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Background Low physical activity levels and high levels of sedentary time among adolescents call for population wide interventions. Public open spaces can be important locations for adolescents’ physical activity. This study aimed to describe the prevalence, frequency and context of public open space visitation and to gain insight into the individual, social and physical environmental factors associated with public open space use among 12- to 16-year-old Flemish (Belgian) adolescents. Methods Global positioning system devices, accelerometers and one-on-one interviews were used to measure location-specific activity levels, time spent at, reasons for using and accompaniment at public open spaces among 173 adolescents. Multilevel hurdle and gamma models were used to estimate the associations between the independent variables (age, gender, ethnicity, education, sport club membership and accompaniment) and the amount of time, sedentary time, light-, moderate- to vigorous- and vigorous-intensity physical activity at public open spaces. Results Three out of four participants had visited a public open space (for recreational purposes) and participants were most often accompanied by friends/classmates. Mainly public transportation stops/stations were used, and subsequently the most reported reason for public open space use was “to wait for something or someone”. Furthermore, boys, younger adolescents, non-western-European adolescents and lower educated adolescents were more likely to use public open spaces. Additionally, boys and younger adolescents were more likely to accumulate physical activity at public open spaces. The only social environmental variable associated with time spent at public open spaces was accompaniment by siblings: adolescents spent more time at public open spaces when accompanied by their siblings. Conclusions Public open spaces may be effective areas to promote physical activity among groups at risk for physical inactivity (i.e. low educated and non-western-European adolescents). Additionally, girls and older adolescents were less likely to visit and be physically active at public open spaces. Therefore, urban planners should consider adding attractive features, in order to encourage physical activity among girls and older adolescents at public open spaces. Furthermore, creating public open spaces that are attractive for youth of all ages could contribute to adolescents visiting public open spaces accompanied by siblings.

The influence of the Earth's magnetic field on locomotory orientation has been studied in many taxa but is best understood for homing pigeons (Columba livia). Effects of experimentally induced and naturally occurring perturbations in the geomagnetic field suggest that pigeons are sensitive to changes in geomagnetic parameters. However, whether pigeons use the Earth's magnetic field for position determination remains unknown. Here we report an apparent orientation to the intensity gradient of the geomagnetic field observed in pigeons homing from sites in and around a magnetic anomaly. From flight trajectories recorded by GPS-based tracking devices, we noted that many pigeons released at unfamiliar sites initially flew, in some cases up to several kilometres, in directions parallel and/or perpendicular to the bearing of the local intensity field. This behaviour occurred irrespective of the homeward direction and significantly more often than what was expected by random chance. Our study describes a novel behaviour which provides strong evidence that pigeons when homing detect and respond to spatial variation in the Earth's magnetic field-information of potential use for navigation.

Understanding how individuals manage costs during the migration period is challenging because individuals are difficult to follow between sites; the advent of hybrid Global Positioning Systemacceleration （ACC） tracking devices enables researchers to link spatial and temporal attributes of avian migration with behavior for the first time ever. We fitted these devices on male Greenland white-fronted geese Anser albifrons flavirostris wintering at 2 sites （Loch Ken, Scotland and Wexford, Ireland） to understand whether birds migrating further during spring fed more on winter- ing and staging areas in advance of migration episodes. Although Irish birds flew significantly further （ca. 300 kin） than Scottish birds during spring, their cumulative hours of migratory flight, flight speed during migration, and overall dynamic body ACC （i.e., a proxy for energy expenditure） were not significantly different. Further, Irish birds did not feed significantly more or expend significantly more energy in advance of migration episodes. These results suggest broad individual plasticity in this species, although Scottish birds arriving on breeding areas in Greenland with greater energy stores （because they migrated less） may be better prepared for food scarcity, which might increase their reproductive success.

Mobile location data has emerged as a valuable data source for studying human mobility patterns in various contexts, including virus spreading, urban planning, and hazard evacuation. However, these data are often anonymized overviews derived from a panel of traced mobile devices, and the representativeness of these panels is not well documented. Without a clear understanding of the data representativeness, the interpretations of research based on mobile location data may be questionable. This article presents a comprehensive examination of the potential biases associated with mobile location data using SafeGraph Patterns data in the United States as a case study. The research rigorously scrutinizes and documents the bias from multiple dimensions, including spatial, temporal, urbanization, demographic, and socioeconomic, over a five-year period from 2018 to 2022 across diverse geographic levels, including state, county, census tract, and census block group. Our analysis of the SafeGraph Patterns dataset revealed an average sampling rate of 7.5% with notable temporal dynamics, geographic disparities, and urban-rural differences. The number of sampled devices was strongly correlated with the census population at the county level over the five years for both urban (r > 0.97) and rural counties (r > 0.91), but less so at the census tract and block group levels. We observed minor sampling biases among groups such as gender, age, and moderate-income, with biases typically ranging from -0.05 to +0.05. However, minority groups such as Hispanic populations, low-income households, and individuals with low levels of education generally exhibited higher levels of underrepresentation bias that varied over space, time, urbanization, and across geographic levels. These findings provide important insights for future studies that utilize SafeGraph data or other mobile location datasets, highlighting the need to thoroughly evaluate the spatiotemporal dynamics of the bias across spatial scales when employing such data sources.

Today, crop suggestions and necessary guidance have become a regular need for a farmer. Farmers generally depend on their local agriculture officers regarding this, and it may be difficult to obtain the right guidance at the right time. Nowadays, crop datasets are available on different websites in the agriculture sector, and they play a crucial role in suggesting suitable crops. So, a decision support system that analyzes the crop dataset using machine learning techniques can assist farmers in making better choices regarding crop selections. The main objective of this research is to provide quick guidance to farmers with more accurate and effective crop recommendations by utilizing machine learning methods, global positioning system coordinates, and crop cloud data. Here, the recommendation can be more personalized, which enables the farmers to predict crops in their specific geographical context, taking into account factors like climate, soil composition, water availability, and local conditions. In this regard, an existing historical crop dataset that contains the state, district, year, area-wise production rate, crop name, and season was collected for 246,091 sample records from the Dataworld website, which holds data on 37 different crops from different areas of India. Also, for better analysis, a dataset was collected from the agriculture offices of the Rayagada, Koraput, and Gajapati districts in Odisha state, India. Both of these datasets were combined and stored using a Firebase cloud service. Thirteen different machine learning algorithms have been applied to the dataset to identify dependencies within the data. To facilitate this process, an Android application was developed using Android Studio (Electric Eel | 2023.1.1) Emulator (Version 32.1.14), Software Development Kit (SDK, Android SDK 33), and Tools. A model has been proposed that implements the SMOTE (Synthetic Minority Oversampling Technique) to balance the dataset, and then it allows for the implementation of 13 different classifiers, such as logistic regression, decision tree (DT), K-Nearest Neighbor (KNN), SVC (Support Vector Classifier), random forest (RF), Gradient Boost (GB), Bagged Tree, extreme gradient boosting (XGB classifier), Ada Boost Classifier, Cat Boost, HGB (Histogram-based Gradient Boosting), SGDC (Stochastic Gradient Descent), and MNB (Multinomial Naive Bayes) on the cloud dataset. It is observed that the performance of the SGDC method is 1.00 in accuracy, precision, recall, F1-score, and ROC AUC (Receiver Operating Characteristics–Area Under the Curve) and is 0.91 in sensitivity and 0.54 in specificity after applying the SMOTE. Overall, SGDC has a better performance compared to all other classifiers implemented in the predictions.

Location‐based services have attracted significant attention since the concept of Industry 4.0 was proposed and the ‘Internet +’ era began owing to its social and commercial value. Many scholars have attempted to introduce machine learning in indoor fingerprint positioning to improve indoor positioning accuracy, enhance system robustness, reduce costs, and improve the performance of indoor positioning methods. A comprehensive overview of indoor positioning technology, its methods, and classifications is provided. Furthermore, a detailed review of the application of WiFi fingerprinting and machine learning methods in indoor positioning is presented, and the advantages and disadvantages of these methods when applied to indoor positioning are analyzed. This study summarizes the difficulties and challenges encountered by indoor positioning and suggests development directions.

Reliable positioning and navigation is becoming imperative in more and more applications for public services, consumer products, and safety-critical purposes. Research for finding pervasive and robust positioning methodologies is critical for a growing amount of societal areas while making sure that navigation is trustworthy and the risks and threats of especially satellite navigation are accounted for. This book provides a comprehensive survey of the effect of radio-frequency interference (RFI) on the Global Navigation Satellite Systems (GNSS) as well as of the spoofing threats. Through case studies and practical implementation/applications, this resource presents engineers and scientists with a better understanding of interference and spoofing threats, ultimately helping them to design and implement robust systems.

Satellite telemetry is an increasingly utilized technology in wildlife research, and current devices can track individual animal movements at unprecedented spatial and temporal resolutions. However, as we enter the golden age of satellite telemetry, we need an in-depth understanding of the main technological, species-specific and environmental factors that determine the success and failure of satellite tracking devices across species and habitats. Here, we assess the relative influence of such factors on the ability of satellite telemetry units to provide the expected amount and quality of data by analyzing data from over 3,000 devices deployed on 62 terrestrial species in 167 projects worldwide. We evaluate the success rate in obtaining GPS fixes as well as in transferring these fixes to the user and we evaluate failure rates. Average fix success and data transfer rates were high and were generally better predicted by species and unit characteristics, while environmental characteristics influenced the variability of performance. However, 48% of the unit deployments ended prematurely, half of them due to technical failure. Nonetheless, this study shows that the performance of satellite telemetry applications has shown improvements over time, and based on our findings, we provide further recommendations for both users and manufacturers.

The use of global navigation satellite systems (GNSS) technology within the fields of ecology and biology has increased over recent years. With Global Positioning System, GLObalnaya NAvigatsionnaya Sputnikovaya Sistema, Galileo, and BeiDou systems fully deployed, >140 navigation satellites are currently available for navigation and high precision positioning applications. The technological improvements in GNSS devices, mainly due to the multi-frequency capability, reduce signal acquisition time and increase the reliability, the continuity, and the accuracy of the estimated position, especially in complex environments like forests or areas with a steep topography. This study aims to test experimentally the influence of multi-constellation multi-frequency modules on the performance of the GNSS collars used to monitor wildlife. We applied static and kinematic tests designed to assess and compare the performance of GNSS collars equipped with a single-frequency versus a multi-frequency chipset. We evaluated the availability and continuity of solutions, number of satellites used, dilution of precision, precision, accuracy, and repeatability of these quality parameters for GNSS devices in southeastern Spain from February 2021 to June 2022. The results confirmed that the multi-constellation multi-frequency GNSS collar showed a stable and good performance in terms of the number of satellites used (>10), horizontal dilution of precision (<1.3), fix success rate (100%), mean location error (<1.5 m), and circular error probability (CEP), which was better by approximately one order of magnitude than the single-frequency collar (1–10 m). The use of reliable and accurate GNSS devices will expand our knowledge of animal behavior and the interactions between species. Multi-frequency GNSS collars allow collection of accurate locations, providing fine-scale information about animal behavior (e.g., feeding strategies, competition for resources), whereas the single-frequency GNSS collars can used for broad-scale studies (e.g., home ranges, habitat use).

The growing interest for indoor position-based applications and services, as well as ubiquitous computing and location aware information, have led to increasing efforts toward the development of positioning techniques. Many applications require accurate positioning or tracking of people and assets inside buildings, and some market sectors are waiting for such technologies for starting a fast growth. Ultrasonic systems have already been shown to possess the desired positioning accuracy and refresh rate. However, they still require accurate synchronization between ultrasound emitters and receivers to work properly. Usually, synchronization is carried out through radio frequency (RF) signals, adding system complexity and raising the cost. In this work, this limit is overcome by introducing a novel self-synchronizing indoor positioning technique. Ultrasonic signals travel from emitters placed at fixed reference positions to any number of mobile devices (MD). The travelled distance is computed from the time of flight (TOF), which requires in turn synchronism between emitter and receiver. It is shown that this synchronism can be indirectly estimated from the time difference of arrival (TDOA) of the ultrasonic signals. The obtained positioning information is private, in the sense that the positioning infrastructure is not aware of the number or identity of the MDs that use it. Computer simulations and experimental results obtained in a typical office room are provided.