Global Positioning System Research Articles

Variation in Accelerometer-Derived Instantaneous Acceleration Distribution Curves of Elite Male Soccer Players According to Playing Position: A Pilot Study.

The incorporation of triaxial accelerometers into Global Positioning Systems (GPS) has significantly advanced our understanding of accelerations in sports. However, inter-positional differences are unknown. This study aimed to explore the variability of acceleration and deceleration (Acc) distribution curves according to players' positions during soccer matches. Thirty-seven male players from a national-level Portuguese club were monitored using 10 Hz GPS with an embedded accelerometer during the 2021/2022 season. Resultant Acc was obtained from the x (lateral), y (frontal/back), and z (vertical) axes and expressed in gravitational units (g). Statistical Parametric Mapping was employed to compare playing positions: central defenders (CD), fullbacks (FB), central midfielders (CM), wide midfielders (WM), and strikers (ST). All positions exhibited a decreasing Acc distribution curve, very similar in shape, with a high frequency of events in the lower ranges (i.e., 0 to 1 g) and a lower frequency of events in the higher values (2 to 10 g). Post hoc comparisons revealed significant differences between all positions, except between FB and WM. Out of 1000 points in the curve, CD had 540, 535, 414, and 264 different points compared to FB, CM, WM, and ST, respectively. These findings indicate that players in different positions face distinct demands during matches, emphasizing the need for position-specific Acc analysis and training programming. By analyzing Acc as a continuous variable, this study highlights the importance of individualized monitoring to ensure the comprehensive and precise tracking of all player activities, without overlooking or omitting critical information.

SSRL-UAVs: A Self-Supervised Deep Representation Learning Approach for GPS Spoofing Attack Detection in Small Unmanned Aerial Vehicles

Self-Supervised Representation Learning (SSRL) has become a potent strategy for addressing the growing threat of Global Positioning System (GPS) spoofing to small Unmanned Aerial Vehicles (UAVs) by capturing more abstract and high-level contributing features. This study focuses on enhancing attack detection capabilities by incorporating SSRL techniques. An innovative hybrid architecture integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to detect attacks on small UAVs alongside two additional architectures, LSTM-Recurrent Neural Network (RNN) and Deep Neural Network (DNN), for detecting GPS spoofing attacks. The proposed model leverages SSRL, autonomously extracting meaningful features without the need for many labelled instances. Key configurations include LSTM-GRU, with 64 neurons in the input and concatenate layers and 32 neurons in the second layer. Ablation analysis explores various parameter settings, with the model achieving an impressive 99.9% accuracy after 10 epoch iterations, effectively countering GPS spoofing attacks. To further enhance this approach, transfer learning techniques are also incorporated, which help to improve the adaptability and generalisation of the SSRL model. By saving and applying pre-trained weights to a new dataset, we leverage prior knowledge to improve performance. This integration of SSRL and transfer learning yields a validation accuracy of 79.0%, demonstrating enhanced generalisation to new data and reduced training time. The combined approach underscores the robustness and efficiency of GPS spoofing detection in UAVs.

Building a General Algorithm for Seismic Hazard Analysis in the Sunda Arc through Geodynamic Simulations

Abstract Because of its well-documented subduction zone and outer island arc, Sumatra provides a unique setting for studying and forecasting earthquakes within the seismically active Sunda Arc. This study builds on previous research that utilized Global Positioning System data and the Akaike information criterion to analyze probabilistic seismic hazard functions. However, this study replaces surface displacement rate data with a forward model derived from previous fault modeling results to create a more broadly applicable earthquake forecasting algorithm. Although the best-fit model patterns generated by this new algorithm are consistent with past studies, the forward model demonstrates a lower degree of fit compared to models utilizing natural surface displacement data. This discrepancy highlights the need to refine the fault parameter models to estimate surface displacement rates. Despite this limitation, the study makes a valuable contribution by developing a general algorithm applicable to other subduction zones within the Sunda Arc region. With further refinement and incorporation of more accurate fault modeling and data, this algorithm has the potential to formulate the best-fit earthquake spatial forecast models. This approach could be applied to other seismically active areas, particularly those near subduction zones.

Developing an internet of things system for hydrogen leak detection at hydrogen refueling stations integrating LoRa and global positioning system

Developing a hydrogen leak detection system that can quickly detect even the smallest amount of hydrogen is extremely crucial in the hydrogen industry. Currently, hydrogen refueling stations use a fixed hydrogen leak detection system. This study develops a mobile hydrogen gas leak detection system for hydrogen refueling stations using s, global positioning system, and Internet of Things (IoT) equipment, including LoRa and Wi-Fi. A digital twin is implemented by linking hydrogen gas-sensing data at a location that is quickly required or suspected using Google Maps. A time-series database is used to store the hydrogen gas-sensing data, and the transfer delay of hydrogen gas-sensing data between the hydrogen equipment was measured. Therefore, we developed a prototype system that transmits, stores, and analyzes hydrogen gas-sensing data in real time using IoT equipment for hydrogen refueling stations.

An Automatic Vehicle Navigation System Based on Filters Integrating Inertial Navigation and Global Positioning Systems

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Orienteering Is More than Just Running! Acute Effect of Competitive Pressure on Autonomic Cardiac Activity among Elite Orienteering Athletes.

Background and Objectives: Orienteering is a sport characterized by high physical exertion and intense mental demands, which increase susceptibility to errors. Understanding the impact of such errors on psychophysiological responses, particularly on heart rate variability (HRV), is essential. This study aimed to investigate the relationship between psychophysiological indicators and checkpoint errors made by elite orienteers during official competition. Materials and Methods: Fifty-three orienteers participated in this study, and their performance was continuously monitored and recorded by using a global positioning system (GPS) and HRV data. Errors made during the orienteering events were identified and analyzed. HRV data were examined in three temporal segments: before, during, and after the identified and standardized errors. Results: The analyses indicated that errors significantly impacted HRV indices across multiple domains: the time domain, frequency domain, and nonlinear domain. Additionally, a significant effect of sex on the normalized the root mean square of successive differences (r-MSSD) before and after the error was observed. Conclusions: The findings of this study underscore the significant impact of errors made by orienteers on cardiovascular responses, as evidenced by measurable alterations in HRV metrics. Cardiovascular activity, represented by the HRV, can provide useful information for coaches and sport psychologists to adopt effective training programs for athletes.

Calibration Methods for Large-Scale and High-Precision Globalization of Local Point Cloud Data Based on iGPS.

The point cloud is one of the measurement results of local measurement and is widely used because of its high measurement accuracy, high data density, and low environmental impact. However, since point cloud data from a single measurement are generally small in spatial extent, it is necessary to accurately globalize the local point cloud to measure large components. In this paper, the method of using an iGPS (indoor Global Positioning System) as an external measurement device to realize high-accuracy globalization of local point cloud data is proposed. Two calibration models are also discussed for different application scenarios. Verification experiments prove that the average calibration errors of these two calibration models are 0.12 mm and 0.17 mm, respectively. The proposed method can maintain calibration precision in a large spatial range (about 10 m × 10 m × 5 m), which is of high value for engineering applications.

An open-source, QGIS-based solution for digital geological mapping: GEOL-QMAPS

Digital geological mapping has experienced significant growth over the past three decades due to the advent of commercial geographical information systems, advances in global positioning systems, and the availability of portable hand-held devices, such as mobile personal computers (PCs), smartphones, and tablets. Numerous software packages have been developed to collect, combine, organise, visualise, publish, and share field data with enhanced spatial accuracy and minimal post-field processing. However, many of these tools are not open-source or are not made available to the geoscientific community, remaining specific to given mapping projects or organisations.In this contribution we introduce GEOL-QMAPS, an open-source, QGIS-based solution promoting digital geological mapping in a harmonised, comprehensive and flexible way. It can be used in the field with a tablet PC or via the QGIS-based QField app on iOS or Android mobile devices, enabling synchronisation with desktop QGIS and the creation of field databases. Designed as a general solution, the GEOL-QMAPS solution consists of a QGIS field data entry template and a custom QGIS plugin, both available on free-access online repositories. The plugin allows for the adaptation of dictionaries (i.e., lists of attributes describing geological features), initially set to international nomenclatures, to the guidelines of different mapping projects. The solution also facilitates the loading and consultation of relevant legacy geodatasets (e.g., preexisting field data, geochemical, geophysical maps or punctual datasets). A fact map, created from field data collected across the Archean Sula-Kangari greenstone belt in Sierra Leone, demonstrates the solution's advantages in terms of post-field processing and raw field data sharing.

Use and application of wearable technology in football further education settings in the UK

ABSTRACT The increase in the number of football-specific industry-related qualifications offered by the post-16 education sector has grown exponentially in recent years. Due to technological advancements and access to enhanced equipment, these courses tend to include wearable technology in their curricular to support student cohorts understanding of specific internal and external load patterns. Despite these technological and education developments, there is a dearth of pedagogic research on the application of various wearable technology in post-16 football education. Using a mixed-method research design, we established the type of technology, and how it is being employed across a range of post-16 educational settings. Survey responses suggested that Global Positioning System (GPS) vests and heart rate chest strap were the most prominent wearable technology. Qualitative findings suggested that there are pedagogic challenges and barriers to using this kind of technology, a lack of understanding, and poor feedback and communication strategies. Having established some preliminary findings, a series of 1–1 interviews with identified practitioners working in post-16 educational settings explored these barriers and challenges within contextualised settings in more depth. The results identified disconnect between coaching performance and coaching education, suggesting a lack of knowledge surrounding the uses and capabilities of wearable technology used in football-related FE settings. These findings can help sport, academia and the technology industry to better align and understand in order to meet the educational needs of students seeking to enter a career in sports such as football that use wearable technology.

Role of artificial intelligence in transmission line protection: A review of three decades of research

The optimal functioning of the power system is crucially dependent upon the sound protection of its major stakeholder, i.e., the transmission line, as it is prone to fault. To maintain the integrity of the power system and protect costly power system equipment, protective relaying is necessary to provide a steady and affordable supply of electricity. Relays recognize, classify, and identify transmission line faults using input signals of voltage and current. Many artificial intelligent methods based on Expert Systems, Artificial Neural Networks, Fuzzy Logic, Support Vector Machines, Wavelet-based systems, and deep learning techniques are being investigated to improve modern digital relays’ consistency, speed, and accuracy. This paper is a comprehensive and all-inclusive survey that reviews and incorporates Phasor Measurement Unit (PMU) and Global Positioning System (GPS) approaches together with all of these intelligent transmission line safety strategies and concepts. Initial investigators will benefit from this study by being able to examine, evaluate, and analyze a variety of approaches with references for all relevant contributions.

Early postseismic slip of the 21 November 2022 Mw 5.6 Cianjur, Indonesia, earthquake based on GPS measurements

ABSTRACT Global Positioning System (GPS) data after the 21 November 2022 Mw 5.6 Cianjur earthquake are crucial for understanding the physical processes during the postseismic phase. New continuous GPS stations were installed and recorded data for periods ranging from five to twenty-five days after the mainshock. The daily time series data at GPS stations shows an evolving logarithmic decay over time, as expected due to frictional afterslip. We inverted the postseismic displacement data to solve the postseismic slip distribution, utilising Akaike’s Bayesian information criterion. A cumulative aseismic slip moment of 1.54 × 1016 N·m, equivalent to Mw 4.7, is modelled during the period from five to twenty-five days after the mainshock. This aseismic slip moment is equivalent to approximately 7% of the seismic moment during the mainshock. Our analysis on the newly identified active fault using GPS displacements highlights the imperative need to identify active faults prior to future earthquake occurrences, especially when conducting hazard mitigation in populated regions in Indonesia.

GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.

Impact of Texting-Induced Distraction on Driving Behavior Based on Field Operation Tests

Driver distraction has proven to be among the top contributory factors in road crashes worldwide. The involvement of drivers in secondary activities, such as phone usage, conversation with co-passengers, reaching for objects, eating, and so forth, have been shown to be significant causes of in-vehicle driver distraction. Text messaging using mobile phones while driving can be detrimental to the primary driving task as it invokes manual, visual, and cognitive distractions. Numerous studies in the past have analyzed the effect of texting on driver distraction using driving simulators. This study followed a field operation test–based approach to simulate real road conditions and evaluate the impact of texting on driving behavior. The study was conducted at the connected autonomous vehicle testbed at the Indian Institute of Technology Hyderabad, using an instrumented test vehicle. Data on gaze parameters and vehicle kinematics were recorded using eye-tracker glasses and a high-end global positioning system (GPS) data logger. Thirty-one participants drove on the pre-defined path at the testbed under baseline and texting conditions. The findings demonstrated that the texting task significantly affected fixation count, mean speed, and lateral acceleration. The texting task led to a reduction in mean fixation counts, mean speed, and mean lateral acceleration by 44.37%, 34%, and 46.72%, respectively. The age and driving experience of the participants had a significant effect on their behavior. The results indicate the critical potential of texting-based driver distraction and suggest the enforcement of stringent rules for texting while driving to create a safer road environment.

Performance Analysis of Internet-based DGPS and Commercial Satellite-based Augmentation System: A Case Study in Peninsular Malaysia

Global Navigation Satellite Systems (GNSS) has become an essential component in modern times for positioning, navigation, and timing. A fast-growing economic region in Malaysia required a GNSS-based augmentation positioning and navigation service. To improve navigation solution, several augmentation techniques exist, such as a differential Global Positioning System (DGPS) assisted by reference station, or augmentation service provided by commercial communication satellite. The DGPS correction is applied through a real-time communication medium and can be received at the user side by several communication methods including internet-based, and this method is favourable for land and near-coast areas. This case study aims to investigate the reliability of internet-based DGPS and SBAS along the Peninsular Malaysia. A test was conducted and data were collected over 15 hours at a rate of 1 Hz from the available GNSS satellite using a geodetic-grade receiver mounted on a moving vehicle. The obtained results showed that DGPS and SBAS perform better than the navigation solution with an accuracy of 1.536 m and 0.955 m respectively, compared to the navigation solution with an accuracy of 3.159 m. The limitations of both augmentation techniques were also analysed and discussed in this study.

Effects of the number of ball touches among elite youth players during small-sided soccer games.

This study investigates the effects of manipulating the number of ball touches (free play vs. two touches) on the physical and technical actions of elite male youth soccer players during 5v5 + 2 goalkeepers (GKs) small-sided games (SSGs). Players played in two different SSGs: 1) free play, where the number of touches per possession was not restricted; and 2) two touches, where players were limited to a maximum of two touches per ball possession. A total of 24 male elite youth soccer players (age: 14.79±0.71 years; body mass: 56.02±1.41 kg; body height: 164±2.12 cm) participated in the study. Players' physical metric and technical performance data were captured using a global positioning system and video camera, respectively. A paired-samples t-test or Wilcoxon Signed-Rank Test was employed to examine differences in players' technical performance variables depending on SSGs' ball-touch rules. The results show significantly more unsuccessful passes (t=-3.48; P=0.04; d=1.92) when players were limited to two touches than when there were no pass limits. The physical metrics indicate that total distance covered (Z=-2.90; P=0.001; d=0.07), meters per minute (Z=-3.44; P=0.001; d=0.11), low-speed running (Z=-2.25; P=0.02; d=0.04) and high acceleration (Z=-1.90; P=0.05; d=0.28) were significantly higher when touches were unlimited than when they were not. Soccer coaches should decide the number of touches per ball possession they allow depending on their tactical and/or physical objectives in training.

COVID-19 affects match running performance in professional soccer players

Objectives: To compare the match running performance of male soccer players in the Brazilian A series before and after being infected with COVID-19. Methods: The sample consisted of 20 professional soccer players from Rio de Janeiro-based teams during the 2020 and 2021 seasons, pre- and post-COVID-19 infection. Match running performance data were collected using a Global positioning system (GPS), and the analysis includes average data across 1 to 17 games pre-infection and 1 to 13 games post-infection per player. Results: COVID-19-infection induced an unclear effect on the total distance traveled during the match [SWC-Factor = -1.9, 90 % CL (-4.7; 0.9)], the number of sprints at speeds over 24 km/h [SWC-Factor = 1.1, 90 % CL (-1.7; 3.9)], maximum speed reached [SWC-Factor = 0.1, 90 % CL (-2.6; 2.9)], the number of decelerations [SWC-Factor = -2.2, 90 % CL (-5.0; 0.6) ], explosive efforts [SWC-Factor = -2.4, 90 % CL (-5.1; 0.4) ], distance traveled at speeds between 0 and 7 km/h [SWC-Factor = 2.4, 90 % CL (-0.4; 5.2) ], and distance traveled at speeds between 7 and 14 km/h [SWC-Factor = -1.8, 90 % CL (-4.5; 1.0) ]. However there was a moderate and significant decrease in distance covered during high-intensity running, defined as speeds greater than 20 km/h [SWC-Factor = -4.2, 90 % CL (-7.0; -1.5) ], the number of accelerations greater than 3 m/s² [SWC-Factor = -3.6, 90 % CL (-6.4; -0.9) ], and distance traveled at speeds between 14 and 20 km/h [SWC-Factor = -3.5, 90 % CL (-6.2; -0.7) ]. The results also showed that COVID-19-infection led to a substantial decrease in minutes played during matches [SWC-Factor = -4.0, 90 % CL (-6.8; -1.3)], affecting the analysis of match variables' absolute values. Conclusion: The results suggest that players infected with COVID-19 experienced changes in match running performance, notably decreases in distance covered during high-intensity running and increases traveled at speeds between 0 and 7 km/h. Further analysis of other player populations (in other leagues) is necessary to verify these findings. Keywords: Coronavirus, Physiology, Task Performance and Analysis, Match running, External Load.

Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories

AbstractThe potential of passively generated big data sources in transport modelling is well‐recognised. However, assessing their accuracy and suitability for policymaking remains challenging due to the lack of ground‐truth (GT) data for validation. This study evaluates the accuracy of inferring human mobility patterns from global positioning system (GPS), call detail records (CDR), and global system for mobile communication (GSM) data. Using outputs from an agent‐based simulation platform (MATSim) as ‘synthetic GT’ (SGT), synthetic GPS, CDR, and GSM data were generated, considering their positional disturbances and conventional spatiotemporal resolutions. Mobility information, including activity location, departure time, and trajectory distance, derived from the synthetic data, was compared with SGT to evaluate the accuracy of passive trajectory data at both disaggregate and aggregate levels. The results indicated a higher accuracy of GPS data in identifying stay locations at high resolution. But, GSM data at a lower resolution effectively accounted for over 80% of the variability in stay locations. Comparisons of departure time distribution and travel distance revealed higher measurement errors in GSM and CDR data than in GPS data. The proposed simulation‐based accuracy assessment framework will aid transport planners select the most suitable data for specific analyses and understand the potential margin of error involved.

Impact of implicit vs. explicit Instructions on tactical performance in under-20 soccer players.

This study aimed to verify whether implicit and explicit informational constraints generate differences in tactical performance and behavior in U-20 national-level soccer players. Thirty-two under-20 male athletes from two clubs participated. Four 4-a-side small-sided games (SSG) protocols were used: R1 - explicit rule for high-press marking, R2 - implicit rule for high-press marking, R3 - game with both previous rules simultaneous and FR - free game, without additional rules. SSGs comprised 4 vs. 4+ goalkeepers games on a 42 m × 29 m field. Position data from 10 Hz Global Positioning System (GPS) devices were used to evaluate individual and collective tactical behavior (spatial occupation metrics) and performance (interpersonal coordination). MANOVA was used for normally distributed variables, and Friedman's with Dunn or Bonferroni post hoc was used for variables without normal distribution. For SEI, an ANOVA was used with Bonferroni post hoc. The R1 protocol showed higher SEI, length, width, and LpWratio than the FR protocol (p = 0.009). There was an effect of the different protocols in SEI values (p < 0.001). Under the explicit rule, players also showed the highest in-phase interpersonal coordination values (p < 0.001). Providing players with explicit tactical instructions improves tactical performance acutely for high-pressing defensive actions.

Smartphone region-wise image indoor localization using deep learning for indoor tourist attraction.

Smart indoor tourist attractions, such as smart museums and aquariums, require a significant investment in indoor localization devices. The use of Global Positioning Systems on smartphones is unsuitable for scenarios where dense materials such as concrete and metal blocks weaken GPS signals, which is most often the case in indoor tourist attractions. With the help of deep learning, indoor localization can be done region by region using smartphone images. This approach requires no investment in infrastructure and reduces the cost and time needed to turn museums and aquariums into smart museums or smart aquariums. In this paper, we propose using deep learning algorithms to classify locations based on smartphone camera images for indoor tourist attractions. We evaluate our proposal in a real-world scenario in Brazil. We extensively collect images from ten different smartphones to classify biome-themed fish tanks in the Pantanal Biopark, creating a new dataset of 3654 images. We tested seven state-of-the-art neural networks, three of them based on transformers. On average, we achieved a precision of about 90% and a recall and f-score of about 89%. The results show that the proposal is suitable for most indoor tourist attractions.

A hybrid genetic-fuzzy ant colony optimization algorithm for automatic K-means clustering in urban global positioning system

This paper introduces an innovative automatic K-means clustering algorithm, namely HGA-FACO, which seamlessly integrates the noise algorithm, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Adaptive Fuzzy System (AFS). The rationale behind the HGA-FACO algorithm is to mitigate the shortcomings of traditional K-means, particularly the reliance on pre-determined cluster centers and the need for specifying the number of clusters in advance. By optimizing the search strategy, HGA-FACO efficiently circumvents local optima and effectively explores the global optimal solution, resulting in more accurate and stable clustering outcomes. To validate the superiority of the HGA-FACO over conventional K-Means Clustering (KMeans) and other intelligent clustering approaches such as ACO-KMeans, GA-KMeans (GAK), particle swarm optimization KMeans (PSOK), and ACO-GAK, we conducted comprehensive experiments on taxi Global Positioning System (GPS) datasets sourced from four distinct cities. Employing rigorous evaluation metrics including Silhouette Coefficient (SC), Partition Coefficient (PBM), Davies-Bouldin Index (DBI), and Sum of Squared Errors (SSE), the experimental results convincingly demonstrate that the HGA-FACO significantly outperforms its counterparts across all metrics, highlighting its exceptional performance in clustering effectiveness and compactness. While the HGA-FACO faces challenges related to computational complexity and the necessity for initial parameter tuning, its performance limitations on small-sized or unevenly distributed datasets are acknowledged. Nevertheless, the algorithm's advancements in the field of clustering algorithms are undeniable and hold immense potential for practical applications, notably in city hotspot identification.