimation for county highway network. It is shown that the developed smartphone GPS data analytics is powerful for predicting reliable annual average daily traffic estimation. The study showcases the capability of GPS location data in identifying municipal mobility patterns for both citizens and vehicles.

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Kunisada, Yugo, and Chinthaka Premachandra. "High Precision Location Estimation in Mountainous Areas Using GPS." Sensors 22, no. 3 (February 2, 2022): 1149. http://dx.doi.org/10.3390/s22031149.

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Outdoor recreation has become popular in recent years, against the backdrop of the new coronavirus epidemic that started in 2020. Mountaineering, in particular, has become a popular pastime for many people as an easy way to experience nature. However, the number of mountaineering accidents is increasing, owing to the inadequate knowledge and equipment for beginners. In particular, the lack of map-reading skills and experience often leads to the selection of wrong trails. The smartphones used for precise location information obtain correction information from radio waves from a base station, and the accuracy of using only the GPS in mountainous areas without radio waves is questionable. In general, the GPS position correction methods in the literature for such situations include complex processing of the GPS radio waves. Some of these methods have been proposed with complex hardware and are difficult to implement with portable hardware. In this study, we develop and demonstrate a method for obtaining accurate location information using GPS without the error correction of radio waves, even in mountainous areas. The multipath is the reason for most of the GPS errors in the mountains. In the mountains, depending on the locations, the correct GPS location can also be received. In the proposed method, the correct GPS data are used to detect the incorrect GPS locations. We present an experimental method for estimating the interrelationship between the GPS longitude and latitude data. Additionally, we demonstrate the effectiveness of our method by showing that the experimental mountain location data presented in this paper are more accurate than the GPS data alone.

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Zandbergen, Paul A., and Sean J. Barbeau. "Positional Accuracy of Assisted GPS Data from High-Sensitivity GPS-enabled Mobile Phones." Journal of Navigation 64, no. 3 (June 7, 2011): 381–99. http://dx.doi.org/10.1017/s0373463311000051.

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Utilizing both Assisted GPS (A-GPS) techniques and new high-sensitivity embedded GPS hardware, mobile phones are now able to achieve positioning in harsh environments such as urban canyons and indoor locations where older embedded GPS chips could not. This paper presents an empirical analysis of the positional accuracy of location data gathered using a high-sensitivity GPS-enabled mobile phone. The performance of the mobile phone is compared to that of regular recreational grade GPS receivers. Availability of valid GPS position fixes on the mobile phones tested was consistently close to 100% both outdoors and indoors. During static outdoor testing, positions provided by the mobile phones revealed a median horizontal error of between 5·0 and 8·5 m, substantially larger than those for regular autonomous GPS units by a factor of 2 to 3. Horizontal errors during static indoor testing were larger compared to outdoors, but the difference in accuracy between mobile phones and regular GPS receivers was reduced. Despite the modest performance of A-GPS on mobile phones, testing under various conditions revealed that very large errors are not very common. The maximum horizontal error during outdoor testing never exceeded 30 metres and during indoor testing never exceeded 100 metres. Combined with the relatively consistent availability of valid GPS position fixes under varying conditions, the current study has confirmed the reliability of A-GPS on mobiles phones as a source of location information for a range of different LBS applications.

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Saeb, Sohrab, Emily G. Lattie, Stephen M. Schueller, Konrad P. Kording, and David C. Mohr. "The relationship between mobile phone location sensor data and depressive symptom severity." PeerJ 4 (September 29, 2016): e2537. http://dx.doi.org/10.7717/peerj.2537.

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BackgroundSmartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify depressive symptom severity.MethodsWe used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate depressive symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks.ResultsThe results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r’s ranging from −0.43 to −0.46,p-values < .05). We also found that these relationships were stronger when GPS features were calculated from weekend, compared to weekday, data. Although the correlation between baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features.DiscussionOur findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of depressive symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict depressive symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.

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Desai, Pooja, Parth Desai, Komal Ajmera, Khushbu Mehta, and Amit Deshmukh. "Data Logger and Accident Location using GSM and GPS." International Journal of Computer Applications 101, no. 8 (September 18, 2014): 37–40. http://dx.doi.org/10.5120/17711-8730.

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Laha, Arnab Kumar, and Sayan Putatunda. "Real time location prediction with taxi-GPS data streams." Transportation Research Part C: Emerging Technologies 92 (July 2018): 298–322. http://dx.doi.org/10.1016/j.trc.2018.05.005.

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Liang, Yi, Zhipeng Cai, Qilong Han, and Yingshu Li. "Location Privacy Leakage through Sensory Data." Security and Communication Networks 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/7576307.

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Mobile devices bring benefits as well as the risk of exposing users’ location information, as some embedded sensors can be accessed without users’ permission and awareness. In this paper, we show that, only by using the data collected from the embedded sensors in mobile devices instead of GPS data, we can infer a user’s location information with high accuracy. Three issues are addressed which are route identification, user localization in a specific route, and user localization in a bounded area. The Dynamic Time Warping based technique is designed and we develop a Hidden Markov Model to solve the localization problem. Real experiments are performed to evaluate our proposed methods.

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Gao, Guangyuan, and Mario Wüthrich. "Convolutional Neural Network Classification of Telematics Car Driving Data." Risks 7, no. 1 (January 10, 2019): 6. http://dx.doi.org/10.3390/risks7010006.

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The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks.

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Oliver, Melody, Hannah Badland, Suzanne Mavoa, Mitch J. Duncan, and Scott Duncan. "Combining GPS, GIS, and Accelerometry: Methodological Issues in the Assessment of Location and Intensity of Travel Behaviors." Journal of Physical Activity and Health 7, no. 1 (January 2010): 102–8. http://dx.doi.org/10.1123/jpah.7.1.102.

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Background:Global positioning systems (GPS), geographic information systems (GIS), and accelerometers are powerful tools to explain activity within a built environment, yet little integration of these tools has taken place. This study aimed to assess the feasibility of combining GPS, GIS, and accelerometry to understand transport-related physical activity (TPA) in adults.Methods:Forty adults wore an accelerometer and portable GPS unit over 7 consecutive days and completed a demographics questionnaire and 7-day travel log. Accelerometer and GPS data were extracted for commutes to/from workplace and integrated into a GIS database. GIS maps were generated to visually explore physical activity intensity, GPS speeds and routes traveled.Results:GPS, accelerometer, and survey data were collected for 37 participants. Loss of GPS data was substantial due to a range of methodological issues, such as low battery life, signal drop out, and participant noncompliance. Nonetheless, greater travel distances and significantly higher speeds were observed for motorized trips when compared with TPA.Conclusions:Pragmatic issues of using GPS monitoring to understand TPA behaviors and methodological recommendations for future research were identified. Although methodologically challenging, the combination of GPS monitoring, accelerometry and GIS technologies holds promise for understanding TPA within the built environment.

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Lin, Iuon-Chang, Chen-Yang Cheng, and Yen-Ting Lin. "Improved location filtering using a context-aware approach." Journal of Ambient Intelligence and Smart Environments 13, no. 1 (January 20, 2021): 55–72. http://dx.doi.org/10.3233/ais-200587.

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With the pervasiveness of GPS-enabled devices, a considerable number of GPS traces are accumulating continuously and unobtrusively in online communities. However, almost all current applications directly use raw GPS data, such as coordinates and time stamps, without interpreting these data. Thus far, online communities cannot offer much support to users in terms of recommending geospatial locations. Furthermore, because the data sets involved are large, users cannot browse each GPS trajectory individually. Therefore, users’ GPS trajectories must be mined and then classified as positive or negative. When the number of ratings for a place exceeds a certain threshold, the place is considered suitable for the user. By contrast, when the ratings for a place are mostly negative, this place is considered unsuitable for the user. When a user searches for the best place, the recommender system determines the user’s location (latitude, longitude) and then sends the best-rated destinations and the shortest routes between the user’s location and the destination to the user’s mobile device. Experiments were conducted in this study to determine the requisite similarity for GPS data points, the user’s information, and the best route for the user.

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Frair, Jacqueline L., John Fieberg, Mark Hebblewhite, Francesca Cagnacci, Nicholas J. DeCesare, and Luca Pedrotti. "Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1550 (July 27, 2010): 2187–200. http://dx.doi.org/10.1098/rstb.2010.0084.

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Global positioning system (GPS) technologies collect unprecedented volumes of animal location data, providing ever greater insight into animal behaviour. Despite a certain degree of inherent imprecision and bias in GPS locations, little synthesis regarding the predominant causes of these errors, their implications for ecological analysis or solutions exists. Terrestrial deployments report 37 per cent or less non-random data loss and location precision 30 m or less on average, with canopy closure having the predominant effect, and animal behaviour interacting with local habitat conditions to affect errors in unpredictable ways. Home-range estimates appear generally robust to contemporary levels of location imprecision and bias, whereas movement paths and inferences of habitat selection may readily become misleading. There is a critical need for greater understanding of the additive or compounding effects of location imprecision, fix-rate bias, and, in the case of resource selection, map error on ecological insights. Technological advances will help, but at present analysts have a suite of ad hoc statistical corrections and modelling approaches available—tools that vary greatly in analytical complexity and utility. The success of these solutions depends critically on understanding the error-inducing mechanisms, and the biggest gap in our current understanding involves species-specific behavioural effects on GPS performance.

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Ullah, Isaac I. T. "Integrating Older Survey Data into Modern Research Paradigms." Advances in Archaeological Practice 3, no. 4 (November 2015): 331–50. http://dx.doi.org/10.7183/2326-3768.3.4.331.

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AbstractThe data from older archaeological surveys are incredibly important resources, often containing our only information about sites that have been destroyed or that are now inaccessible. These surveys occurred before the advent of GPS technology, however, so their spatial accuracy is often uncertain. Many types of locational errors accumulate in such “legacy” datasets, so using them in modern GIS-based spatial analyses is frequently problematic. Many of the sources of error can be identified and quantified, however, and systematic and random errors (derived mainly from Cartesian, rounding, and human error) can largely be mitigated by scanning the original field maps, georectifying the maps to trusted imagery, and then digitizing sites directly. The remaining “mislocation” errors derive from difficulty identifying locations in the field. The original survey notes may contain clues about mislocation error, but it is impossible to mitigate these errors without re-recording site locations with more accurate survey instruments. Instead, I advocate the use of GIS-based models to estimate the influence of specific surveying practices on site location accuracy. These models can provide a standardized, quantifiable measure of mislocation error in a legacy dataset, which can help guide its use in modern GIS analyses that require accurate site locations.

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A.G., Prof Dongre, Ankita Kadale, Snehal Khavle, and Ajinkya Dalvi. "GPS based Location Services and Analysis of Transit Data using Data Mining." IJARCCE 6, no. 3 (March 30, 2017): 884–88. http://dx.doi.org/10.17148/ijarcce.2017.63206.

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Miller, John S., and Duane Karr. "Experimental Application of Global Positioning system To Locate Motor Vehicle Crashes: Impact on Time and Accuracy." Transportation Research Record: Journal of the Transportation Research Board 1625, no. 1 (January 1998): 41–49. http://dx.doi.org/10.3141/1625-06.

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Motor vehicle crash countermeasures often are selected after an extensive data analysis of the crash history of a roadway segment. The value of this analysis depends on the accuracy or precision with which the crash itself is located. yet this crash location only is as accurate as the estimate of the police officer. Global Positioning System (GPS) technology may have the potential to increase data accuracy and decrease the time spent to record crash locations. Over 10 months, 32 motor vehicle crash locations were determined by using both conventional methods and hand-held GPS receivers, and the timeliness and precision of the methods were compared. Local crash data analysts were asked how the improved precision affected their consideration of potential crash countermeasures with regard to five crashes selected from the sample. On average, measuring a crash location by using GPS receivers added up to 10 extra minutes, depending on the definition of the crash location, the technology employed, and how that technology was applied. The average difference between conventional methods of measuring the crash location and either GPS or a wheel ranged from 5 m (16 ft) to 39 m (130 ft), depending on how one defined the crash location. Although there are instances in which improved precision will affect the evaluation of crash countermeasures, survey respondents and the literature suggest that problems with conventional crash location methods often arise from human error, not a lack of precision inherent in the technology employed.

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Anwar, Nuril, Murein Miksa Mardhia, and Luthfi Ryanto. "Live Forensics on GPS inactive Smartphone." Mobile and Forensics 3, no. 1 (March 31, 2021): 32–44. http://dx.doi.org/10.12928/mf.v3i1.3847.

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Google is known to still track the user's location despite the GPS settings and location history in smartphone settings has been turned off by the user. This requires special handling to prove the location on smartphones with inactive GPS and view its Location History previously used by user. The research investigates if Google is still recording its user data location. Live Forensic requires data from the running system or volatile data which is usually found in Random Access Memory (RAM) or transit on the network. Investigations are carried out using a Google account with a method used by live forensics to obtain results from the location history. Smartphones have been checked manually through data backup through custom recovery that has been installed. When checking the backup filesystem, turned out that no location data is stored. Therefore, researchers conducted an analysis on the Google Account which was analyzed using a forensic tool to analyze cloud services to obtain location data results. The results of the analysis carried out obtained a similarity in location from 8-days investigations. Google can still find the location of smartphones with GPS disabled, but the location results are not accurate. Google can store user location data via cellular networks, Wi-Fi, and sensors to help estimate the user's location. The process of extracting the results from the google maps log using a Google account will be analyzed using the Elcomsoft Cloud eXplorer and Oxygen Forensic Cloud Extractor so that the log location results are still available by Google.

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Zhou, C., W. D. Xiao, and D. Q. Tang. "MINING CO-LOCATION PATTERNS FROM SPATIAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 85–90. http://dx.doi.org/10.5194/isprsannals-iii-2-85-2016.

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Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

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Zhou, C., W. D. Xiao, and D. Q. Tang. "MINING CO-LOCATION PATTERNS FROM SPATIAL DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-2 (June 2, 2016): 85–90. http://dx.doi.org/10.5194/isprs-annals-iii-2-85-2016.

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Due to the widespread application of geographic information systems (GIS) and GPS technology and the increasingly mature infrastructure for data collection, sharing, and integration, more and more research domains have gained access to high-quality geographic data and created new ways to incorporate spatial information and analysis in various studies. There is an urgent need for effective and efficient methods to extract unknown and unexpected information, e.g., co-location patterns, from spatial datasets of high dimensionality and complexity. A co-location pattern is defined as a subset of spatial items whose instances are often located together in spatial proximity. Current co-location mining algorithms are unable to quantify the spatial proximity of a co-location pattern. We propose a co-location pattern miner aiming to discover co-location patterns in a multidimensional spatial data by measuring the cohesion of a pattern. We present a model to measure the cohesion in an attempt to improve the efficiency of existing methods. The usefulness of our method is demonstrated by applying them on the publicly available spatial data of the city of Antwerp in Belgium. The experimental results show that our method is more efficient than existing methods.

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Sharma, Jayesh. "Live GPS Location Tracking using GPRS." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (July 15, 2021): 744–48. http://dx.doi.org/10.22214/ijraset.2021.36081.

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In this paper, a real time tracking system is put forward. In this we are going to design a system which is used for tracking and positioning by using (GPS) and (GSM). This design is based on embedded application, which will regularly monitor location and report the status. This tracking device which is used in real time vehicle location tracking is done using the Arduino Uno Atmega328P, SIM800A module and NEO 6M GPS module. For doing so, the Arduino Uno Atmega328P is combined serially to a GSM module and GPS module. The design make use of RS-232 protocol for serial communication between the modems and the microcontroller. A serial driver IC is used for transforming TTL voltage levels to RS-232 voltage levels. The GSM module is used to regularly send the position of the vehicle from distant place. The GPS module that makes use of satellite technology for its navigation system will regularly give information like longitude, latitude, speed, distance travelled etc. For this purpose, Amazon Cloud Services is used for location data handling. The MySQL database is used to reserve all the data of the GPS.

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Li, Fenghua, Xinyu Wang, Ben Niu, Hui Li, Chao Li, and Lihua Chen. "Exploiting location-related behaviors without the GPS data on smartphones." Information Sciences 527 (July 2020): 444–59. http://dx.doi.org/10.1016/j.ins.2019.05.052.

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Bastari, Avando, Eko Saputra, Sunarta Sunarta, and Cucuk Wahyudianto. "DESIGN SMART MINE EXERCISE POSITIONING USING GLOBAL POSITIONING SYSTEM (GPS)." JOURNAL ASRO 11, no. 2 (April 21, 2020): 41. http://dx.doi.org/10.37875/asro.v11i2.268.

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Marine mines are one of the supporters in securing the marine region of NKRI. In relation to marine mines, the Navy has mapped mines to the territorial waters. The importance of mine site mapping information aims to prevent landmines from being a feeding weapon for the NAVY's Alutsista. In realizing a mine-site mapping and location system that can provide geo-location information for mines using Arduino devices. Build your exercise Smart mine positioning using Global Positioning System (GPS) Neo-M6. A study in providing latitude and longitude location information is packaged using a GPS module that is connected to the Arduino processing device to obtain the location coordinates of the latitude and longitude mines. Test results of GPS-based location tracking devices are obtained by the results of location data in latitude and longitude formats. Smart Mine location identification, can be applied using GPS based on the results of the design and creation of the system that has been done. Location displacement speed calculations can be obtained by calculating the starting location with the updated location in a time span and converting to a unit of distance. Based on the results of the speed test can be obtained even if the device's location position is unchanged, this is due to the GPS data always changing due to the level of position accuracy obtained from satellites. The use of the Neo-M6 GPS can be applied to the latitude and longitude location identification systems acquired a relatively small error value with an average satellite number obtained by as much as 8 satellites. The use of the Neo-M6 GPS, to get GPS data from satellites is a long time, the problem of GPS gets satellite data due to the less supportive weather conditions. Keyword: GPS Neo-m6, Arduino Mega 2560, weather.

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Marchal, F., J. Hackney, and K. W. Axhausen. "Efficient Map Matching of Large Global Positioning System Data Sets." Transportation Research Record: Journal of the Transportation Research Board 1935, no. 1 (January 2005): 93–100. http://dx.doi.org/10.1177/0361198105193500111.

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Speed and location observations from Global Positioning System (GPS) loggers are quickly becoming an important source of data for travel behavior researchers. Postprocessing these data requires identifying the location of the GPS data points on a coded map of the transportation network. The output of the map-matching process is the identification of the routes that were actually taken. This paper presents an innovative map-matching algorithm that relies only on the GPS coordinates and the network topology. Examples are provided on a large data set for the Zürich area. The paper demonstrates the efficiency of the algorithm in regard to accuracy and computational speed.

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Maddison, Ralph, Yannan Jiang, Stephen Vander Hoorn, Daniel Exeter, Cliona Ni Mhurchu, and Enid Dorey. "Describing Patterns of Physical Activity in Adolescents Using Global Positioning Systems and Accelerometry." Pediatric Exercise Science 22, no. 3 (August 2010): 392–407. http://dx.doi.org/10.1123/pes.22.3.392.

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This study aimed to describe the location and intensity of free-living physical activity in New Zealand adolescents during weekdays and weekend days using Global Positioning Systems (GPS), accelerometry, and Geographical Information Systems (GIS). Participants (n = 79) aged 12–17 years (M = 14.5, SD 1.6) recruited from two large metropolitan high schools each wore a GPS watch and an accelerometer for four consecutive days. GPS and accelerometer data were integrated with GIS software to map the main locations of each participant’s episodes of moderate-vigorous physical activity. On average participants performed 74 (SD 36) minutes of moderate and 7.5 (SD 8) minutes of vigorous activity per day, which on weekdays was most likely to occur within a 1 km radius of their school or 150 meters of their home environment. On weekends physical activity patterns were more disparate and took place outside of the home environment. Example maps were generated to display the location of moderate to vigorous activity for weekdays and weekends.

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Baranski, Przemyslaw, Maciej Polanczyk, and Pawel Strumillo. "Fusion of Data from Inertial Sensors, Raster Maps and GPS for Estimation of Pedestrian Geographic Location in Urban Terrain." Metrology and Measurement Systems 18, no. 1 (January 1, 2011): 145–58. http://dx.doi.org/10.2478/v10178-011-0014-3.

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Fusion of Data from Inertial Sensors, Raster Maps and GPS for Estimation of Pedestrian Geographic Location in Urban TerrainAn electronic system and an algorithm for estimating pedestrian geographic location in urban terrain is reported in the paper. Different sources of kinematic and positioning data are acquired (i.e.: accelerometer, gyroscope, GPS receiver, raster maps of terrain) and jointly processed by a Monte-Carlo simulation algorithm based on the particle filtering scheme. These data are processed and fused to estimate the most probable geographical location of the user. A prototype system was designed, built and tested with a view to aiding blind pedestrians. It was shown in the conducted field trials that the method yields superior results to sole GPS readouts. Moreover, the estimated location of the user can be effectively sustained when GPS fixes are not available (e.g. tunnels).

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Alamleh, Hosam, and Ali Abdullah S. AlQahtani. "A weighting system to build physical layer measurements maps by crowdsourcing data from smartphones." IAES International Journal of Robotics and Automation (IJRA) 9, no. 3 (September 1, 2020): 211. http://dx.doi.org/10.11591/ijra.v9i3.pp211-219.

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<p>Mobile devices can sense different types of radio signals. For example, broadcast signals. These broadcasted signals allow the device to establish a connection to the access point broadcasting it. Moreover, mobile devices can record different physical layer measurements. These measurements are an indication of the service quality at the point they were collected. These measurements data can be aggregated to form physical layer measurement maps. These maps are useful for several applications such as location fixing, navigation, access control, and evaluating network coverage and performance. Crowdsourcing can be an efficient way to create such maps. However, users in a crowdsourcing application tend to have different devices with different capabilities, which might impact the overall accuracy of the generated maps. In this paper, we propose a method to build physical layer measurements maps by crowdsourcing physical layer measurements, GPS locations, from participating mobile devices. The proposed system gives different weights to each data point provided by the participating devices based on the data source’s trustworthiness. Our tests showed that the different models of mobile devices return GPS location with different location accuracies. Consequently, when building the physical layer measurements maps our algorithm assigns a higher weight to data points coming from devices with higher GPS location accuracy. This allows accommodating a wide range of mobile devices with different capabilities in crowdsourcing applications. An experiment and a simulation were performed to test the proposed method. The results showed improvement in crowdsourced map accuracy when the proposed method is implemented.</p>

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Basukesti, Agus, and Bangga Dirgantara Adiputra. "FILTER ADAPTIF GPS PADA SISTEM DOWNLINK DATA." Angkasa: Jurnal Ilmiah Bidang Teknologi 8, no. 2 (August 25, 2017): 121. http://dx.doi.org/10.28989/angkasa.v8i2.125.

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GPS (Global Positioning System) is a system used for navigation and positioning with the help of 32 satellites that orbit the earth. GPS satellites emit radio waves to activate the GPS receiver on the Earth to determine the exact location, speed and time for all weather conditions. Currently, GPS has a vital role in navigation drone. However GPS have a lot of noise, it is necessary to overcome the noise filter on the GPS. In this research conducted a simulation of adaptive algorithm in extracting data from the GPS sensor. The method in this research is the Kalman Filter. From the identification and simulation can be concluded that the adaptive algorithm is designed to work well and need to be implemented on the downlink system. The simulation results showed that the error of algorithm has convergent properties are increasingly shrinking.

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Maekawa, Takuya, Naomi Yamashita, and Yasushi Sakurai. "How Well Can a User’s Location Privacy Preferences be Determined Without Using GPS Location Data?" IEEE Transactions on Emerging Topics in Computing 5, no. 4 (October 2017): 526–39. http://dx.doi.org/10.1109/tetc.2014.2335537.

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Khan, Sarosh, Pawan Maini, and Kittichai Thanasupsin. "Car-Following and Collision Constraint Models for Uninterrupted Traffic: Reexamination Using High-Precision Global Positioning System Data." Transportation Research Record: Journal of the Transportation Research Board 1710, no. 1 (January 2000): 37–46. http://dx.doi.org/10.3141/1710-05.

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In the last few decades several car-following models have been proposed and tested using mainly vehicle location data. The use of high-precision Global Positioning System (GPS) data to test several car-following and collision constraint models is reported, with a critical evaluation of these models and proposal of a modified collision constraint formulation. GPS receivers typically report time-stamped location or position fixes and velocity. For a pair of leading and following vehicles, location and velocity data were used to examine estimates of acceleration, velocity, and headway by Pipes’s; modified Pitt’s, or FRESIM; CARSIM; and INTELSIM car-following models. The important aspects of collecting accurate GPS data are also highlighted.

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Saleem jan, Mehrunnisa, Salman Ahmed, and Safdar Nawaz Khan Marwat. "A New Strategy to Enhance the Security of GPS Location by PGP Algorithm in Smart Containers." Sir Syed University Research Journal of Engineering & Technology 12, no. 1 (June 30, 2022): 86–91. http://dx.doi.org/10.33317/ssurj.458.

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Dynamic navigation devices like Global Positioning Systems (GPSs) are deployed for various purposes in different areas and these devices are usually the central point of interest of various groups like hackers to exploit the data sent and received by GPS systems. The GPS data is usually manipulated using spoofing attacks. This paper proposes a robust solution to the spoofing attacks carried out to manipulate, control and modify the location sharing of smart containers. The primary focus of this paper is securing the GPS information shared by the smart containers. The location shared by the smart containers is secured by encrypting it with Pretty Good Privacy (PGP) algorithm to avoid spoofing attacks in particular. The encrypted GPS location is sent across any communication channel. The receiver side will decrypt the encrypted GPS location at the receiving end. Hence, using this method of PGP encryption will ensure the safe and secure sharing of GPS location by the smart containers. As a result, GPS security has been improved by 80%.

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YADAV, J. K. S., R. K. GIRI, and L. R. MEENA. "Error handling in GPS data processing." MAUSAM 62, no. 1 (December 14, 2021): 97–102. http://dx.doi.org/10.54302/mausam.v62i1.212.

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We are aware that the processing of GPS data through GAMIT processing software is not free from errors. Some of them are generated due to different modules involved in processing. The data quality depends so many factors, like quality of met-instrument, which supplies the meteorological data, algorithm of processing which based on the network homogeneity or heterogeneity and location of the site, whether it is free from multi-path etc. The root mean square errors for New Delhi, Mumbai, Kolkata, Guwahati and Chennai GPS stations are spatially correlated and observations are weighted according to the satellite elevation angle. Diurnal variability of Integrated Precipitable Water Vapour (IPWV) has been shown its range from 45 mm to 65 mm for New Delhi during the monsoon season, 2008.

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Chen, Su Ping, and Dai Zong Liu. "Bus Passenger Origin-Destination Matrix Estimation Using Available Information from Automatic Data Collection Systems in Chongqing, China." Advanced Materials Research 779-780 (September 2013): 878–89. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.878.

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This research explores the application of available information from Automated Data Collection Systems (ADC) with a focus on bus passenger Origin-Destination (OD) matrices inference, using Chongqing as a case study. It demonstrates the feasibility and ease of applying to infer bus passengers boarding and alighting locations if boarding information is available from neither Automatic Fare Collection System (AFC) nor GPS data, and is the first known attempt to create an algorism to use stop Geographic Information System (GIS) and dual-direction bus stop group to estimate the boarding location and traveling direction when GPS data cannot provide the arriving information at bus stops. The results of this research have been practically applied to a full-size bus network in Chongqing, and can be easily implemented to other Chinese cities as Chongqing presents most popular data structure of ADC in China.

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Roihan, Ahmad, Muhammad Sri Bintang Prasetyo, and Annas Rifa'i. "MONITORING LOCATION TRACKER UNTUK KENDARAAN BERBASIS RASPBERY Pi." Journal CERITA 3, no. 2 (August 1, 2017): 148–61. http://dx.doi.org/10.33050/cerita.v3i2.652.

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Learning from the development of new technologies using the Internet as a monitoring medium, it can be ascertained that gps can be applied in various fields. These gps systems offer efficient improvements in vehicle monitoring and obtaining accurate and real time data. That with thedevelopment of new technologies that can be done that was the work-manual work using a computer that is Raspberry Pi. Data collection method is done by observation method, interview method and literature study, tool design method is done by using Fritzing application as a tool to design the system to be made and design of prototype to be built. But so far some are still local (Intranet), such as Infrared, Bluetooth and Wireless LAN. With the latest technology changing the local paradigm (Intranet) into IoT / Internet Protocol Based. In designing this tool required Raspberry Pi as Gps media as well as vehicle monitoring media using GPS, equipped with GPS module as a monitoring medium with ease. The creation of this tool may make it easy for vehicle owners to monitor vehicles remotely and provide reports in realtime.

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Moad, Dominica, Alison Fielding, Amanda Tapley, Mieke L. van Driel, Elizabeth G. Holliday, Jean I. Ball, Andrew R. Davey, et al. "Socioeconomic disadvantage and the practice location of recently Fellowed Australian GPs: a cross-sectional analysis." Australian Journal of Primary Health 28, no. 2 (February 23, 2022): 104–9. http://dx.doi.org/10.1071/py21179.

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Background: Socioeconomic disadvantage and the ‘inverse care law’ have significant effects on the health and well-being of Australians. Early career GPs can help address the needs of socioeconomically disadvantaged communities by choosing to practice in these locations. This study addressed an evidence gap around GPs post-Fellowship (within 2 years) practice location, and whether practice location is related to postgraduate vocational training. Methods: This was a cross-sectional questionnaire-based study of recently Fellowed GPs from New South Wales, the Australian Capital Territory, Victoria and Tasmania. Questionnaire items elicited information about participants’ current practice, including location. Where consent was provided, participants’ questionnaire responses were linked to previously collected vocational GP training data. The outcome factor in analyses was practice location socioeconomic status (SES): the four deciles of greater socioeconomic disadvantage versus locations with a higher SES. SES was classified according to the Socio-Economic Indexes for Areas – Index of Relative Socioeconomic Disadvantage. Multivariable logistic regression was undertaken. Results: Of participants currently working in clinical general practice, 26% were practicing in the four deciles of greater socioeconomic disadvantage. Significant multivariable associations of working in these locations included having trained in a practice located in an area of greater socioeconomic disadvantage (odds ratio (OR) 3.14), and having worked at their current practice during vocational training (OR 2.99). Conclusion: Given the association of training and practice location for recently Fellowed GPs, policies focused on training location may help in addressing ongoing workforce issues faced by areas of higher socioeconomic disadvantage.

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Ahn, Junho, Akshay Mysore, Kati Zybko, Caroline Krumm, Sravan Thokala, Xinyu Xing, Ming Lian, Richard Han, Shivakant Mishra, and Thompson Hobbs. "WildSense: Monitoring Interactions among Wild Deer in Harsh Outdoor Environments Using a Delay-Tolerant WSN." Journal of Sensors 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/1693460.

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Biologists and ecologists often monitor the spread of disease among deer in the wild by using tracking systems that record their movement patterns, locations, and interaction behavior. The existing commercial systems for monitoring wild deer utilize collars with GPS sensors, deployed on captured and rereleased deer. The GPS sensors record location data every few hours, enabling researchers to approximate the interaction behavior of tracked deer with their GPS locations. However, the coarse granularity of periodically recorded GPS location data provides only limited precision for determining deer interaction behavior. We have designed a novel system to monitor wild deer interaction behavior more precisely in harsh wilderness environments. Our system combines the functionalities of both GPS and RF-radio sensors with low-cost and minimal-resource motes. We designed and built our system to be able to operate robustly for a period of up to several months for continual tracking and monitoring of the locations and interaction behaviors of wild deer in harsh environments. We successfully deployed six deer collars on six wild deer that were captured and rereleased in the Soapstone Prairie Natural Area of northern Colorado over a one-month period. In this paper, we describe how we designed and built this system and evaluate its successful operation in a wilderness area.

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Koswatte, S., K. Mcdougall, and X. Liu. "SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 543–47. http://dx.doi.org/10.5194/isprs-archives-xli-b2-543-2016.

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Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.

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Koswatte, S., K. Mcdougall, and X. Liu. "SEMANTIC LOCATION EXTRACTION FROM CROWDSOURCED DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 8, 2016): 543–47. http://dx.doi.org/10.5194/isprsarchives-xli-b2-543-2016.

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Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction.

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Min Seon Park, Hyung Rim Choi, Byung Kwon Park, and Young Jae Park. "A Global Location Tracking System using GPS data for Container Logistics." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 8 (April 30, 2013): 1272–81. http://dx.doi.org/10.4156/aiss.vol5.issue8.149.

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Fan, Lingling, Liang Tang, and Shaokuan Chen. "Optimizing location of variable message signs using GPS probe vehicle data." PLOS ONE 13, no. 7 (July 6, 2018): e0199831. http://dx.doi.org/10.1371/journal.pone.0199831.

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Hamdi, Khairil, Budi Sunaryo, Arianto Arianto, Yuhefizar, and Imam Gunawan. "Sistem Pelacakan Lokasi Petugas Survei RTLH Menggunakan GPS Android dan WebGIS." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 3, no. 3 (December 16, 2019): 552–59. http://dx.doi.org/10.29207/resti.v3i3.1355.

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The feasibility of a home for family residence is the basis for growing and developing a better family life. The roof, floor, and walls are data on the indicator of the feasibility of the house at the time of the survey. The need for a system that can display RTLH locations in the Web Geographic Information System (WebGIS) accompanied by detailed RTLH data and photos. This study explains how the system tracks the location of RTLH survey data officers using Android GPS, then represents it on WebGIS. The tracking method starts when the clerk fills out the home data input form via the Android application. Location coordinates in the form of latitude and longitude will be detected automatically and become input parameters accompanied by photos and RTLH data details. Offline data will be stored in SQLite then sent to MySQL via database synchronization while online. The purpose of this research is to establish a location tracking system for RTLH survey officers to facilitate the validation process of data suitability between data and facts in the field. This system managed to track the location of RTLH survey officers by being marked by the appearance of markers on the Google Map on the WebGIS application. From a total of 14,949 houses in Kota Solok in December 2018, there were 938 RTLH. From 938 RTLH, the coordinates of the location of latitude and longitude are 100%.

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Soundy, Andy W. R., Bradley J. Panckhurst, Phillip Brown, Andrew Martin, Timothy C. A. Molteno, and Daniel Schumayer. "Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data." Sensors 20, no. 21 (October 24, 2020): 6050. http://dx.doi.org/10.3390/s20216050.

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We recorded the time series of location data from stationary, single-frequency (L1) GPS positioning systems at a variety of geographic locations. The empirical autocorrelation function of these data shows significant temporal correlations. The Gaussian white noise model, widely used in sensor-fusion algorithms, does not account for the observed autocorrelations and has an artificially large variance. Noise-model analysis—using Akaike’s Information Criterion—favours alternative models, such as an Ornstein–Uhlenbeck or an autoregressive process. We suggest that incorporating a suitable enhanced noise model into applications (e.g., Kalman Filters) that rely on GPS position estimates will improve performance. This provides an alternative to explicitly modelling possible sources of correlation (e.g., multipath, shadowing, or other second-order physical phenomena).

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Lee, Eun Soo, Woo Sang Lee, Kwang Man Lim, and Myung Seok Bang. "The Measurement of Railway Using VRS GPS." Applied Mechanics and Materials 446-447 (November 2013): 1118–22. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.1118.

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This study is based on those data gathered on a 450m-long railway with a VRS GPS. In addition, certain location data on a railway were gathered through three 'Continuous VRS GPS' measurements and one 'Stop & Go VRS GPS' measurements while a railway-maintenance vehicle with GPS receivers mounted on its left & right sides was moving, Based on these location data, an average inter-rail space was analyzed. As a result of the analysis, the inter-rail space ranged from 1480mm to 1530mm. Based on the standard design dimension of an inter-rail space equal to 1,500mm and the average VRS GPS accuracy of ±3cm permissible in South Korea, the range was acceptable. Furthermore, if the center of a GPS receiver is placed over the vertical line of a rail center, more accurate location data on the railway could have been gathered.

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Tomkiewicz, Stanley M., Mark R. Fuller, John G. Kie, and Kirk K. Bates. "Global positioning system and associated technologies in animal behaviour and ecological research." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1550 (July 27, 2010): 2163–76. http://dx.doi.org/10.1098/rstb.2010.0090.

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Biologists can equip animals with global positioning system (GPS) technology to obtain accurate (less than or equal to 30 m) locations that can be combined with sensor data to study animal behaviour and ecology. We provide the background of GPS techniques that have been used to gather data for wildlife studies. We review how GPS has been integrated into functional systems with data storage, data transfer, power supplies, packaging and sensor technologies to collect temperature, activity, proximity and mortality data from terrestrial species and birds. GPS ‘rapid fixing’ technologies combined with sensors provide location, dive frequency and duration profiles, and underwater acoustic information for the study of marine species. We examine how these rapid fixing technologies may be applied to terrestrial and avian applications. We discuss positional data quality and the capability for high-frequency sampling associated with GPS locations. We present alternatives for storing and retrieving data by using dataloggers (biologging), radio-frequency download systems (e.g. very high frequency, spread spectrum), integration of GPS with other satellite systems (e.g. Argos, Globalstar) and potential new data recovery technologies (e.g. network nodes). GPS is one component among many rapidly evolving technologies. Therefore, we recommend that users and suppliers interact to ensure the availability of appropriate equipment to meet animal research objectives.

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Chen, J., T. Hu, P. Zhang, W. Shi, and J. Shan. "Trajectory Clustering for People's Movement Pattern Based on Crowd Souring Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2 (November 11, 2014): 55–62. http://dx.doi.org/10.5194/isprsarchives-xl-2-55-2014.

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With the increasing availability of GPS-enabled devices, a huge amount of GPS trajectories recording people's location traces have been accumulated and shared freely on the Web. In this area, one of the most important research topics is to exploit trajectory-movement pattern about where and when people clustered based on the raw GPS data. In order to solve this problem, clustering is a good way to perform data mining tasks on trajectory data. <br><br> This paper provides a clustering algorithm which aims at mining people’s movement pattern about the clustered location and their temporal evolution characteristics. Firstly, the characteristic points of GPS trajectories were chosen. Based on the characteristic points, a trajectory has been partitioned into a group of line segments. These line segments can represent the movement pattern of trajectories much better than that of track points. Secondly, an improved density-based line clustering method was used for the individual partitioned line segments to find out individual clusters with similar track segments. In this step, the absolute time spot of people’s trajectories was taking into account as a characteristic for the temporal evolution of people’s trajectories. Finally, the representative clustered hot spots of multiple users’ line segments achieved by above steps were output. Experiments were conducted with GPS trajectories data downloaded from the web to verify the effectiveness of the algorithm in this paper. According to the results, the spatial distribution and temporal evolution characteristics of people’s stay hot spots were effectively discovered from people’s GPS trajectories data.

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Remias, Stephen M., Christopher M. Day, Jonathan M. Waddell, Jenna N. Kirsch, and Ted Trepanier. "Evaluating the Performance of Coordinated Signal Timing: Comparison of Common Data Types with Automated Vehicle Location Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 18 (September 10, 2018): 128–42. http://dx.doi.org/10.1177/0361198118794546.

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Performance measures are essential for managing transportation systems, including signalized corridors. Coordination is an essential element of signal timing, enabling reliable progression of traffic along corridors. Improved progression leads to less user delay, which leads to user cost savings and lower vehicle emissions. This paper presents a comparative study of signal coordination assessment using four different technologies. These technologies include detector-based high-resolution controller data, Bluetooth/Wi-Fi sensors, segment-based probe vehicle data, and automated vehicle location data consisting of GPS-based vehicle trajectories, representing the data anticipated from emerging connected vehicle technologies. The data were compiled for a 4.2-mi corridor in Holland, Michigan. The results show that all of the data sources were able to identify, at some level, where coordination issues existed. Detector-based controller data and GPS-based vehicle trajectory data were capable of showing greater detail, and could be used to make offset adjustments. The paper concludes by demonstrating the identification of signal coordination issues with the use of visual performance metrics incorporating automated vehicle location (AVL) trajectory data.

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Fenando, Fenando. "Sistem Informasi Geografis (SIG) Pemetaan Lokasi Pertambangan Batu Bara Berbasis Quantum GIS (Studi Kasus: PT. Hasil Bumi Kalimantan)." Journal of Information Systems and Informatics 3, no. 1 (March 2, 2021): 108–20. http://dx.doi.org/10.33557/journalisi.v3i1.94.

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During this time, the determination of the mine coordinates location done by the fieldman of PT. Kalimantan Earth Results was using a GPS locator. Found 27 points of coal mines and 18 coal ports spread across the southern Sumatra region. At present the process of recording and mapping locations is still done conventionally, causing frequent data loss, inaccuracies in the location of mining and information about mining results are less up-to-date. Systematic determination of location points is needed to make the companies and customers easier to see the coal mine profiles, coal quality, find out the location of each mine, and up-to-date mining results reports via webgis The prototype model was chosen as a method in system development. As well as UML (Unified Modeling Language), Quantum GIS (Qgis) and php will be used for the designers, builders of the webgis. This research is expected to produce a geographic information system (GIS) mapping of coal mining location points.

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A G, Rashmi. "Insider Attack Detection in IoT devices using Data Analytics." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 870–74. http://dx.doi.org/10.22214/ijraset.2021.36520.

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In the recent years, the rate of theft of money being carried to ATM machines is increasing day by day. Each vehicle carrying money should be monitored at all times through communication protocol and the vehicle should have a GPS installed. This location information should be very confidential and accessible only to the authorized officials. Due to the advancement in the technology, there are numerous ways in which the attacks are happening. One such attack is accessing the confidential information (i.e., the GPS location of the vehicle in this case) by unauthorized means from the people within the same network and using it for various purposes. It's become a challenge to overcome these attacks and deposit the amount to ATM machines safely. There are other scenarios viz. carrying the witnesses to the court, shipping important materials like medicines or official documents where the GPS information is being misused. Providing security against insider attacks is the need of the hour. This paper mainly focuses on the development of an end-to-end system which detects the unauthorized access to the confidential information and gives analysis of the time and frequency of attack using data analytics.

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Li, Wenjun, Linda Churchill, Jie Cheng, Rachel Siden, Annabella Aguirre, and Kevin Kane. "MEASURING SPACE AND TIME USE, PHYSICAL AND SOCIAL ACTIVITIES AMONG NON-ENGLISH SPEAKING LATE-LIFE ASIAN IMMIGRANTS." Innovation in Aging 3, Supplement_1 (November 2019): S196—S197. http://dx.doi.org/10.1093/geroni/igz038.709.

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Abstract Little is known about the health and health care needs of non-English Speaking late-life Asian immigrants. Due to language barriers and memory issues, self-report data are unreliable for investigating activity patterns in this population. In the ongoing NIA-funded Healthy Aging and Neighborhood Study, we developed a novel method to objectively measure space and time use, location- and time-specific physical and social activities using accelerometer (ACC) and Global Positioning System (GPS) devices. The study has recruited over 150 Caucasians and 150 minorities including 50 non-English speaking late-life Asian immigrants. The participants answered surveys in their preferred language (English, Spanish, traditional or simplified Chinese) and wore ACC/GPS devices for 7 to 10 full days. Activity levels and geographic locations are recorded every 30 seconds. Using the combined ACC/GPS data, time- and location-specific activity amounts, time use and mobility patterns are objectively measured. Baseline findings will be reported at the GSA conference.

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Vazirabadkar, Ketan, Siddhant Gadre, Rajeev Sebastian, and Nikhil Dwivedi. "Crowdsourcing Based on GPS." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 (September 23, 2015): 42–43. http://dx.doi.org/10.1609/hcomp.v3i1.13245.

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Machines cannot answer all the queries. There are some queries which need human input for providing information that is missing from the database, to perform computationally difficult procedures, and for cross checking, ordering, or aggregating results based on fuzzy criteria. Crowd sourcing can be used to solve such queries. Smartphones can unfold the full potential of crowdsourcing, allowing users to transparently contribute to complex and novel problem solving. We present the intrinsic characteristics of smartphones, a taxonomy that gives classification of the emerging field of mobile crowdsourcing and application that optimize location-based queries and similarity services over data generated by a crowd. This paper will focus on our application which uses Internet as a source of getting information which is so relevant to us that it is about what we want to do right where we are right now in the best possible way. We have implemented two ideas over the course of the project — first, a Location-Based crowd sourced forum and secondly, a platform where one can get location based relevant infor-mation. The forum will enable a user to ask relevant and location based question which will be answered by the people who are familiar with that area and the platform will monitor user’s location at regular intervals and pop up relevant information as soon as s/he enters a particular radius of an area of his/her preferred choices.

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Mann, Supreet Kaur, and Sonal Chawla. "Cluster-Based Cab Recommender System (CBCRS) for Solo Cab Drivers." International Journal of Information Retrieval Research 12, no. 1 (January 1, 2022): 1–15. http://dx.doi.org/10.4018/ijirr.314604.

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An efficient cluster-based cab recommender system (CBCRS) provides solo cab drivers with recommendations about the next pickup location having high passenger finding potential at the shortest distance. To recommend the cab drivers with the next passenger location, it becomes imperative to cluster the global positioning system (GPS) coordinates of various pick-up locations of the geographic region as that of the cab. Clustering is the unsupervised data science that groups similar objects into a cluster. Therefore, the objectives of the research paper are fourfold: Firstly, the research paper identifies various clustering techniques to cluster GPS coordinates. Secondly, to design and develop an efficient algorithm to cluster GPS coordinates for CBCRS. Thirdly, the research paper evaluates the proposed algorithm using standard datasets over silhouette coefficient and Calinski-Harabasz index. Finally, the paper concludes and analyses the results of the proposed algorithm to find out the most optimal clustering technique for clustering GPS coordinates assisting cab recommender system.

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Lin, C. Y., T. Matsuo, J. Y. Liu, C. H. Lin, H. F. Tsai, and E. A. Araujo-Pradere. "Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance." Atmospheric Measurement Techniques 8, no. 1 (January 12, 2015): 171–82. http://dx.doi.org/10.5194/amt-8-171-2015.

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Abstract. Ionospheric data assimilation is a powerful approach to reconstruct the 3-D distribution of the ionospheric electron density from various types of observations. We present a data assimilation model for the ionosphere, based on the Gauss–Markov Kalman filter with the International Reference Ionosphere (IRI) as the background model, to assimilate two different types of slant total electron content (TEC) observations from ground-based GPS and space-based FORMOSAT-3/COSMIC (F3/C) radio occultation. Covariance models for the background model error and observational error play important roles in data assimilation. The objective of this study is to investigate impacts of stationary (location-independent) and non-stationary (location-dependent) classes of the background model error covariance on the quality of assimilation analyses. Location-dependent correlations are modeled using empirical orthogonal functions computed from an ensemble of the IRI outputs, while location-independent correlations are modeled using a Gaussian function. Observing system simulation experiments suggest that assimilation of slant TEC data facilitated by the location-dependent background model error covariance yields considerably higher quality assimilation analyses. Results from assimilation of real ground-based GPS and F3/C radio occultation observations over the continental United States are presented as TEC and electron density profiles. Validation with the Millstone Hill incoherent scatter radar data and comparison with the Abel inversion results are also presented. Our new ionospheric data assimilation model that employs the location-dependent background model error covariance outperforms the earlier assimilation model with the location-independent background model error covariance, and can reconstruct the 3-D ionospheric electron density distribution satisfactorily from both ground- and space-based GPS observations.

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Journal articles on the topic 'GPS location data'

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