Global Positioning System Research Articles

Assessing the potential of camera traps for estimating activity pattern compared to collar‐mounted activity sensors: a case study on Eurasian lynx Lynx lynx in south‐eastern Norway

The diel activity patterns of animals convey information about physiology, ecological niches and animal behaviour relevant for both applied conservation and more theoretical research. However, these patterns are challenging to study in the field. The current gold‐standard approach to quantify movements and activity patterns of medium to large wildlife species is to use global positioning systems (GPS) collars equipped with activity sensors (e.g. accelerometers). A more recent approach consists of inferring activity patterns from the time‐stamped pictures of wildlife obtained from camera traps now routinely used in wildlife monitoring projects. However, few studies have attempted to validate estimates of activity patterns obtained from camera traps against those obtained from activity sensors. In this study, we compared the diel activity pattern of the Eurasian lynx Lynx lynx inferred from detections by a network of over 300 camera traps active between 2010 and 2020, to activity patterns obtained from 18 GPS‐collared lynx (8 females, 10 males) equipped with 2‐axis accelerometer sensors, in the same area of southern Norway. Our results suggest that camera traps can be used to estimate diel activity curves that are comparable to those obtained from accelerometers. In our study, 75 detections were sufficient to approximate the diel activity pattern obtained from accelerometer. Subsampling indicated that a low number of detections results in a coarser approximation of the diel activity pattern.

Analyzing Key Factors on Training Days within a Standard Microcycle for Young Sub-Elite Football Players: A Principal Component Approach.

Utilizing techniques for reducing multivariate data is essential for comprehensively understanding the variations and relationships within both biomechanical and physiological datasets in the context of youth football training. Therefore, the objective of this study was to identify the primary factors influencing training sessions within a standard microcycle among young sub-elite football players. A total of 60 male Portuguese youth sub-elite footballers (15.19 ± 1.75 years) were continuous monitored across six weeks during the 2019-2020 in-season, comprising the training days from match day minus (MD-) 3, MD-2, and MD-1. The weekly training load was collected by an 18 Hz global positioning system (GPS), 1 Hz heart rate (HR) monitors, the perceived exertion (RPE) and the total quality recovery (TQR). A principal component approach (PCA) coupled with a Monte Carlo parallel analysis was applied to the training datasets. The training datasets were condensed into three to five principal components, explaining between 37.0% and 83.5% of the explained variance (proportion and cumulative) according to the training day (p < 0.001). Notably, the eigenvalue for this study ranged from 1.20% to 5.21% within the overall training data. The PCA analysis of the standard microcycle in youth sub-elite football identified that, across MD-3, MD-2, and MD-1, the first was dominated by the covered distances and sprinting variables, while the second component focused on HR measures and training impulse (TRIMP). For the weekly microcycle, the first component continued to emphasize distance and intensity variables, with the ACC and DEC being particularly influential, whereas the second and subsequent components included HR measures and perceived exertion. On the three training days analyzed, the first component primarily consisted of variables related to the distance covered, running speed, high metabolic load, sprinting, dynamic stress load, accelerations, and decelerations. The high intensity demands have a high relative weight throughout the standard microcycle, which means that the training load needs to be carefully monitored and managed.

Multipath mitigation for GPS/Galileo/BDS-3 precise point positioning with overlap-frequency signals

The multipath effect is a major Global Navigation Satellite System (GNSS) error source due to its environment-dependent characteristic, which complicates its mitigation process for the high-rate determination of displacements. For instance, Sidereal Filtering (SF) and Multipath Hemispherical Map (MHM) require the observations spanning at least one full cycle of satellite orbit repeat period (e.g., ten days for Galileo navigation satellite system (Galileo) to reproduce the satellite geometry against ground stations. As a consequence, the practicability of SF and MHM is limited due to potential station-surrounding changes over a long period. In this study, we used the overlap-frequency signals on Global Positioning System (GPS) L1/L5, Galileo E1/E5a, and BeiDou-3 Navigation Satellite System (BDS-3) B1C/B2a to construct an interoperable MHM (i.e., MHM_GEC) across constellations to mitigate multipath more efficiently. We thus used 31 days of 1-Hz GPS/Galileo/BDS-3 data at 21 stations in Europe to compare this overlap-frequency MHM with those GNSS-specific MHMs (i.e., MHM_G for GPS, MHM_E for Galileo, and MHM_C for BDS-3), as well as SF. It is confirmed that the multipath effects on overlap-frequency signals are of a high spatial consistency across all GNSS. The mean reduction rate of applying MHM_GEC to GPS, Galileo, and BDS-3 carrier-phase residuals is 25%, 31%, and 28.5%, respectively, which are up to 25 percentage points higher than those of MHM_G, MHM_E, and MHM _C. Furthermore, the MHM_GEC constructed using 5 to 6 days of data can improve the positioning precision by 40%, outperforming the MHM_E, MHM_C, and SF using 10 days of data. Therefore, the interoperable MHM_GEC is more efficient in mitigating multipath effects for high-precision GNSS positioning.

Comprehensive geospatial mapping and monitoring of an eastern coalfield in India

Geospatial mapping and monitoring are crucial for designing slopes in opencast mining, as failure can result in significant economic and life loss. To overcome these issues, the present investigation evaluated the Digital Elevation Model (DEM) generated using high resolution Cartosat-1 satellite imagery. Simultaneously, mapping of shaded relief map, aspect map and slope maps was done for the monitoring of Shatabdi opencast coal mine of Jharia region, an eastern coalfield region of India. Shaded relief map was categorized in to low, moderate and high categories and maximum area was covered by moderate to high shades. The constructed aspect map encompasses the range between 0 and 360 degrees of direction, with categorization and representation through distinct colors. The slope map was classified into various categories based on degrees of slope, including very gentle, gentle, moderate, moderately steep, steep, and very steep slopes. The maps were ground validated using Differential Global Positioning System (DGPS) collected data points by field visit in the region. For opencast mines to operate safely and profitably, slope stability in different stages of mining must be mapped and monitored. The study synthesizes data from diverse sources, highlighting the role of geospatial technology in addressing multiple research gaps within the mining industries prior to leveraging Cartosat-1 satellite data. Slope mapping and monitoring of coalfield regions are crucial to reducing construction costs, mitigation of natural hazard risks like flooding and landslides, and effective conservation of natural resources such as soils, vegetation, and water systems. The present study benefits policymakers, environmental planners and government in formulating policies to ensure safety, reliability, and enhance the economic growth of workers and the country.

Abnormal Pavement Condition Detection with Vehicle Posture Data Considering Speed Variations.

Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors.

Observing and Modeling Short‐Term Changes in Basal Friction During Rain‐Induced Speed‐Ups on an Alpine Glacier

AbstractBasal shear stress on hard‐bedded glaciers results from normal stress against bed roughness, which depends on basal water pressure and cavity size. These quantities are related in a steady state but are expected to behave differently under rapid changes in water input, which may lead to a transient frictional response not captured by existing friction laws. Here, we investigate transient friction using Global Positioning System vertical displacement and horizontal velocity observations, basal water pressure measurements, and cavitation model predictions during rain‐induced speed‐up events at Glacier d'Argentière, French Alps. We observe up to a threefold increase in horizontal surface velocity, spatially migrating at rates consistent with subglacial flow drainage, and associated with surface uplift and increased water pressure. We show that frictional changes are mainly driven by changes in water pressure at nearly constant cavity size. We propose a generalized friction law capable of capturing observations in both the transient and steady‐state regimes.

The Concurrent Validity and Reliability of a Global Positioning System for Measuring Maximum Sprinting Speed and Split Times of Linear and Curvilinear Sprint Tests

This study investigated the reliability and validity of linear sprint tests (LS) and curvilinear sprint tests (CS) using a GPS device (GPEXE lt). Twenty-one (21) student athletes completed a 40 m LS as well as a left and right CS. Maximum sprint speed (MSS) and split times at short (0–10 m), intermediate (0–20 m), and long (0–30 m) distances were determined using the GPEXE lt and single-beam timing gates (TG). Intrasession reliability and concurrent validity of GPEXE lt were assessed. The GPEXE lt consistently showed high reliability for MSS (ICC ≥ 0.95; CV ≤ 1.8%), despite underestimation compared to TG (bias = −2.57 to −0.95%; ICC ≥ 0.87). Acceptable reliability was observed for CS and LS intermediate and long distance split times (ICC ≥ 0.91; CV ≤ 2.1%), with lower reliability for short distances (ICC ≥ 0.70; CV ≤ 3.6%). The GPEXE lt split times for CS and LS showed good agreement with TG (ICC ≥ 0.66), but were overestimated at long distances (5.5–9.7%) and short distances (11.1–14.6%). Although the MSS was found to be the most reliable and valid variable to assess LS and CS speed performance with GPEXE lt, caution is needed due to limited validity.

Assessing Variations in Positional Performance across Age Groups and during Matches in Youth Association Football Competitions.

This study aimed to explore how positional performance varies across different youth age groups and during matches in football competitions. The study encompassed 160 male outfield youth football players (n = 80, under-13, U13; n = 80, under-15, U15) who belonged to the starting line-up and played the entire first half of each match. The players' positional data were gathered through the global positional system for each of the eight matches performed by each age group. The frequency of near-in-phase synchronization based on speed displacements, spatial exploration index, and the distance to the nearest teammate and opponent were used as variables. Additionally, each match half was segmented into three equal parts to assess changes over time and used as a period factor along with age group. The results indicated that U13 players showed a significant decrease (from small to large ES) in synchronization speed and spatial exploration index throughout the first half of the match, along with a decrease in the distance to the nearest opponent. In contrast, U15 players exhibited most changes during the third segment of the half, with a decrease in speed synchronization and spatial exploration, but an increase in the distance and regularity to the nearest opponent. Comparing both age groups revealed significant differences in speed synchronization across the entire half of the match and within each segmented period (from small to large ES), with U13 consistently showing higher values. The study highlights that long durations in 11 vs. 11 matches might not provide an appropriate learning environment in the U13 age group. Conversely, the U15 group displayed better capacity for tactical adjustments over time, suggesting a higher level of tactical maturity. Overall, these findings emphasize the importance of adapting youth football training and competition structures to the developmental needs and capabilities of different age groups to optimize learning and performance outcomes.

Weekly External Load Correlation in Season Microcycles with Game Running Performance and Training Quantification in Elite Young Soccer Players.

The purpose of this study was to (a) correlate the weekly external training load with the game running performance in season microcycles and (b) specify the optimal training/game ratio of the weekly external load in elite youth soccer players. The total distance (TD), the high-speed running distance (HSRD) (19.8-25.2 km/h), the ZONE6 distance (>25.2 km/h), the acceleration (ACC) (≥+2 m/s2), and the deceleration (DEC) (≥-2 m/s2) were monitored with global positioning system (GPS) technology throughout 18 microcycles and official games. TD had a very high positive correlation average (r = 0.820, p = 0.001), the HSRD had a high positive correlation average (r = 0.658, p = 0.001), the ZONE6 distance and DEC had a moderate positive correlation average ((r = 0.473, p = 0.001) and (r = 0.478, p = 0.001), respectively), and the ACC had a low positive correlation average (r = 0.364, p = 0.001) between microcycles and games. Regarding the training/game ratio, the HSRD showed statistically significant differences between ratios 1.43 and 2.60 (p = 0.012, p ≤ 0.05), the ACC between ratios 2.42 and 4.45 (p = 0.050, p ≤ 0.05) and ratios 3.29 and 4.45 (p = 0.046, p ≤ 0.05), and the DEC between ratios 2.28 and 3.94 (p = 0.034, p ≤ 0.05). Considering the correlation between weekly training and game external load, high weekly training TD values correspond to higher game values, whereas HSRD, ZONE6 distance, ACC, and DEC, which determine training intensity, should be trained in a specific volume. Training/game ratios of 1.43, 2.42 to 3.29, and 2.28 to 3.11 seem to be optimal for HSRD, ACC, and DEC weekly training, respectively.

The Influence of the Playing Surface on Workload Response in Spanish Professional Male Soccer Players.

This study aimed to quantify the influence of the playing surface on workload-related variables (i.e., external load, Rate of perceived exertion (RPE), and mental load) in training sessions with a Spanish professional soccer team. Twenty professional male players from the same soccer team were involved. A total of thirty training sessions related to the preseason period were included. All the players completed training sessions on three playing surfaces: natural turf of poor quality, natural turf of high quality, and third-generation artificial turf. Monitoring during sessions involved assessing internal load (i.e., RPE and mental load) via self-reported questionnaires, and external load using Global Positioning System devices. Linear mixed models showed that RPE was significantly higher on natural turf of high quality than on natural turf of poor quality (p < 0.001). Total distance, relative total distance, the number of accelerations, decelerations, and high metabolic load distance were significantly lower on third-generation artificial turf compared to natural turf of poor quality (p < 0.001) and high quality (p < 0.001). In addition, high-speed running, sprint running distances, and the number of sprints reached higher values on third-generation artificial turf compared to the other two playing surfaces. These findings highlight the need for coaches to consider the type of training surface in soccer to optimize training load planning and prevent injuries.

In Pursuit of Justice: The Evolution of Social Work in Criminal Supervision

Social work punishment is a form of renewal of basic criminal punishment in Indonesia. Therefore, it is important to regulate the mechanism of social punishment supervision so that the implementation of social work punishment is in accordance with the objectives of punishment in Indonesia, one of which is to create a sense of security in the community. As a form of legal novelty, currently Indonesia does not have legal regulations governing the mechanism of supervision of social work punishment. The policy of social work criminal supervision greatly affects the legal framework in realizing justice. The purpose of this research is to explain the importance of legal reform in the supervision of criminal social work and provide ideas on the ideal model for criminal social work supervision in Indonesia. The method used in this research is normative juridical, by examining the application of rules or norms in positive law with a statutory approach, historical approach, and conceptual approach. The result of this research is that legal reform is needed so that Indonesia has rules governing the implementation of social work punishment in detail so that it can create justice and benefit in society. The ideal model of supervision of social work punishment in ius constituendum criminal law reform, namely by formulating a direct supervision model and using a global positioning system (GPS) by Community Supervisors and coordinating with the Prosecutor and the agency where the convict works.

Patterns of island fox habitat use in sand dune habitat on San Clemente Island

On San Clemente Island (SCI), the island fox subspecies Urocyon littoralis clementae has been monitored annually since 1988 to track long‐term population trends. Annual density estimates in most habitat types across the island range from 2 to 13 foxes/km2, yet unusually high estimates have repeatedly approached 50 foxes/km2 in a unique sand dune habitat area. Although sand dune habitat is restricted to one small area on the island, these estimates suggest sand dune habitat supports one of the highest population densities of any fox species in the world, and it may support &gt; 5% of the SCI fox population. This finding prompted our investigation to determine if SCI foxes captured in the sand dunes habitat area maintained home ranges within this habitat type. Between January and July 2018, we used global positioning system (GPS) collars to track the movements of 12 island foxes captured in the sand dune habitat area. Contrary to our initial predictions, we found that island foxes captured in the sand dune habitat area do maintain home ranges and core areas centralized in sand dune habitat. All 12 island fox home ranges estimated contained &gt; 50% sand dune habitat in either their 50% or 95% fixed kernel density estimate (KDE) home range, and island foxes were 3.14 times more likely to use active sand dune habitat when compared to the second most abundant habitat type, maritime desert scrub (Adjusted β = 3.14, 95% CI = 3.07–3.12). We also found that island foxes in sand dune habitat maintained much smaller home ranges than reported estimates in other habitat types, with an average 95% KDE home range size of 0.42 km2 (95% CI = 0.20–0.63 km2). Although sand dune habitat comprises just 2% of available habitat on SCI, our research highlights the importance of this unique habitat area for island foxes.

A machine learning-based overlay technique for improving the mechanism of road traffic prediction using global positioning system

A machine learning-based overlay technique for improving the mechanism of road traffic prediction using global positioning system

Influence of ACTN3 R577X Polymorphism on Blood Creatine Kinase Levels Relative to Number of Sprints in Brazilian Professional Soccer Players.

This study sought to assess how post-game creatine kinase (CK) levels correlate with the number of sprints and the impact of the ACTN3 polymorphism on this response. This research constituted a descriptive/observational, retrospective cross-sectional study. DNA was extracted from blood samples for ACTN3 polymorphism genotyping. CK was measured 48 h after official matches, and the number of sprints (>19 km/h) was tracked using Global Positioning System (GPS) technology. The main cohort included 23 professional soccer players from the top tier of the Brazilian Championship. We analyzed 115 GPS + CK data sets. The replication cohort comprised 18 professional soccer players from the First Division of the Championship, had the same methodology applied, and featured a total of 90 GPS (sprints > 25.2 km/h) + CK data sets. For the main cohort, a significant positive correlation was seen between the number of sprints and the CK levels (p = 0.009). Athletes with the ACTN3 RR genotype had higher CK levels as more sprints were performed during the match (p = 0.017). However, the relationship was not found for X allele carriers (p > 0.05). For the replication cohort, there was a near-significant correlation between CK levels and the number of sprints (p = 0.05), and RR individuals showed a significant association (p = 0.01), whereas X allele carriers did not (p = 0.06). A greater number of sprints during matches is linked to higher CK levels, primarily among players with the ACTN3 RR genotype, which is potentially due to an increased presence of type II muscle fibers. These findings were replicated for both cohorts of elite Brazilian soccer players, emphasizing the importance of genetic factors in injury prevention.

Multiple integer candidates ambiguity resolution: a unification ambiguity resolution algorithm

Among all the ambiguity resolution techniques, the Full Ambiguity Resolution (FAR), Partial Ambiguity Resolution (PAR) and Best Integer Equivariant (BIE) estimator are widely used. Although the researches have been done on the different classes of ambiguity resolution, we still hope to find the relationships among these specific algorithms. In this work, we unify the PAR and FAR algorithms under a whole framework of BIE by applying multiple integer candidates. A concise estimation formula of the variance of Gaussian BIE estimator based on the variance of float solution and the probability distribution of the candidates is first derived. Then, we propose an algorithm named Multiple Integer Candidates Ambiguity Resolution (MICAR) to discover as many ambiguities in the BIE as possible that can be estimated more precisely by PAR (FAR) algorithm instead of BIE. In the experiments, we utilize the simulated data of GPS (Global Positioning System) + BDS (BeiDou Navigation Satellite System) + Galileo (Galileo navigation satellite system) to contrast the effects of MICAR and single candidate estimator, i.e., FAR. By taking the threshold of 5 cm at 95% confidence level as an example, MICAR accelerates the convergence process by about 3.0 min. When the positioning sequence converges, MICAR reduces the root mean square of the positioning error by 9.8% in horizontal directions and 3.5% in vertical direction, which is attributed to more fixed NL.

Fault system dynamics of the Kashmir, NW Himalaya, India using continuous GPS observations and geomorphic evidences

We collected data from the continuous Global Positioning System (cGPS) sites across the Kashmir Valley, situated at latitude 34◦N, spanning from 2008 to 2021. Inter-site velocities define a region of approximately 15,000 km2 with broadly distributed strain accumulation at −7.22×10−8 nano strain/year (compression component) and the maximum shear strain γmax of 1.9051×10−7 nano strain/year. The estimated site velocity in the ITRF14 ranges between 30.5±1–42.85±3 mm/yr. It was observed that the average deformation rate of the GPS sites in the Kashmir region ranges between 2.86±1–15.47±3 mm/yr relative to the India fixed reference frame, suggesting a predominant N-S directed compressional tectonic regime. The focal mechanism solutions of the earthquakes in and around the Kashmir Valley suggest dominant thrust faulting followed by normal faulting. Analysis of the vertical component of the GPS time series shows that the northwest segment of the valley subsides at the rate of −1.71± 0.70 mm/yr, while the southeast segment uplifts at the rate of 5.4 ± 0.5 mm/yr. In addition to vertical component, we observed differential movement of the sites relative to IISC site on the northwest and southeast segments. The rate of baseline change of the GPS sites indicates 7.30 ± 0.75 mm/yr extension in SE-NW direction and −5.32 ± 0.75 mm/yr NE-SW compression across and along the Kashmir Valley. Geodetic observations reveal a transition that aligns with the Magam lineament/fault previously identified by Ganju and Khar (1984) using gravity and magnetic data. The observation was supported by the field investigations and remote sensing techniques, confirming the existence of Magam Fault. During the field investigations, various geomorphic expressions of fault were observed, including fault ruptures, fault scarps, offset ridges, deflected drainages/rivers, linear alignment of springs, linear drainage lines, triangular facets and offset Recent sedimentary deposits (Karewas) were observed. The field evidence suggests exposure of normal faults at Kondabal, Nasrullapora, Biru and Radbugh. These exposed extensional structures, trends in NE-SW direction and dip in NW direction with varying offset and dip amount. GPS observations supplemented by geomorphic evidences infer the presence of normal fault ̴ 80 Km extending from northeast to southwest.

A dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR

Due to the influence of environmental factors (i.e., terrain and surface coverage) around the GPS receivers, the snow depth retrieval results obtained by the existing global positioning system interferometric reflection (GPS-IR) method show significant variability. The resulting loss of reliability and accuracy limits the broad application of this technology. Therefore, this paper proposes a dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR to fully leverage multi-source satellite observation data for automatic and high-precision snow depth retrieval. The model employs Dynamic Time Warping distance measurement combined with the K-Medoids clustering algorithm to categorize frequency sequences obtained from various satellite trajectories, facilitating effective integration of multi-constellation data and acquisition of optimal datasets. Additionally, Long Short-Term Memory networks are integrated to capture and process the long-term dependencies in snow depth data, enhancing the model’s adaptability in handling time-series data. Validated against SNOTEL measured data and standard machine learning algorithms (such as BP Neural Networks, RBF, and SVM), the model’s retrieval capability is confirmed. For P351 and AB39 sites, the correlation coefficients for L1 band data retrieval were both 0.996, with RMSEs of 0.051 and 0.018 m, respectively. The experiment results show that the proposed model demonstrates superior precision and robustness in snow depth retrieval compared to the previous method. Then, we analyze the accuracy loss caused by sudden snowfall events. The proposed model and methodology offer new insights into the in-depth study of snow depth monitoring.

Improving the performance of GPS/INS integration during GPS outage with incremental regularized LSTM learning

Global Positioning System (GPS)/Inertial Navigation System (INS) integration is a widely used technique for navigation and positioning applications. It combines the advantages of GPS and INS to provide accurate and reliable information. However, the GPS/INS integration suffers from performance degradation during a GPS outage, which occurs when natural or artificial factors block the GPS signal. The novelty of this paper is improving GPS/INS integration performance during GPS outages using Incremental Regularized LSTM (IncRLSTM) learning. Incremental learning is a learning paradigm that can be learned from streaming data online and updating the model parameters without forgetting the previous ones. Also, regularization is a technique that prevents overfitting and improves the generalization of the network by adding some constraints or penalties to the model. IncRLSTM learning models the GPS signal as a multi-objective regression and corrects the INS output with the Faded Memory Kalman filter. The results on real-world datasets significantly show the reduction of positioning errors by an average of 72 % during GPS outages and the improvement of the accuracy and robustness of GPS/INS integration by an average of 58 % compared with existing methods. Moreover, IncRLSTM presents, on average, a 10 % improvement compared to the existing methods.

Inequalities in geographical distribution of heritage in Scotland, investigating spatial exposure to heritage sites through area-based and individual-based (GPS) measurement

Growing evidence suggests that visiting heritage, such as historic buildings, or landscapes, may enhance mental health. However, adults and children from deprived areas are less likely to visit heritage, perhaps partly due to unequal geographical availability. Few studies investigate socio-spatial inequity of heritage access nationally, and there is an absence of research exploring individual-based contact with heritage. We study both and ours is the first to use Global Positioning System (GPS) data to examine the latter. For Scotland we quantify area-level ‘opportunities’ for heritage contact (i.e., data zone-level counts of sites), and SPACES Study children's (n = 688) individual-level heritage ‘encounters’ (i.e., GPS buffer-level counts of sites), by income deprivation. Heritage exposure varied by deprivation at area- (ANOVA p < 0.001) and individual-level (p = 0.003); in poorer areas there were fewer ‘opportunities’ to visit heritage (mean numbers in most deprived: 2.8 (CI:2.2–3.5), and least deprived: 11.7 (CI:10.3–13.1)) and children had fewer ‘encounters’ with heritage (most deprived: 32.6 (CI:25.3–40.2), and least deprived: 58.0 (CI:47.9–69.3)). Inequalities at individual-level were smaller than at area-level; children in poorer areas appeared to compensate for fewer sites near home during their daily movements. Nonetheless, inequalities in exposure did persist, and opportunities to access heritage remained unequal to the detriment of those in poorer areas. Our evidence is policy relevant, highlighting a need for place-based schemes to address geographic inequalities in access. For example, targeted investment in heritage in/near deprived areas; free site entry for those on lower incomes; and educational outreach activities to improve awareness of local heritage.

Match-to-Match Variation on High-Intensity Demands in a Portuguese Professional Football Team.

The aim of this study was to analyze the match-to-match variation in high-intensity demands from one Portuguese professional football team according to playing positions. Twenty-three male outfield professional football players were observed during eighteen matches of the Portuguese Second League. Time-motion data were collected using Global Positioning System (GPS) technology. Match running performance was analyzed based on the following three playing positions: defenders (DF), midfielders (MF), and forwards (FW). Repeated measures ANOVA was utilized to compare match running performance within each position role, and seasonal running variation. Practical differences were assessed using the smallest worthwhile change (SWC), coefficient of variation (CV), and twice the coefficient of variation (2CV). Significant differences were found among playing positions in total distance covered (F = 15.45, p < 0.001, η2 = 0.33), average speed (F = 12.79, p < 0.001, η2 = 0.29), high-speed running (F = 16.93, p < 0.001, η2 = 0.36), sprinting (F = 13.49, p < 0.001, η2 = 0.31), accelerations (F = 4.69, p = 0.001, η2 = 0.132), and decelerations (F = 12.21, p < 0.001, η2 = 0.284). The match-to-match running performance encompassed TD (6.59%), AvS (8.67%), HSRr (37.83%), SPR (34.82%), ACC (26.92%), and DEC (27.85%). CV values for total distance covered ranged from 4.87-6.82%, with forwards and midfielders exhibiting the greatest and smallest variation, respectively. Midfielders demonstrated the highest match-to-match variation for all other analyzed variables (8.12-69.17%). All playing positions showed significant variation in high-demanding variables (26.94-37.83%). This study presents the initial analysis of match-to-match variation in high-intensity demands within a Portuguese professional football team. Thus, the position's specificity and context can provide a helpful strategy for evaluating match-to-match running performance, and for recommending individualized training exercises based on the peak and high-intensity demands for each player's role within the game.