Geolocation Data Research Articles

The Eye in the Sky—A Method to Obtain On-Field Locations of Australian Rules Football Athletes

The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools.

Geolocation Analysis System using K-Mean Clustering

We propose a system using K-Means Clustering and data analysis technique, to recommend accommodations for migrants based on factors like facilities, budget, and proximity to their destination. By analyzing geo-locational data, we can classify different types of accommodations such as PGs, flats, hostels, and nearby amenities like food stalls and daily necessities.

Estimating Malware Impact on Network Traffic Analysis by Using Wireshark

With the increasing prevalence of advanced cyber threats, there is a growing need for effective cybersecurity measures that can detect and visualize malware attacks. This research introduces an integrated approach that combines malware detection techniques with geospatial visualization methods to enhance the identification and analysis of cyber-attacks. By analyzing packets in the HTTP protocol, we identify suspicious file transfers and compute their hash values for further examination. To assess the threat level of these transferred files, we utilize the Virus Total platform to conduct comprehensive scans for malware. At the same time, by utilizing geolocation data, we map out both the origins and destinations of these attacks, providing valuable spatial context for understanding global patterns in cyber threats. In addition to enabling the identification of potentially harmful files, the proposed approach also provides a comprehensive visualization of how these threats are spread geographically. Our findings contribute to advancing cybersecurity strategies by facilitating proactive threat mitigation and enhancing incident response capabilities. The integration of malware detection, hash analysis, and geospatial visualization emphasizes the importance of adopting a multidimensional approach in strengthening network security infrastructure.

Characterizing behavioral responses of Pacific cod to bottom trawl vessels and gear using archival tag accelerometer data

Fish behavior during the capture process affects the overall efficiency of survey vessels and gear, influencing selectivity and catchability, which in turn influences stock assessments. The effects of behavior on capture are typically investigated through experiments that use video or acoustic technologies to observe behavioral responses of fish to vessels and gear directly. While these approaches provide valuable information about fish behavior, their use is limited to observing the movement of fish inside a small observed area. In this proof-of-concept study, we demonstrate a novel application of bio-logging Pop-up Satellite Archival Tags (PSATs) equipped with accelerometer sensors to observe behavioral responses of free-swimming demersal fish to approaching bottom trawl fishing vessels and gear. By combining PSAT and fishing vessel geolocation data from the Vessel Monitoring System, we characterized diving and herding responses of three individual Pacific cod (Gadus macrocephalus) at-liberty for 21–106 days to trawling fishing vessels. We found that fish descended as trawlers approached, presumably in response to noise generated by the fishing vessel and gear, which supports the previously untested hypothesis that Pacific cod dive in response to approaching fishing vessels and gear, similar to closely related gadids. Additionally, one 88 cm fish swam ahead of a trawl towed at 1.25 m·s−1 (i.e., 1.42 body lengths per second) for 55.6 minutes, covering a distance of 4.17 km before capture, which challenges previous assertions that Pacific cod are unlikely to outswim bottom trawl gear towed at typical towing speeds. This study demonstrates that opportunistically collected archival tag data can be used to improve knowledge of behavioral interactions between fish and fishing gear.

A Survey of Various Recommendation Systems

Geolocation-based recommender systems have gained significant traction in recent years due to their ability to provide personalized recommendations tailored to users' geographical locations [1]. This survey paper explores the landscape of food recommendation systems with a specific focus on geographical aspects. It examines existing frameworks, solutions, and challenges in the field, highlighting key research contributions and methodologies [2]. By incorporating spatial context into the recommendation process, these systems can identify nearby food establishments, consider regional culinary preferences, and recommend dishes that are popular or highly rated in the vicinity [1]. The paper also discusses the potential of location-aware recommendation systems and proposes a novel approach for food recommendation based on geographical location [3]. Various methodologies employed in geolocation-based food recommendation systems, including collaborative filtering, content-based filtering, and hybrid approaches, are explored, along with their advantages and limitations [5]. Challenges associated with developing geolocation-based recommendation systems, such as data privacy concerns and data sparsity in certain regions, are also addressed [4]. The proposed approach aims to provide more accurate and personalized recommendations by integrating geolocation data with user preferences and contextual information, thereby enriching the overall dining experience for users worldwide [3].

Geolocation data incorporation in Mapbox for comprehensive mapping of tourism areas on Lombok Island

Lombok Island is one of the islands that has many tourist areas. With so many tourist areas spread to various regions on the island of Lombok, an accurate and comprehensive tourist area mapping system is needed. The problem faced is that the existing mapping is still constrained regarding accuracy and data persistence. The solution offered in this research is the incorporation of geolocation data on the Mapbox platform to improve the accuracy and detail of data in mapping tourism areas on the island of Lombok. In this research, there are several stages carried out starting from data collection to testing. This research results in a tourist area mapping information system that applies geolocation data incorporation on Mapbox. The test results show an increase in accuracy of 8% from the previous mapping and a usability test score of 81 which means that the system developed is acceptable or feasible by users.

CYBER-PHYSICAL SYSTEM FOR MONITORING THE ENVIRONMENT FOR ALLERGENS USING GEOLOCATION DATA

The task of creating a cyber-physical system for monitoring the environment for allergens is relevant. Therefore, our study is devoted to the development of a method and a cyber-physical system for monitoring the environment for allergens using geolocation data. The developed cyber-physical system for monitoring the environment for allergens not only uses the available information on the activity of pollen sources in a given region, but also measures the concentration of pollen in the air in real time, which helps to monitor the level of pollen in the region where the user is located, and, taking into account the user's geolocation data, provides him with useful information for route planning to avoid staying in high-risk areas. Collecting and analyzing geolocation data allows you to track changes in the distribution of allergens over time and in space. This can be useful for understanding trends in allergenic reactions and for developing effective allergy management strategies. The cyber-physical system for monitoring the environment for allergens is useful for people with allergies by providing them with accessible and up-to-date information about the environment and the risks of allergic reactions, allowing them to better plan their activities and avoid areas with high levels of plant pollen concentration. Such a cyber-physical system enables an individualized approach to each user, taking into account their unique problems by performing allergen filtering. This allows for effective and personalized allergy management solutions. The proposed cyber-physical system can create a valuable database with allergic reactions and predictions that will be used to improve the methods of diagnosis, treatment and management of allergies. The proposed cyber-physical system can be used for global monitoring of pollen and allergen levels in different parts of the world. This will allow us to identify global trends in the distribution of allergens and develop effective strategies for managing allergies at the global level.

What can location-based social media reveal on human migration patterns in Europe?

ABSTRACT Numerous visualisation methods have been proposed, including Origin-Destination maps to represent movement patterns gathered from social media; however, visual clutter remains a persistent issue due to complex data dimensionality. Besides, most Origin-Destination maps fail to illustrate the temporal dimension of social network phenomena within the geographical environment. To tackle this issue, we propose the visualisation method for geo-located Facebook social-media data while emphasising the time aspect. Based on the citizen-generated data for the European Union (EU), we estimated the EU citizens’ residing or travelling across the EU member states as a means of current and previous destinations to reveal the extent of the hypothetical human migration. The proposed methodology consists of Origin-Destination maps implemented within the time geography framework as a model to support the process of analysis for decision-making. The generated visualisation allows comprehension of the scale of human movement distribution internally within the EU from a space–time perspective.

Differences in Emotional Preferences toward Urban Green Spaces among Various Cultural Groups in Macau and Their Influencing Factors

This study explores the diversity in emotional tendencies and needs toward urban green spaces (UGSs) among people from different cultural backgrounds in the wave of cultural integration. We utilized social media data as research tools, gathering a wide range of perspectives and voices. Utilizing geolocation data from 176 UGSs in Macau, we collected 139,162 social media comments to analyze the emotional perceptions of different cultural groups. Furthermore, we conducted regression analysis on the number of posts and emotional intensity values from four linguistic groups—Chinese, English, Southeast Asian languages, and Portuguese—in UGSs, correlating them with ten locally relevant landscape features. Our findings reveal diverse attitudes, emotional inclinations, and functional and design needs of different linguistic groups toward UGSs, as follows: (1) there were significant differences in emotional intensity and tweet counts across 176 UGSs; (2) Chinese and Portuguese speakers showed a more positive attitude toward plazas and natural ecological areas, whereas English- and Southeast-Asian-language speakers tended to favor recreational areas and suburban parks; (3) Chinese speakers exhibited a more positive emotional intensity toward sports facilities, while English speakers placed more emphasis on green space areas, architecture, sports infrastructure, and plant landscapes; (4) there was no specific landscape feature preference for Portuguese- and Southeast-Asian-language speakers. This research not only deepens our understanding of the emotional perceptions and preferences of UGSs among different cultural groups but also explores the association between these groups and various urban landscape features. This provides important theoretical and practical insights for future UGS planning, construction, and promoting multicultural coexistence for sustainable urban development.

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine.

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform. 111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences. A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total). The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

Enhancing Wireless Security and Privacy: A 2-Way Identity Authentication Method for 5G Networks

The communication network would have potential uses dependent on the infrastructure once the fifth generation (5G) network is fully commercialized. Subscriber Identification Module (SIM) authentication is a well-known mechanism for safeguarding the confidentiality of wireless device users. Fifth-generation computer technologies are very advanced in certification based on geolocation, regular confirmation, and authentication mechanisms. The clonable authentication feature solves the issue of authentication using duplicate modules. In highly dynamic environments, the problem poses a risk to privacy protection, adaptiveness, self-organization, information leakage, and the reliability of services. The article introduces a 2-way identity authentication method (2WIAM) using a Physical Unclonable Function (PUF) to address the issue above. The suggested technique for detecting module clones depends on user-provided authentication and geolocation data. The 2-way mutual authentication used in the suggested technique is a clever way to combine the many wireless technologies accessible to a given network node and enable the creation of wireless on-demand service providers and networks. The initial stage involves immediate passcode authentication, and the second is verifying the identity of the mobile equipment and any shifts or copies in its physical position. A new-cross authentication is then carried out to determine the identity of the clone's physical counterpart. In prior authentication situations, the choice to provide authorization or ban the clone was made fairly and objectively. The experiment results show that the users' privacy is protected, enhanced adaptiveness and the amount of data lost is limited.

Using k means clustering and integrated 2 DULV'S topsis method for geolocational data

Using k means clustering and integrated 2 DULV'S topsis method for geolocational data

Positive Outliers: A Mixed Methods Study of Resiliency to Childhood Obesity in High-Risk Neighborhoods

ObjectiveDespite the high prevalence of obesity and the clustering of risk by neighborhood, few studies have examined characteristics which promote healthy child weight in neighborhoods with high obesity risk. We aimed to identify protective factors for children living in neighborhoods with high obesity risk. MethodsWe identified neighborhoods with high obesity risk using geolocated electronic health record data with measured body mass index (BMI) from well-child visits (2012–2017). We then recruited caregivers with children aged 5 to 13 years who lived in census tracts with mean child BMI percentile ≥72 (February 2020–August 2021). We used sequential mixed methods (quantitative surveys, qualitative interviews) to compare individual, interpersonal, and perceived neighborhood factors among families with children at a healthy weight (positive outliers [PO]) versus families with ≥1 child with overweight or obesity (controls). Regression models and comparative qualitative analysis were used to identify protective characteristics. ResultsSeventy-three caregivers participated in the quantitative phase (41% PO; 34% preferred Spanish) and twenty in the qualitative phase (50% PO; 50% preferred Spanish). The frequency of healthy caregiver behaviors was associated with being a PO (Family Health Behavior Scale Parent Score adjusted β 3.67; 95% CI 0.52–6.81 and qualitative data). Protective factors also included caregivers’ ability to minimize the negative health influences of family members and adhere to family routines. ConclusionsThere were few differences between PO and control families. Support for caregiver healthy habits and adherence to healthy family routines emerged as opportunities for childhood obesity prevention in neighborhoods with high obesity risk.

Geo-localized Network Data Analysis Using VIKOR: Application of Network Health Monitoring in Turkey

Middle-high level managers of operators of telecommunications have to follow many key parameters to increase the profitability along with strategy of company. Therefore, easily readable data are being provided to managers by experts. With large screens in rooms, these data are monitored momentarily and ensured to take necessary actions without creating customer dissatisfaction. Especially for operators, it is one of the key parameters to manage the network in a quality and healthy way without creating complaints. It is important to monitor network health with appointed criteria and methods. In this context, there have been weights of province-based network health by processing geolocation-based big data and using the VIKOR method. Thus, there will have been presented the network monitoring opportunity from instant real user information to managers. Although there is a lot of criteria and methods for network monitoring, it will have been one of the priority studies that will enable the analysis of geo location data with a MCDM method in this area.

The MAPSTROKE project: A computational strategy to improve access to acute stroke care.

Global access to acute stroke treatment is variable worldwide, with notable gaps in low and middle-income countries (LMIC), especially in rural areas. Ensuring a standardized method for pinpointing the existing regional coverage and proposing potential sites for new stroke centers is essential to change this scenario. To create and apply computational strategies (CSs) to determine optimal locations for new acute stroke centers (ASCs), with a pilot application in nine Latin American regions/countries. Hospitals treating acute ischemic stroke (AIS) with intravenous thrombolysis (IVT) and meeting the minimum infrastructure requirements per structured protocols were categorized as ASCs. Hospitals with emergency departments, noncontrast computed tomography (NCCT) scanners, and 24/7 laboratories were identified as potential acute stroke centers (PASCs). Hospital geolocation data were collected and mapped using the OpenStreetMap data set. A 45-min drive radius was considered the ideal coverage area for each hospital based on the drive speeds from the OpenRouteService database. Population data, including demographic density, were obtained from the Kontur Population data sets. The proposed CS assessed the population covered by ASCs and proposed new ASCs or artificial points (APs) settled in densely populated areas to achieve a target population coverage (TPC) of 95%. The observed coverage in the region presented significant disparities, ranging from 0% in the Bahamas to 73.92% in Trinidad and Tobago. No country/region reached the 95% TPC using only its current ASCs or PASCs, leading to the proposal of APs. For example, in Rio Grande do Sul, Brazil, the introduction of 132 new centers was suggested. Furthermore, it was observed that most ASCs were in major urban hubs or university hospitals, leaving rural areas largely underserved. The MAPSTROKE project has the potential to provide a systematic approach to identify areas with limited access to stroke centers and propose solutions for increasing access to AIS treatment. Data used for this publication are available from the authors upon reasonable request.

A Literature Review of Exploratory Analysis of Geolocational Data

Geospatial data is a key part of the digital age. It contains geographic coordinates and location information of people, vehicles, objects and natural phenomena. The rapid growth in its use is due to smartphones, GPS, social media and various location-based applications. This has led to a new paradigm shift in how data is used across industries. This article looks at the multifaceted role of geolocation data in today's society and examines how important it is in the development and change of various industries in today's world. Geospatial data plays a key role in the digital age and the birth of the Internet of Things. In addition to smooth navigation, it also helps industries better allocate resources, improve decision-making and increase overall efficiency. This report explores the growing importance of spatial data and its critical role in urban planning, transportation, environmental monitoring and surveillance, marketing, and public safety

Deep learning-based glioma grading and feature visualization analysis

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.

Research on the fast geolocation algorithm based on Bloom filter

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.

Residential Address Amplifies Health Disparities and Risk of Infection in Individuals With Diabetic Foot Ulcers.

To determine the association between social determinants of health (SDOH) and a diagnosis of diabetic foot ulcer (DFU) infection. Targeted interrogation of electronic health record data using novel search engines to analyze individuals with a DFU infection during a 5-year period (2013-2017) was performed. We extracted geolocated neighborhood data and SDOH characteristics from the National Neighborhood Data Archive and used univariate and multiple logistic regression to evaluate associations with outcomes in the population with diabetes. Among 4.3 million people overall and 144,564 individuals with diabetes seen between 2013 and 2017, 8,351 developed DFU, of which cases 2,252 were complicated by a DFU infection. Sex interactions occurred, as men who experienced a DFU infection more frequently identified as having nonmarried status than their female counterparts. For the population with DFU infection, there were higher rates for other SDOH, including higher neighborhood disadvantaged index score, poverty, nonmarriage, and less access to physician/allied health professionals (all P < 0.01). In multiple logistic regression, those individuals who developed DFU infection came from neighborhoods with greater Hispanic and/or foreign-born concentrations (odds ratio 1.11, P = 0.015). We found significant differences in neighborhood characteristics driving a higher risk for DFU infection in comparisons with the grouping of individuals with diabetes overall, including increased risk for individuals with Hispanic and/or foreign-born immigration status. These data strongly support the need to incorporate SDOH, particularly ethnic and immigration status, into triage algorithms for DFU risk stratification to prevent severe diabetic foot complications and move beyond biologic-only determinants of health.

Comparison of home detection algorithms using smartphone GPS data

Estimation of people’s home locations using location-based services data from smartphones is a common task in human mobility assessment. However, commonly used home detection algorithms (HDAs) are often arbitrary and unexamined. In this study, we review existing HDAs and examine five HDAs using eight high-quality mobile phone geolocation datasets. These include four commonly used HDAs as well as an HDA proposed in this work. To make quantitative comparisons, we propose three novel metrics to assess the quality of detected home locations and test them on eight datasets across four U.S. cities. We find that all three metrics show a consistent rank of HDAs’ performances, with the proposed HDA outperforming the others. We infer that the temporal and spatial continuity of the geolocation data points matters more than the overall size of the data for accurate home detection. We also find that HDAs with high (and similar) performance metrics tend to create results with better consistency and closer to common expectations. Further, the performance deteriorates with decreasing data quality of the devices, though the patterns of relative performance persist. Finally, we show how the differences in home detection can lead to substantial differences in subsequent inferences using two case studies—(i) hurricane evacuation estimation, and (ii) correlation of mobility patterns with socioeconomic status. Our work contributes to improving the transparency of large-scale human mobility assessment applications.