Relevant bibliographies by topics / Geolocation data analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Geolocation data analysis'

Author: Grafiati
Published: 5 June 2025
Last updated: 11 July 2025

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Journal articles on the topic "Geolocation data analysis"

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Smt., Jayanti K., Pare Ravi, S. P. Saurabh, and S. H. Shashank. "Exploratory Analysis Of Geo-Location Data." Journal Of Scientific Research And Technology (JSRT) 1, no. 2 (2023): 60–67. https://doi.org/10.5281/zenodo.8034007.

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Geography and regional human behavior may be more fully comprehended via the study of geo-locational data. A wealth of conveniences that make life easier in today's fast-paced, high-effort world. Many fields now rely heavily on geolocation and geographic information systems (GIS). Simply said, they may show geographical information and connect databases. This evaluates the effectiveness of an accommodation search in each given area as a way to demonstrate the value of geolocation. In this project, we apply K-Means Clustering to the geo-locational data we gathered from the Foursquare API (Application Programming Interface) URL (Uniform Resource Locator) in order to classify accommodations and determine which ones are best suited to a given set of coordinates. In this work, we use feature selection to identify location indicator words (LIWs) and test whether a smaller feature set improves geolocation precision.
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Marathe, Rushabh, and Adarsh Jadhav. "Exploratory Analysis of Geolocational Data." International Journal of Research Publication and Reviews 04, no. 02 (2023): 728–31. http://dx.doi.org/10.55248/gengpi.2023.42005.

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This project uses the K-Means clustering method to find the best migrant shelters by ranking migrant shelters according to their amenities, budget and proximity preferences. Fetch, cleans, analyse and aggregates K-Means on geolocation data to recommend immigrant accommodation in the city.
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Li, Facheng, and Qiming Zeng. "High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction." Remote Sensing 16, no. 22 (2024): 4210. http://dx.doi.org/10.3390/rs16224210.

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High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have a theoretical geolocation accuracy better than 1 m, but this may be unrealizable with less accurate external data, such as atmospheric parameters and ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a geolocation procedure, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 m for TerraSAR-X and 2 m/4 m for the Tianhui-2 reference/secondary satellites. Based on the WorldDEM and the SRTM, additional geolocation errors of 2 m and 4 m are introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that tropospheric errors and ground elevation errors are the primary factors influencing geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite.
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Sreelatha, Gavini, Veeresh Dachepalli, and Gudur Sahiti. "DATA ANALYSIS FOR STUDENTS BASED ON GEOLOCATION APPROACH." International Journal of Interpreting Enigma Engineers 01, no. 03 (2024): 33–41. http://dx.doi.org/10.62674/ijiee.2024.v1i03.005.

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It is quite important for students, particularly those who are new to strange places, to find appropriate housing. Some viable solutions to this difficulty can be found in advanced analytical approaches such as geolocational analysis and clustering algorithms. The optimal student housing options in any city can be found by thoroughly exploring geolocational data, as demonstrated in this study. In order to organise housing possibilities according to criteria such as budget, proximity to amenities, and availability, we employ the popular K-Means Clustering technique. Our objective is to provide new students with tailored recommendations by grouping homes into clusters; this will simplify their search and increase their level of satisfaction. This project seeks to demonstrate how geolocational analysis and clustering can improve student housing selection, which in turn improves students' health and academic performance.
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Kinakh, V., T. Oda, R. Bun, and O. Novitska. "Mitigating geolocation errors in nighttime light satellite data and global CO2 emission gridded data." Mathematical Modeling and Computing 8, no. 2 (2021): 304–16. http://dx.doi.org/10.23939/mmc2021.02.304.

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Accurate geospatial modeling of greenhouse gas (GHG) emissions is an essential part of the future of global GHG monitoring systems. Our previous work found a systematic displacement in the high-resolution carbon dioxide (CO2) emission raster data of the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission product. It turns out this displacement is due to geolocation bias in the Defense Meteorological Satellite Program (DMSP) nighttime lights (NTL) data products, which are used as a spatial emission proxy for estimating non-point source emissions distributions in ODIAC. Mitigating such geolocation error (~1.7 km), which is on the same order of the size of the carbon observing satellites field of view, is especially critical for the spatial analysis of emissions from cities. In this paper, there is proposed a method to mitigate the geolocation bias in DMSP NTL data that can be applied to DMSP NTL-based geospatial products, such as ODIAC. To identify and characterize the geolocation bias, we used the OpenStreetMap repository to define city boundaries for a large number of global cities. Assumption is that the total emissions within the city boundaries are at the maximum if there is no displacement (geolocation bias) in NTL data. Therefore, it is necessary to find an optimal vector (distance and angle) that maximizes the ODIAC total emissions within cities by shifting the emission fields. In the process of preparing annual composites of the nighttime stable lights data, some pixels of the DMSP data corresponding to water bodies were zeroed, which due to the geolocation bias unreasonably distorted the ODIAC emission fields. Hence, an original approach for restoring data in such pixels is considered using elimination of the factor that distorted the ODIAC emission fields. It is also proposed a bias correction method for shifted high-resolution emission fields in ODIAC. The bias correction was applied to multiple cities from the different continents. It is shown that the bias correction to the emission data (elimination of geolocation error in non-point emission source fields) increases the total CO2 emissions within city boundaries by 4.76% on average, due to reduced emissions from non-urban areas to which these emissions were likely to be erroneously attributed.
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Jayusta, Evan, Marhalim Marhalim, Muhammad Immanullah, and Yuza Reswan. "Robustness Analysis of QR – Code Based and Geolocation Based Attendance System." JURNAL MEDIA INFOTAMA 20, no. 2 (2024): 517–24. https://doi.org/10.37676/jmi.v20i2.6510.

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The use of QR-code and GPS technology in the attendance system also has several advantages, such as making the attendance process easier for students, monitoring employee attendance accurately and efficiently, and improving the quality of student work. Therefore, it is important to compare the efficiency of the QR-code attendance method with Geolocation to find out which technology is more effective and efficient in improving the attendance system. In this research the author used the K-means Clustering method. The K-means method is a non-hierarchical data grouping method that attempts to partition existing data into two or more 13 groups. This method partitions data into groups so that data with the same characteristics is included in other groups. The research results show that Cluster 1 has a centeroid that is close to the QR Code features (3,4,4), namely moderate efficiency in application implementation, relatively high ease of use, and system robustness with high data accuracy. Cluster 2 has a centeroid with Geolocation features (3,3,3), namely moderate efficiency and flexibility, moderate ease of use, and system robustness with moderate data accuracy. Thus, after obtaining comparison results between QR code and Geolocation in the lecture attendance process, researchers can recommend the best system to use in terms of user aspects and needs. If the user needs a presence system that prioritizes ease of use and robustness of the system, the user is suited to using the QR-Code system because the usability and durability aspects are relatively high. Meanwhile, if the user prioritizes efficiency and flexibility in the process, it is best to use a presence system in the form of Geolocation, because the results of this research show that the efficient aspect of Geolocation is higher
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Carmody, Kathryn G., Arthur J. Mariano, and David William Kerstetter. "A Principal Component Analysis of Vertical Temperature Profiles for Tracking Movements of Large Pelagic Fishes." Aquatic Science and Technology 5, no. 1 (2017): 33. http://dx.doi.org/10.5296/ast.v5i1.10649.

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Pop-up satellite archival tag (PSAT) technology that records depth, temperature, and light-level data has expanded the understanding of free-swimming behavior for numerous pelagic animals. Astronomical algorithms using these light-level data have allowed geolocation estimates of daily longitude and latitude. However, many pelagic animals have a crepuscular behavior pattern in which individuals are at depths below the photic layer during the day, thus precluding the use of traditional light-based movement algorithms for geolocation in such species as swordfish. A principal component analysis (PCA) of temperature profiles is described herein that utilizes depth and temperature data rather than light to estimate the horizontal movement between the initial location of tag release and transmission. PSAT data from swordfish (n=4), blue marlin (n=14), white marlin (n=2), and black marlin (n=1) were used to generate daily coordinate estimates. The marlin data provided sufficient light information to derive geolocation estimates using two light-based state space models, while the hydrographic PCA model was used to derive comparison estimates. Comparisons of the two model types show an average root mean square difference of 175.4 km demonstrating that the PCA model can be used to extract the movement of tagged swordfish and other pelagic species demonstrating crepuscular behavior. Integration of this PCA-based geolocation methods with both the best available estimates of the ocean temperature at the time of tag deployment and the existing light-based geolocation models would provide additional information on fine-scale movement of tagged fish.
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Zhou, Muzhi, Zheng Wei, and Junxiang Liao. "How Can the Universal Disclosure of Provincial-level IP Geolocation Change the Landscape of Social Media Analysis." ACM SIGWEB Newsletter 2024, Autumn (2024): 1–9. https://doi.org/10.1145/3704991.3704994.

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Since April 2022, the Chinese government has mandated the disclosure of domestic users' provincial- level geolocation information on social media platforms in China. This has led to unprecedented, universal accessibility to geolocation information that is automatically tagged to various forms of online content, including short video posts, user comments, and any other forms of user-generated online content. In this paper, we present suggestions and examples that illustrate how this newly available information can potentially change the landscape of social media analysis. First, the universal geolocation disclosure substantially lowers the small sample size and sample selection issues due to voluntary geolocation tagging, thereby ensuring a more comprehensive and general- izable approach to social media analysis. Second, this geolocation data advances the exploration of the relationships between online content and regional characteristics. Researchers can now uncover comparable and less biased regional variations re ected by online content, shedding light on the regional-specific socio-cultural dynamics. This enhanced accuracy of social media analysis provides a new avenue for regional and comparative studies. We also discuss some limitations.
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Takenaka, Hideaki, Taiyou Sakashita, Atsushi Higuchi, and Teruyuki Nakajima. "Geolocation Correction for Geostationary Satellite Observations by a Phase-Only Correlation Method Using a Visible Channel." Remote Sensing 12, no. 15 (2020): 2472. http://dx.doi.org/10.3390/rs12152472.

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This study describes a high-speed correction method for geolocation information of geostationary satellite data for accurate physical analysis. Geostationary satellite observations with high temporal resolution provide instantaneous analysis and prompt reports. We have previously reported the quasi real-time analysis of solar radiation at the surface and top of the atmosphere using geostationary satellite data. Estimating atmospheric parameters and surface albedo requires accurate geolocation information to estimate the solar radiation accurately. The physical analysis algorithm for Earth observations is verified by the ground truth. In particular, downward solar radiation at the surface is validated by pyranometers installed at ground observation sites. The ground truth requires that the satellite observation data pixels be accurately linked to the location of the observation equipment on the ground. Thus, inaccurate geolocation information disrupts verification and causes complex problems. It is difficult to determine whether error in the validation of physical quantities arises from the estimation algorithm, satellite sensor calibration, or a geolocation problem. Geolocation error hinders the development of accurate analysis algorithms; therefore, accurate observational information with geolocation information based on latitude and longitude is crucial in atmosphere and land target analysis. This method provides the basic data underlying physical analysis, parallax correction, etc. Because the processing speed is important in geolocation correction, we used the phase-only correlation (POC) method, which is fast and maintains the accuracy of geolocation information in geostationary satellite observation data. Furthermore, two-dimensional fast Fourier transform allowed the accurate correction of multiple target points, which improved the overall accuracy. The reference dataset was created using NASA’s Shuttle Radar Topography Mission 1-s mesh data. We used HIMAWARI-8/Advanced HIMAWARI Imager data to demonstrate our method, with 22,709 target points for every 10-min observation and 5826 points for every 2.5 min observation. Despite the presence of disturbances, the POC method maintained its accuracy. Column offset and line offset statistics showed stability and characteristic error trends in the raw HIMAWARI standard data. Our method was sufficiently fast to apply to quasi real-time analysis of solar radiation every 10 and 2.5 min. Although HIMAWARI-8 is used as an example here, our method is applicable to all geostationary satellites. The corrected HIMAWARI 16 channel gridded dataset is available from the open database of the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The total download count was 50,352,443 on 8 July 2020. Our method has already been applied to NASA GeoNEX geostationary satellite products.
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Chai, Yunmeng, Yuehan Wang, and Wenqi Xue. "Deep learning-based glioma grading and feature visualization analysis." Applied and Computational Engineering 31, no. 1 (2024): 296–302. http://dx.doi.org/10.54254/2755-2721/31/20230173.

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With the rapid development of geolocation information services, which involves large-scale location data querying, including map data and user location data. Traditional data storage and query methods face challenges of storage space and query efficiency, and the ability to process real-time geolocation data becomes critical in Geographic Information Services positioning systems that require servers to achieve millisecond-level response speeds. In addition to this, in geolocation services, for a given latitude and longitude information, it is necessary to quickly determine whether it exists or matches some specific location in the map service. The common traditional information finding algorithm in geographic location service is B-tree, and this paper proposes a geographic location information finding algorithm based on the Bloom filter, which mainly solves two problems. One is the storage problem of large amounts of latitude and longitude information; the other is how to convert the data stored in bytes level to bits level, which greatly reduces the space complexity and the inefficient information finding. The Bloom filter reduces the query time to linear time complexity O(n). The fastest query time for 5 million items of latitude and longitude information can reach 3.12 seconds. This article will illustrate the efficiency of the proposed method through a real-time traffic navigation scenario.
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Dissertations / Theses on the topic "Geolocation data analysis"

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Wright, Christopher M. "Using Statistical Methods to Determine Geolocation Via Twitter." TopSCHOLAR®, 2014. http://digitalcommons.wku.edu/theses/1372.

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With the ever expanding usage of social media websites such as Twitter, it is possible to use statistical inquires to form a geographic location of a person using solely the content of their tweets. According to a study done in 2010, Zhiyuan Cheng, was able to detect a location of a Twitter user within 100 miles of their actual location 51% of the time. While this may seem like an already significant find, this study was done while Twitter was still finding its ground to stand on. In 2010, Twitter had 75 million unique users registered, as of March 2013, Twitter has around 500 million unique users. In this thesis, my own dataset was collected and using Excel macros, a comparison of my results to that of Cheng’s will see if the results have changed over the three years since his study. If found to be that Cheng’s 51% can be shown more efficiently using a simpler methodology, this could have a significant impact on Homeland Security and cyber security measures.
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(8764473), Luke Snyder. "Predictive Visual Analytics of Social Media Data for Supporting Real-time Situational Awareness." Thesis, 2020.

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<div>Real-time social media data can provide useful information on evolving events and situations. In addition, various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. Informed by discussions with first responders and government officials, we focus on two major barriers limiting the widespread adoption of social media for situational awareness: the lack of geotagged data and the deluge of irrelevant information during events. Geotags are naturally useful, as they indicate the location of origin and provide geographic context. Only a small portion of social media is geotagged, however, limiting its practical use for situational awareness. The deluge of irrelevant data provides equal difficulties, impeding the effective identification of semantically relevant information. Existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time, limiting situational awareness. In this work, we first adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness. We then present a novel interactive learning framework in which users rapidly identify relevant data by iteratively correcting the relevance classification of tweets in real-time. We integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system adapted for real-time situational awareness.</div>
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Book chapters on the topic "Geolocation data analysis"

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Makhortykh, Mykola. "Geospatial Data Analysis in Russia’s Geoweb." In The Palgrave Handbook of Digital Russia Studies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42855-6_32.

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AbstractThe chapter examines the role of geospatial data in Russia’s online ecosystem. Facilitated by the rise of geographic information systems and user-generated content, the distribution of geospatial data has blurred the line between physical spaces and their virtual representations. The chapter discusses different sources of these data available for Digital Russian Studies (e.g., social data and crowdsourced databases) together with the novel techniques for extracting geolocation from various data formats (e.g., textual documents and images). It also scrutinizes different ways of using these data, varying from mapping the spatial distribution of social and political phenomena to investigating the use of geotag data for cultural practices’ digitization to exploring the use of geoweb for narrating individual and collective identities online.
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Jaroš, Václav. "Geolocation Data as a Research Tool for the Organization of the Settlement System and Mobility Mapping – Case Study of the Spatial Mobility Model in Czechia." In AI, Data, and Digitalization. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53770-7_1.

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AbstractGeolocation data is a widely used source of the spatial information about the population. Their great potential might be also used for population mobility research to identify spatial interactions forming the hierarchical structure of the settlement system. For this purpose, a model of data acquisition and their preliminary analysis was developed. This model represents an effective tool for mapping the mobility behavior of the population. Using the example of Czechia, primary commuting links are identified, which are subsequently analyzed in detail using GIS tools in both desktop and online environments. Therefore, important commuting centers of different hierarchical levels are defined by the volume and nature of spatial interactions. This approach is used as a source of important expertise for the proposals on subsequent administration reform in Czechia. Nevertheless, the entire model is generally transferable, and the entire method of using the geolocation data for mapping the hierarchy within the settlement system can be replicated in other countries as well.
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Rahouma, Kamel H., and Aya S. A. Mostafa. "A 3D Geolocation Analysis of an RF Emitter Source with Two RF Sensors Based on Time and Angle of Arrival." In Studies in Big Data. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59338-4_30.

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Ferreira, Marta Campos, Teresa Galvão Dias, and João Falcão e Cunha. "With Whom Transport Operators Should Partner? An Urban Mobility and Services Geolocation Data Analysis." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14757-0_10.

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Knittel, Johannes, Franziska Huth, Steffen Koch, and Thomas Ertl. "Toward Visually Analyzing Dynamic Social Messages and News Articles Containing Geo-Referenced Information." In Volunteered Geographic Information. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_6.

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AbstractThe number of social media posts and news articles that are being published every day is high. This makes them an attractive source of human-generated information for different domain experts such as journalists and business analysts but also emergency responders, particularly if posts contain references to geolocations. Visual analytics approaches can help to gain insights into such datasets and inform decision-makers. However, the high volume and the veracity of the data, as well as the velocity in the case of streaming data, pose challenges when supporting explorative analysis with interactive visualization. Based on four exemplary approaches, we outline recently proposed strategies to tackle these challenges. We describe how geo-aware filtering and anomaly detection methods can help to inform stakeholders based on geolocated tweets. We show that data-aware tag maps can provide analysts with an overview-first, details-on-demand visual summary of large amounts of text content over time. With space-filling curves, we can visualize the temporal evolution of geolocations in a two-dimensional plot without relying on animations that would impede comparative analyses. Additionally, we discuss the use of an efficient dynamic clustering algorithm for enabling large-scale visual analyses of streaming posts.
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Lemmel, Julian, Zahra Babaiee, Marvin Kleinlehner, et al. "Prediction of Tourism Flow with Sparse Geolocation Data." In Data Science—Analytics and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-42171-6_6.

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Uday, Jhanvi, and Mohona Ghosh. "Safeguarding GeoLocation for Social Media with Local Differential Privacy and L-Diversity." In Security, Privacy and Data Analytics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9089-1_2.

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Shamini, P. Baby, Shubham Trivedi, K. S. Shriram, R. R. Selva Rishi, and D. Sayyee Sabarish. "Exploratory Spatial Data Analysis (ESDA) Based on Geolocational Area." In Futuristic Communication and Network Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8338-2_13.

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Visoli, Marcos, Sandro Bimonte, Sônia Ternes, François Pinet, and Jean-Pierre Chanet. "Towards Spatial Decision Support System for Animals Traceability." In Data Mining. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2455-9.ch107.

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Animal traceability is a very important question for several government and private institutions from many points of view: economical, sanitary, etc. Traditional systems are able to memorize the main bovine movements, or to capture the geolocation of an animal using RFID. Now it should be possible to envisage a new generation of traceability systems in which the different locations are automatically recorded several times per day for each animal. These systems should also be coupled with analysis techniques to help decision-makers to take decisions, validate and/or reformulate their hypothesis. In this chapter the authors present a spatial decision support system dedicated to the animal geolocation acquisitions and analysis of possible sanitary problems. Indeed, in case of sanitary alerts, the system is able to determine the animals which have been in contact with a diseased animal exploiting historical trajectories of animals. It is applied to traceability of beef cattle using the Brazilian production system as a case study. OTAG focuses on improving methods and geotechnologies for recording reliable and accurate data on beef production.
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Hasni, Sarra. "Towards an Embedding-Based Approach for the Geolocation of Texts and Users on Social Networks." In Interdisciplinary Approaches to Spatial Optimization Issues. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-1954-7.ch012.

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The geolocation task of textual data shared on social networks like Twitter attracts a progressive attention. Since those data are supported by advanced geographic information systems for multipurpose spatial analysis, new trends to extend the paradigm of geolocated data become more emergent. Differently from statistical language models that are widely adopted in prior works, the authors propose a new approach that is adopted to the geolocation of both tweets and users through the application of embedding models. The authors boost the geolocation strategy with a sequential modelling using recurrent neural networks to delimit the importance of words in tweets with respect to contextual information. They evaluate the power of this strategy in order to determine locations of unstructured texts that reflect unlimited user's writing styles. Especially, the authors demonstrate that semantic proprieties and word forms can be effective to geolocate texts without specifying local words or topics' descriptions per region.
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Conference papers on the topic "Geolocation data analysis"

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Vinora, A., S. Afrin, Lavina Roshni Manoj, and A. Alagu Gomathi. "Exploratory Data Analysis of Geolocation Data using Machine Learning." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10915093.

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Christou, C. T., G. M. Jacyna, F. J. Goodman, D. G. Deanto, and D. Masters. "Geolocation analysis using Maxent and plant sample data." In 2015 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, 2015. http://dx.doi.org/10.1109/ths.2015.7225273.

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Taylor, David W. A., and Brian T. Clark. "GPS‐Denied Geolocation for Geophysical Data Acquisition and Analysis." In Symposium on the Application of Geophysics to Engineering and Environmental Problems 2008. Environment and Engineering Geophysical Society, 2008. http://dx.doi.org/10.4133/1.2963274.

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W.A. Taylor, David, and Brian T. Clark. "GPS-Denied Geolocation For Geophysical Data Acquisition And Analysis." In 21st EEGS Symposium on the Application of Geophysics to Engineering and Environmental Problems. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609-pdb.177.182.

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Kyunghyun Lee, Jungkeun Oh, and Kwanho You. "TDOA/AOA based geolocation using Newton method under NLOS environment." In 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2016. http://dx.doi.org/10.1109/icccbda.2016.7529586.

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Utomo, Muhammad Nur Yasir, Teguh Bharata Adji, and Igi Ardiyanto. "Geolocation prediction in social media data using text analysis: A review." In 2018 International Conference on Information and Communications Technology (ICOIACT). IEEE, 2018. http://dx.doi.org/10.1109/icoiact.2018.8350674.

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Kinakh, Vitaliy, Tomohiro Oda, and Rostyslav Bun. "Algorithms for Analysis of Geolocation Error of Nightlight Satellite Data and Greenhouse Gas Data Calculated on Their Basis." In 2020 IEEE 15th International Conference on Computer Sciences and Information Technologies (CSIT). IEEE, 2020. http://dx.doi.org/10.1109/csit49958.2020.9322037.

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Gupta, Vanshaj, Jaydeep Patel, Safa Shubbar, and Kambiz Ghazinour. "COVBERT: Enhancing Sentiment Analysis Accuracy in COVID-19 X Data through Customized BERT." In 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140212.

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In a time when social media information is a valuable resource for gaining insights, the COVID-19 pandemic has released a flood of public sentiment, abundant with unstructured text data. This paper introduces CovBERT, a novel adaptation of the BERT model, specifically honed for the nuanced analysis of COVID-19-related discourse on X (formerly Twitter). CovBERT stands out by incorporating a bespoke vocabulary, meticulously curated from pandemic-centric tweets, resulting in a remarkable leap in sentiment analysis accuracy—from the baseline 72\% to an impressive 78.64\%. This paper not only presents a detailed comparison of CovBERT with the standard BERT model but also juxtaposes it against traditional machine learning approaches, showcasing its superior proficiency in decoding complex emotional undercurrents in social media data. Furthermore, the integration of geolocation analysis pipeline adds another layer of depth, offering a panoramic view of global sentiment trends.
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Barth, Raul S., and Renata Galante. "Passenger density and flow analysis and city zones and bus stops classification for public bus service management." In Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/sbbd.2016.24331.

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This work presents, for the first time in literature, a low-cost framework to mine data obtained from passengers smart cards, buses GPS and bus stops geolocation using Lambda Architecture approach. Operators, companies, government and passengers will use this knowledge for improving usability, comfort, and quality of transportation service. This analysis gives greater insight into the volume and flow of passengers and the real existing demand for bus services, facilitating its control and management, allowing decision-making. As result, bus stops and city areas are classified according to buses demand.
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10

Ilieva, Tamara. "DEVELOPMENT OF INDOOR POSITIONING SYSTEM, BASED ON LEAFLET MAP, GEOLOCATION AND WI-FI POSITION ESTIMATION WITH ACCURACY ASSESSMENT." In 23rd SGEM International Multidisciplinary Scientific GeoConference 2023. STEF92 Technology, 2023. http://dx.doi.org/10.5593/sgem2023/2.1/s08.21.

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Abstract:
Indoor positioning systems have a wide variety of applications. They can be used to see and track user location, for real-time navigation and others. They are usually developed for a specific building or set of buildings and may use different positioning techniques to meet the user needs in terms of accuracy and budget constraints. For the current study free and open source data is used for development of the base map � OpenStreetMap and Leaflet library. The data for specific building is added, as the floor plans are transformed to GeoJSON format. As positioning techniques, in this case, geolocation and Wi-Fi position estimation are used and there are three main steps for calculating user coordinates: � The user gets the available Wi-Fi signals strength and based on defined fixed limits the algorithm choose which of the preliminary coordinated and fingerprinted control points in the building is the closest; � Geolocation data for the user (latitude and longitude) is streamed and trough communication channel is imported in the developed system; � The user location is translated to the nearest control point and corrections are calculated as coordinate differences, similar to differential outdoor GNSS measurements with initialization on known points, but with modification only for the rover absolute position, so the indoor position of the user is corrected and shown on the map. The data was collected repeatedly static for 6 points in the building (3 on the first floor and 3 on the second), the corrections are calculated after 3 seconds of observations and applied to the coordinates for 1 minute intervals. All corrected coordinates are compared to the control ones, obtained by geodetic measurements in order to be analyzed the corrections validity. Also, tracks were made, so it could be seen what is the difference between the real moving trajectory, the corrected and the measured ones. The results of the analysis show that the data collected by this indoor positioning system is not very precise, compared to other systems, based on other positioning techniques � the difference compared to absolute position is approximately 1.8 m. Even though, this position estimation is much better than standard accuracy achieved by separate use of geolocation or Wi-Fi positioning with only 2 Wi-Fi sources, without preliminary defined signal strength fingerprint map.
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