Location-based Services Research Articles

The Integration of IoT (Internet of Things) Sensors and Location-Based Services for Water Quality Monitoring: A Systematic Literature Review

The Integration of IoT (Internet of Things) Sensors and Location-Based Services for Water Quality Monitoring: A Systematic Literature Review

Understanding Cancer Screening Behavior in South Korea: A Biopsychosocial Approach to Regional Differences

Background/Objectives: This study aimed to examine regional cancer screening participation rates among South Korean adults aged 40 and over and to identify biological, psychological, and sociocultural factors associated with cancer screening behavior using the biopsychosocial model. Methods: This research was a secondary analysis study. Data were obtained from the 2023 Korean Community Health Survey, focusing on adults residing in cities that exhibited the highest and lowest rates of cancer screening. Differences in cancer screening rates by city were visualized using a location-based open service platform. Variables were categorized into biological, psychological, and sociocultural factors, and logistic regression analysis was conducted to ascertain the factors associated with screening participation. Results: The cancer screening rate for adults aged 40 or older in 17 metropolitan cities in Korea ranged from 64.9% to 76.0%, and the national average was 70.9%. In the city with the highest screening rate, participation was positively associated with oral health, physical activity, breakfast-eating habits, and past smoking. In the city with the lowest screening rate, higher screening participation correlated with family cohabitation and satisfaction with the social environment. Conclusions: Our results suggest that cancer screening participation rates vary across regions and that the factors associated with cancer screening participation differ between regions with the highest and lowest participation rates. These results provide evidence for targeted interventions that take into account regional factors to improve cancer screening rates in South Korea.

The Sheraton Guangzhou Nansha Hotel: An analysis of service excellence and its role in enhancing the business travel experience

Business travel has become an integral component of the global economy, driving corporate growth and fostering international collaboration. In this context, the hospitality industry plays a critical role in providing accommodations that cater to the unique needs of business travellers. This paper examines the Sheraton Guangzhou Nansha Hotel as a case study to explore how it meets the demands of corporate guests through its strategic location, state-of-the-art facilities, and exceptional service quality. Drawing on qualitative research methods, including site visits, observations, and interviews with guests, the study highlights the hotel’s ability to enhance productivity, comfort, and overall satisfaction for business travellers. Key findings reveal that the hotel excels in areas such as meeting and conference facilities, high-speed internet access, lounge services, and personalized concierge support. Additionally, the hotel’s wellness amenities and on-site dining options contribute to a holistic guest experience. A particularly noteworthy aspect of the hotel’s service is its bakery, which creates personalized baked goods inspired by guests’ cherished belongings, such as beloved dolls. This initiative exemplifies the hotel’s commitment to emotional engagement and experiential hospitality. The study underscores the importance of continuous innovation and adaptation in the hospitality industry to meet the evolving needs of business travellers. By integrating technology, prioritizing service quality, and offering personalized touches, the Sheraton Guangzhou Nansha Hotel sets a benchmark for excellence in catering to corporate guests. The findings have broader implications for the industry, emphasizing the value of creating memorable experiences that foster loyalty and satisfaction among business travellers . Keywords: Business Travel, Hospitality, Service Quality, Personalized Experiences, Sheraton Guangzhou Nansha Hotel, SERVQUAL Model, Experiential Hospitality.

Development of Mobile GIS Based Digital Map Location Marking Application for Navigation Purposes

In today's digital era, map and navigation applications have become essential tools for users to find locations, obtain the fastest routes, and explore new places. Google Maps is one of the most popular and comprehensive mapping applications that offers various features, such as navigation. However, despite being highly beneficial, Google Maps has several limitations, particularly regarding map markers, personalized, creating individual routes, and offline map usage on navigation. To address these issues, this research develops an innovative application called StellarPath. StellarPath is a mobile-based digital map location marker application that adopts Location Based Service (LBS) methods for more effective navigation. This application focuses on offline usability as a digital map marker and includes features such as offline maps stored indefinitely, offline navigation, manual route creation, and higher personalization, allowing users to save and manage markers with additional information offline. Test results show a success rate of 92.5%, indicating the application's effectiveness. The conclusion drawn is that the application can download and store maps offline, although it has shortcomings in managing downloaded maps. The offline navigation feature allows users to draw, save, and manage manual routes on the map. Users can create and save location markers with additional information offline. Navigation is flexible, with the option to start navigation by tapping on the map and features that can be enabled or disabled. This research aims to provide a more flexible and personalized navigation solution compared to existing standard map and navigation applications, such as Google Maps.

Spatial distribution characteristics and influencing factors of milk tea stores in Wuhan based on sDNA and OPGD models.

Milk tea stores have rapidly expanded in Wuhan due to residents' increased consumption demand. Therefore, studying the spatial distribution and influencing factors of stores is important for optimizing their layout and promoting economic development. Using milk-tea store data from Amap City's Point of Interest function and road network data from Baidu HeatMap, we analyzed the spatial distribution characteristics of stores within the third ring road of Wuhan City using ArcGIS. We then examined the influencing factors by combining spatial design network analysis, optimal parameters-based geographical detection, and location-based service big data. Our results revealed the following: (1) The spatial distribution of stores was concentrated in areas with high closeness and betweenness centrality, forming a multi-core "northwest-southeast" distribution pattern with significant spatial positive correlation. (2) The stores' spatial pattern was influenced by the road network betweenness and the presence of office buildings, shopping malls, shopping centers, and tourism resources. The road network betweenness had the greatest impact on the stores' spatial distribution, while the kernel density of betweenness presented a "one major and multiple sub-core" structure consistent with that of the stores' spatial distribution. The kernel density of closeness and betweenness regulated the formation of the stores' core area and multiple sub-core areas, respectively, and both factors governed the stores' spatial distribution, which was characterized by a "widely-scattered and sporadically-clustered" pattern. (3) The stores' distribution was closely associated with the spatial and dynamic population distribution at different times of the day. By demonstrating the big data for the spatial distribution and driving factors of milk tea stores at the urban regional scale, we fill the research gap on the spatial distribution of milk tea stores at the meso-scale. Our results offer insights into the future urban planning of milk tea stores amid the current milk tea craze.

The Metaverse Is Geospatial: A System Model Architecture Integrating Spatial Computing, Digital Twins, and Virtual Worlds

Virtual geographic environments long simulated real-world scenarios in urban planning, monument preservation, city infrastructure management, education, and entertainment. Their web-based visualisation and distribution made these environments widely accessible. However, many systems remain static, lacking real-time data integration and multi-user collaboration, while virtual worlds designed for the Metaverse emphasise dynamic interaction yet often omit essential geospatial context. Bridging this gap is critical for advancing virtual geographic environments into the next generation. In this paper, we present a modular system architecture for applications demonstrating geospatial virtual worlds over the web. Our goal is to provide a generic, well-structured framework that exposes the essential classes and interfaces needed for building 3D virtual worlds with geospatial data at their core. Our work focuses on defining specific geospatial components, methods, classes, and interfaces that form the foundation of a modern geospatial virtual environment in the Metaverse era. The proposed architecture is organised into three layers: access, world, and integration, which together enable accurate mapping and integration of real-time sensor data, digital twin synchronisation, and support for location-based services. Our analysis reveals that while most current solutions excel in either multi-user interaction or geospatial data management, they rarely combine both. In contrast, our model delivers enhanced geospatial focus, real-time collaboration, and interoperability between physical and digital realms. Overall, this work lays a solid foundation for future innovations in creating immersive, interactive, and geospatially grounded virtual experiences over the web, marking an important step in the evolution of virtual geographic environments for the Metaverse era.

PERANCANGAN FORMULASI DAN IMPLEMENTASI STRATEGI BISNIS PADA UMKM DAPURATU DI SUBANG

This study aims to design the formulation and implementation of business strategies in DapuRatu MSMEs located in Subang, with a focus on developing sustainable and competitive culinary businesses. In facing increasingly competitive market challenges, DapuRatu needs the right strategy to improve product quality, service, and competitiveness. This study uses a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis approach to identify internal and external factors that affect business performance and formulate appropriate strategies to optimize business potential. The results of the study indicate that DapuRatu's strengths lie in its strategic location, affordable prices, and good service quality. However, the company also faces challenges such as the number of competitors and lack of product innovation. By taking advantage of existing opportunities, such as increasing the tourism sector in Subang, DapuRatu can develop new, innovative menus and increase market reach through online platforms. In addition, effective marketing strategies and good financial management are needed to ensure sustainable growth.

Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System

Background/Objectives: Early detection and diagnosis are important when treating gallbladder (GB) diseases. Poorer clinical outcomes and increased patient symptoms may result from any error or delay in diagnosis. Many signs and symptoms, especially those related to GB diseases with similar symptoms, may be unclear. Therefore, highly qualified medical professionals should interpret and understand ultrasound images. Considering that diagnosis via ultrasound imaging can be time- and labor-consuming, it may be challenging to finance and benefit from this service in remote locations. Methods: Today, artificial intelligence (AI) techniques ranging from machine learning (ML) to deep learning (DL), especially in large datasets, can help analysts using Content-Based Image Retrieval (CBIR) systems with the early diagnosis, treatment, and recognition of diseases, and then provide effective methods for a medical diagnosis. Results: The developed model is compared with two different textural and six different Convolutional Neural Network (CNN) models accepted in the literature—the developed model combines features obtained from three different pre-trained architectures for feature extraction. The cosine method was preferred as the similarity measurement metric. Conclusions: Our proposed CBIR model achieved successful results from six other different models. The AP value obtained in the proposed model is 0.94. This value shows that our CBIR-based model can be used to detect GB diseases.

Privacy-Preserving Dynamic Spatial Keyword Query Scheme with Multi-Attribute Cost Constraints in Cloud–Edge Collaboration

The rapid advancement of the Internet of Things (IoT) and mobile devices has made location-based services (LBSs) increasingly prevalent, significantly improving daily convenience and work efficiency. However, this widespread usage has raised growing concerns about privacy and security, particularly during data outsourcing to cloud servers, where users’ location information and related data are susceptible to breaches by malicious actors or attackers. Traditional privacy-preserving spatial keyword schemes often employ Bloom filters for data encoding and storage. While Bloom filters offer high lookup speeds, they suffer from limitations such as a relatively high false positive rate in certain scenarios and poor space efficiency. These issues can adversely affect query accuracy and overall user experience. Furthermore, existing schemes have not sufficiently addressed the multi-attribute characteristics of spatial textual data. At the same time, relying solely on cloud servers for large-scale data processing introduces additional challenges, including heavy computational overhead, high latency, and substantial communication costs. To address these challenges, we propose a cloud–edge collaborative privacy-preserving dynamic spatial keyword query scheme with multi-attribute cost constraints. This scheme introduces a novel index structure that leverages security-enhanced Xor filter technology and Geohash techniques. This index structure not only strengthens query security and efficiency but also significantly reduces the false positive rate, thereby improving query accuracy. Moreover, the proposed scheme supports multi-attribute cost constraints and dynamic data updates, allowing it to adapt flexibly to practical requirements and user-specific needs. Finally, through security analysis and experimental evaluation, we demonstrate that the proposed scheme is both secure and effective.

Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead Reckoning.

Location-based services provide significant economic and social benefits. The ubiquity, low cost, and accessibility of geomagnetism are highly advantageous for localization. However, the existing geomagnetic localization methods suffer from location ambiguity. To address these issues, we propose a fusion localization algorithm based on particle swarm optimization. First, we construct a five-dimensional hybrid LSTM (5DHLSTM) neural network model, and the 5DHLSTM network structure parameters are optimized via particle swarm optimization (PSO) to achieve geomagnetic localization. The eight-dimensional BiLSTM (8DBiLSTM) algorithm is subsequently proposed for heading estimation in dead reckoning, which effectively improves the heading accuracy. Finally, fusion localization is achieved by combining geomagnetic localization with an improved pedestrian dead reckoning (IPDR) based on an extended Kalman filter (EKF). To validate the localization performance of the proposed PSO-5DHLSTM-IPDR method, several extended experiments using Xiaomi 10 and Hi Nova 9 are conducted in two different scenarios. The experimental results demonstrate that the proposed method improves localization accuracy and has good robustness and flexibility.

Ambulatory Medication Errors and Adverse Events Involved in Medicine-Related Malpractice Cases From 2011 to 2021.

The aims of the study were to identify the characteristics of medication-related malpractice claims occurring in the ambulatory setting across 2 time periods. A retrospective, descriptive study was used. Ambulatory medication-related closed malpractice events from loss years of 2011-2021 were analyzed. Analysis included gender and age, injury severity, location, major injury, medications, primary and secondary services, roles, contributing factors, primary drivers, indemnity paid, and disposition status. Findings were analyzed using the Candello Explore tool. Five hundred four ambulatory medication-related closed malpractice events were included. Family medicine (19.4%) and internal medicine (10.4%) services had the largest number of claims. The top medications were anti-infectants (16.4%), narcotics (15.6%), and anticoagulants (14.4%). Medication-related claims were multifactorial, averaging 4.1 contributing factors per claim. Clinical judgment issues were found in over 55% of the claims, and communication factors were observed in almost 48% of the claims. In the comparison analysis, 433 claims from 2011 to 2015 and 161 claims from 2016 to 2021 were evaluated. A statistical decrease in claims involving anticoagulants and narcotics ( P = 0.01) was seen in the second period. There was a statistical increase in claims involving pharmacy dispensing errors during the 2016-2021 period ( P = 0.03). Adverse drug events in ambulatory care are multifactorial. Errors in the technical process of ordering and managing medications, patient education, patient adherence, and pharmacy dispensing were the primary drivers of ambulatory medication malpractice claims in this study.

Disparities between residential and commercial zones in air quality revealed by location-based services

Disparities between residential and commercial zones in air quality revealed by location-based services

Eviora: A Womens Safety Application

Abstract: Eviora is a comprehensive safety application designed to empower women with tools for real-time assistance, incident reporting, and personal security. By integrating advanced location services, crowdsourced safety data, and emergency response mechanisms, the app bridges the gap in women’s safety technology. This paper explores the app's features, technical architecture, and potential societal impact

Individual mobility prediction by considering current traveling features and historical activity chain

ABSTRACT Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current traveling, but overlook those in historical trips, as well as relationships between traversed road intersections. These issues hinder the model from effectively capturing complex mobility patterns. To fill this gap, we propose a novel method that incorporates current traveling features and historical activity chain to predict the coordinates of traveling destination. Specifically, (1) we construct current traveling features by extracting real-time moving states, and represent spatiotemporal correlations between traversed road intersections using word embedding; (2) we learn travel intentions as a probability vector for each historical trip, and combine it with spatiotemporal features to construct historical activity chain; (3) we construct an individual mobility prediction model using Long Short-Term Memory (LSTM) network and spatiotemporal scoring mechanism, to capture short-term and long-term dependencies in current trip and historical activity chain, respectively. Experiments on 21,890 trajectories over the whole Year 2019 of 20 representatives selected from 1916 private car travelers in Shenzhen City, reveal the effectiveness of our model. It outperforms four baselines, Random Forest (RF), Distant Neighboring Dependencies (DND), Location Semantics and Location Importance (LSI)-LSTM, as well as Intersection Transfer Preference and Current Movement Mode (ITP-CMM), by approximately 10%-15% improvement in accuracy. In addition, we further explore the impact of historical activity chain length, and destination visiting frequency on prediction, as well as the relationship between predictability and eight mobility pattern features. This study benefits potential applications such as personalized location-based service recommendations and targeted advertising, and also provides implications for understanding human mobility.

The Impact of Tele pharmacy in Rural Healthcare

In many nations, the rapid expansion of information and telecommunication technology use has a significant impact on healthcare delivery. Geographical and demographic characteristics, however, frequently make it difficult for rural areas and residents to access healthcare services. Telepharmacy, a relatively new term for pharmaceutical services, allows a qualified pharmacist to provide healthcare services, including medication reviews, patient counseling, and prescription verification, to patients who live far from a hospital, pharmacy, or healthcare facility. Economic benefits, patient satisfaction due to medication access and information in rural areas, easy access to healthcare services in remote and rural locations, effective patient counseling, and a lack of local pharmacists and pharmacy services are just a few of the well-known advantages of telepharmacy. Although telepharmacy is an excellent idea, its implementation can occasionally be difficult. There are hazards and legal issues that come with implementing these techniques that must be resolved. Significant time, money, and effort are required for the technology, software, connectivity, and operational costs associated with telepharmacy startup. One of the main obstacles to telepharmacy services for rural hospitals with fewer patients seems to be cost.Furthermore, there are still issues with the execution and uniformity of the telepharmacy law. A properly-designed system, however, has the power to transform pharmacy practice in ways that benefit rural communities as well as the hospitals or retail pharmacies that provide these services.

Research on multiple enhanced k combination reverse Skyline query method

The reverse Skyline query aims to identify a set of data points that dynamically dominate a query point from the decision-makers’ perspective. It has been widely applied in business decision-making, recommendation systems, location-based services, knowledge discovery, and data mining. However, existing reverse Skyline queries mainly focus on single-point queries, overlooking multi-point combination queries. To address this, we propose the concept of combination reverse Skyline query (CRSQ), based on single query combinations. Furthermore, to handle multiple combinations with different cardinalities, we develop the Multiple Enhanced k combination reverse Skyline query method (MkECRSQ). MkECRSQ includes three main phases. Initially, we prove that k combination reverse Skyline query (kCRSQ) is NP-hard and propose a novel index structure called QR-GMap for combination queries to significantly accelerate kCRSQ. Subsequently, we compare the multiple kCRSQ results of various k values to determine the most dominant combinations. Finally, we expand the result set by proving the monotonicity of the ECRSQ algorithm. The final MkECRSQ results consist of the obtained combinations and the expanded result set. Theoretical and experimental results show that MkECRSQ not only rapidly yields results for CRSQ but also recommends the most dominant combinations to decision-makers among multiple combinations in the query dataset, while also overcoming the challenge of limited cardinality in the result sets. By introducing CRSQ and MkECRSQ, our work fills a significant research gap in reverse Skyline queries, extending their applicability to multi-point combination queries and offering enhanced decision-making support.

Automatically generated place descriptions for accurate location identification: a hybrid approach with rule-based methods and LLM

ABSTRACT This paper explores the potential of using machine-generated descriptions to characterize a place in a way that humans can identify it. It presents a hybrid approach for generating place descriptions by combining rule-based generation of spatial relation facts with a LLM that converts these facts into natural language descriptions. The study focuses on urban areas and street-level scale, using OpenStreetMap as the primary data source. The rule-based method is informed by an experimental study that analyzed human-made place descriptions to understand reference object types used, their quantities, distances, and spatial relations. An experiment is carried out to assess the quality of machine-generated descriptions compared to human-made descriptions in a place identification task. The evaluation involved 70 participants identifying locations based on both human and machine-generated descriptions across a 200-hectare urban area. The results show that the same average identification accuracy was not achieved. However, the proposed method reached lower variance and the difference in accuracy is not substantial enough to impede place identification in the anticipated use cases. The method shows promise for applications in navigation systems, virtual assistants, and location-based services, particularly in situations where visual media cannot be used.

Cost-Effective Electromagnetic Lens-Assisted Microstrip Patch Antenna Design for Location-Based Services in 5G/6G Technology Radio Frequency Front End

Cost-Effective Electromagnetic Lens-Assisted Microstrip Patch Antenna Design for Location-Based Services in 5G/6G Technology Radio Frequency Front End

Collision avoidance and routing based on location access (CARLA) of mobile robots

Abstract The paper introduces a new path-planning robotic system methodology called Collision Avoidance and Routing based on Location Access (CARLA) for use in critical environments such as hospitals and crises where quick action and saving human lives are vital. The main focus of our framework is on accuracy and fast responses, such as delivering tools or items in a specific area while avoiding collisions with other robots and obstacles. CARLA is designed to provide quick responses during emergencies, unlike most existing algorithms that are integrated into site control units or distributed among mobile robots on-site. By being loaded onto a remote server node rather than individual robots, CARLA helps to conserve the robots' capabilities, hardware resources, and power consumption. Additionally, our system utilizes cloud computing and Fog servers technology to improve data transmission times between the cloud and smart devices, especially for applications with strict timing requirements like emergency response. The Fog platform is also leveraged to enhance on-site access to real-time interaction and location-based services by bringing processing power closer to the robots from far-off Cloud servers. CARLA has various applications, such as in factories and warehouses, where mobile robots need to be selected and directed by a central control system remotely. The proposed framework consists of three main modules: Robot Knowledge Module, Robot Selection Module, and Route Reservation Module, which will all be discussed in detail in this paper. The results of simulations using this framework show that the robots have improved flexibility and efficiency in terms of computing paths and successfully fulfiling requests without colliding, compared to traditional methods used in similar scenarios.

Twitter User Geolocation Based on Location Feature Enhancement

User location discovery from social media is crucial for location-based services like emergency awareness and event monitoring. Existing approaches generally integrate user-generated text features and social relationships, but insufficiently explore location-specific features and geographically proximate relationships, leading to suboptimal accuracy. In this paper, we propose a Twitter user geolocation method based on location feature enhancement, to better capture the location characteristics in users’ tweets and social relationships. Specifically, a user tweet representation algorithm based on location feature separation (TwLS) is designed. By leveraging words’ location-aware weight matrix and pre-trained embeddings, TwLS calculates a tweet representation for each user in every location, explicitly indicating the relevance between users and various locations. Additionally, we develop the local celebrity discovery method (LocCel) to construct social networks by identifying and preserving geographically concentrated high-degree nodes while filtering noise. Thereby LocCel enhances local relationships and strengthens location-proximate connections within the user social network. Experiments on two real-world datasets show that our method outperforms seven baselines, improving user geolocation accuracy by 3.1% ∼ 8.1% and 1.8% ∼ 8.8%, while reducing median error by 22.2% ∼ 52.8% and 19.4% ∼ 50.7%, respectively.