Points Of Interest Research Articles

Identifying Points of Interest (POIs) as sentinels for infectious disease surveillance: a COVID-19 study

Traditional surveillance relies on medical facilities, such as clinics and laboratories, as sentinels to monitor disease activities. Few studies have investigated the feasibility of using Point of Interests (POIs) as sentinels for disease surveillance. POIs, such as restaurants, retail stores, and churches, are places where people often interact with one another and thus play a critical role in transmission of infectious diseases like influenza and COVID-19. To fill this gap, we proposed a method to estimate people's potential crowdedness at POIs and explored its utility as an early indicator to signal local disease outbreaks. In a case study in Florida, USA, we utilized weekly foot traffic data at 0.3 million POIs to calculate their weekly crowdedness, and tested local correlations between the crowdedness of each POI and its surrounding COVID-19 incidences with different time lags. We identified 261 POIs as potential sentinels that could signal the risk one to three weeks ahead of disease outbreaks. Most of these sentinel POIs provided food/drink services, ambulatory healthcare and religious/civic services. They were characterized by a relatively large group of customers and a stable patronization over time. This research provides new insights into improving current disease surveillance systems by incorporating more diverse and widely distributed POIs.

Big geo-data unveils influencing factors on customer flow dynamics within urban commercial districts

Commercial districts, as the epicenters of urban commerce and economic activity, largely reflect an area’s prosperity through their customer flow. However, previous research, which often relied on statistical and survey data, has typically not captured the full scope of customer flow dynamics throughout urban commercial districts and has not adequately measured the specific impacts of business district locations and their surrounding communities on customer flow. To bridge these gaps, this study utilizes multidimensional big geo-data resources, including mobile phone signaling data, Points of Interest (POI) data, and transportation network data, to uncover the underlying factors that influence customer flow within urban commercial districts. The findings suggest that several factors—the size of the commercial district, the diversity of business formats, the convenience of parking, the working and residential population in surrounding communities, and the proximity to urban centers—significantly influence the customer flow. Consumers show a preference for larger-scale, centrally-located commercial districts that offer convenient parking options, while a homogenized and uncharacteristic business format may reduce a commercial district’s appeal. Furthermore, the study reveals that industrial parks and mixed-use complexes within the 15-minute living circle surrounding the commercial district have a stronger attraction to customer flow than residential neighborhoods do. The insights from this research not only guide the strategic placement of new commercial centers but also provide a robust framework for enhancing the layout of urban commercial spaces and for the revitalization and advancement of established commercial districts.

Planning Public Space Climate Comfortability: A GIS-Based Algorithm for the Compact Cities of the Far North

The issue of forming a comfortable environment in cities with complex climatic conditions has always been an urgent and difficult issue for urban planners. Cities located in the territories of the Far North are characterized by extremely harsh climatic characteristics that affect the planning solutions for the public spaces of the city. Low temperatures and strong winds reduce the time of comfortable stay in the open air, which leads to a decrease in the mobility of the population in the city and stimulates the use of personal cars. The research question is the rational placement of points of interest on the street network to ensure a comfortable travel time between objects. The research methodology of public space planning taking into account the climatic comfortability of Far North cities is proposed in this article. Also, an automated GIS-based algorithm for determining intermediate points on linear objects to increase POIs’ connectivity for the development of the public space of Far North cities under the condition of organizing climatic comfort is proposed. Development of safe and comfortable public space on the basis of network accessibility, taking into account the difficult climatic conditions of these cities, will increase the social activity of the population and tourists, as well as promote economic growth and business development in the city.

Association between land use features and changes in walking patterns from pre-pandemic to post-pandemic: A case study of city of Sydney (2013–2023)

While the impact of the pandemic on active mobility patterns is widely studied in several cities, the underlying characteristics that describe the heterogeneity in changes in active mobility are less understood. This is particularly important for post-pandemic active mobility planning. This study aims to investigate and describe the association between urban population and land-use features, as well as changes in the spatio-temporal patterns of walking from pre-pandemic to post-pandemic through a case study of the city of Sydney, Australia, using 11 years of pedestrian count data from 2013 to 2023. The findings indicate that during the pandemic, the average daily pedestrian traffic in Sydney decreased significantly compared to the pre-pandemic period. However, since experiencing the lowest pedestrian traffic in 2020, activities in the study area have shown signs of partial recovery, with a 51% increase observed in 2023. The observed changes in pedestrian activities are, however, spatially heterogeneous. Modeling results reveal that areas with greater commercial land use, more points of interest (POIs), higher population density, and higher network connectivity experienced a significant negative change in the number of walking trips from the pre-pandemic to the pandemic period. Areas with higher percentages of educational and residential use and with higher personal income experienced smaller changes in pedestrian activities during the pandemic compared to the pre-pandemic period. During the post-pandemic recovery, the influential features remain mostly unchanged; however, the association direction is the opposite.

Audio-Vibratory You-Are-Here Mobile Maps for People with Visual Impairments

Self-localization and wayfinding are challenging tasks for people with visual impairments (PVIs), severely impacting independent mobility. Visual “You-are-here” (YAH) maps are useful for assisting local wayfinding of sighted users. They are used to self-localize and display points of interest, landmarks and routes in the surroundings. However, these maps are not always available and rarely accessible to PVIs. Relying on an iterative participatory design process with eight end-users with visual impairments, we created a proof of concept of a mobile audio-vibratory YAH map. Our design is based on either a tablet or a smartphone to ensure a small and portable solution. A user study with ten PVIs showed that the audio-vibratory YAH map that we designed provides the user with a good understanding of the surroundings and wayfinding cues. Surprisingly, the results show that the audio-vibratory YAH map prototype was as usable as the control condition (audio-tactile YAH map with a tactile overlay), with similar user satisfaction and cognitive load. A follow-up field study with two participants showed the effectiveness of the prototype for assisting in crossroad understanding. To conclude, our innovative design of a mobile audio-vibratory YAH map can overcome the portability and printing issues associated with tactile overlays and can be an appropriate solution for assisting the pedestrian navigation of PVIs.

Wireless Dynamic Sensor Network for Water Quality Monitoring Based on the IoT

Water is a critical resource for human survival worldwide, and its availability and quality in natural reservoirs such as lakes and rivers must be monitored. In that way, wireless dynamic sensor networks can help monitor water quality. These networks have significantly advanced across various sectors, including industrial automation and environmental monitoring. Moreover, the Internet of Things has emerged as a global technological marvel, garnering interest for its ability to facilitate information visualization and ease of deployment—the combination of wireless dynamic sensor networks and the Internet of Things improves water monitoring and helps to care for this vital resource. This article presents the design and deployment of a wireless dynamic sensor network comprising a mobile node outfitted with multiple sensors for remote aquatic navigation and a stationary node similarly equipped and linked to a server via the IoT. Both nodes can measure parameters like pH, temperature, and total dissolved solids (TDS), enabling real-time data monitoring through a user interface and generating a database for future reference. The integrated control system within the developed interface enhances the mobile node’s ability to survey various points of interest. The developed project enabled real-time monitoring of the aforementioned parameters, with the recorded data being stored in a database for subsequent graphing and analysis using the IoT. The system facilitated data collection at various points of interest, allowing for a graphical representation of parameter evolution. This included consistent temperature trends, neutral and alkaline zone data for pH levels, and variations in total dissolved solids (TDS) recorded by the mobile node, reaching up to 100 ppm.

Measuring daily tourism mobility spillover at the intra-metropolis level with mobile positioning data

ABSTRACT Although tourism mobility spillover continues to be a key indicator for tourism management, more innovative research must be conducted at the micro level and high sampling frequency. Against the backdrop of an increasing number of global cities, in this paper, we evaluate the daily tourism mobility spillover inside a worldwide city of China: Shanghai. Based on the Granger causal network model and an original mobile positioning dataset, we analyse the causal relationship between local tourism flows and the spillover effects of tourism mobility within Shanghai. By categorising tourists into ‘local tourists from Shanghai’ and ‘tourists from out of Shanghai’, we reveal a significant causal relationship between Shanghai districts and flows generated by ‘tourists from out of Shanghai’. The analysis of the causal network structure also reveals key districts and points of interest that significantly contribute to congestion in tourism mobility and Shanghai's dynamics. This econometric approach offers policymakers a valuable tool to monitor mobility drivers and optimise flows within the city.

Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting

Urban planning and management increasingly depend on accurate building and population data. However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. This research contributes to bridging the gap between data needs and availability in resource-constrained urban environments, offering a scalable solution for global application in urban planning and population mapping.

Measuring Street Quality: A Human-Centered Exploration Based on Multi-Sourced Data and Classical Urban Design Theories

Advancements in analytical tools have facilitated numerous studies on perceived street quality. However, most have focused on limited aspects of street quality, failing to capture a comprehensive perception. This study introduces a quantitative approach to holistically measure street quality by integrating three key dimensions: visual perception, network accessibility, and functional diversity. Using Beijing and Shanghai as case studies, we employed artificial neural networks to analyze street view images and quantify the visual characteristics of streets. Additionally, street network accessibility was assessed through spatial design network analysis, and functional diversity was evaluated using the entropy of points of interest (POIs) data. The evaluation results were combined using the analytic hierarchy process. The reliability and accuracy of this method were validated through further testing. Our approach offers a human-centered, large-scale measurement framework, providing valuable insights for urban street renewal and design.

Study on the Coupling Coordination Degree and Driving Mechanism of “Production-Living-Ecological” Space in Ecologically Fragile Areas: A Case Study of the Turpan–Hami Basin

One of the key conditions for achieving superior regional growth is ensuring the harmonious development of both the layout and functions of territorial space. Territorial space, which includes production space, living space, and ecological space, serves as a critical system and venue for economic, cultural, and social activities in a region. The harmonized growth of production–living–ecological space (PLES) is essential for attaining sustainable development goals. Research on PLES offers a fresh perspective on promoting sustainable development of the spatial domain and the sustainable use of resources. However, studies on PLE functions in ecologically fragile areas are lacking. Therefore, in this study, which adopts a PLES perspective, land-use data are used to classify land according to the dominant functions of production, ecology, and living. Integration of point-of-interest (POI) data with socio-economic data was established to spatially describe indicators at the grid level and create a scoring system for PLES indicators in ecologically fragile areas. Finally, the entropy weight method, holistic assessment methods, coupling coordination degree model (CCDM), and geodetector were employed to explore the coupling coordination relationships and factors influencing PLESs in the Turpan–Hami Basin from 2010 to 2020. The results indicate that the Turpan–Hami Basin consists predominantly of potential ecological space, mainly in the central and northern regions, which are characterized by the Gobi Desert and bare rock landforms. Over the past decade, the PLES framework has seen a notable rise in the allocation of residential and ecological areas, whereas the portion dedicated to production spaces has diminished. The overall coupling coordination degree (CCD) of PLES in the Turpan–Hami Basin is at a coordinated level and gradually increasing. The most significant impact on the degree of PLES coupling coordination is exerted by population and natural factors. The research findings provide theoretical support for the sustainable utilization of resources in the Turpan–Hami Basin and other ecologically fragile areas while also offering scientific evidence to promote the coupling coordination of PLES, thereby contributing to high-quality regional development.

Reduced-Order Modeling for Dynamic System Identification with Lumped and Distributed Parameters via Receptance Coupling Using Frequency-Based Substructuring (FBS)

Paper presents an effective technique for developing reduced-order models to predict the dynamic responses of systems using the receptance coupling and frequency-based substructuring (RCFBS) method. The proposed approach is particularly suited for reconfigurable dynamic systems across various applications, like cars, robots, mechanical machineries, and aerospace structures. The methodology focuses on determining the overall system receptance matrix by coupling the receptance matrices (FRFs) of individual subsystems in a disassembled configuration. Two case studies, one with distributed parameters and the other with lumped parameters, are used to illustrate the application of this approach. The first case involves coupling three substructures with flexible components under fixed–fixed boundary conditions, while the second case examines the coupling of subsystems characterized by multiple masses, springs, and dampers, with various internal and connection degrees of freedom. The accuracy of the proposed method is validated against a numerical finite element analysis (FEA), direct methods, and a modal analysis. The results demonstrate the reliability of RCFBS in predicting dynamic responses for reconfigurable systems, offering an efficient framework for reduced-order modeling by focusing on critical points of interest without the need to account for detailed modeling with numerous degrees of freedom.

WF-LDPSR: A local differential privacy mechanism based on water-filling for secure release of trajectory statistics data

Open Data Processing Services are used to solve the bottleneck of big data storage and operation. At the same time, massive trajectory data is generated, and the basic information of users’ spatio-temporal historical data is provided, including points of interest and movement patterns. Improving the availability of published trajectory statistics data without compromising user privacy is critical. Differential privacy technology is a standard technology to realize the secure release of trajectory statistics data. Several research efforts have focused on secure publication of trajectory statistics data in a central environment by adding noise to a trusted third-party server. However, this central approach is vulnerable to privacy breaches, where adversaries can access real data by locking down the third-party server. The local differential privacy, based on a distributed architecture, overcomes this form of attack by allowing users to scramble personal data records before they are sent to third-party server. However, the existing distributed privacy protection schemes still have the balance problem of poor availability of data when ensuring privacy, as well as the problem of excessive operation cost. Therefore, a local differential privacy mechanism based on water-filling for secure release of trajectory statistics data (WF-LDPSR) is proposed in this paper. Firstly, in order to protect user privacy individually, a user automatic personalized segmentation method is proposed to determine the effective user sensitivity level automatically. Secondly, a distributed privacy protection model based on local differential privacy is designed to resist the attacks on the third-party server. Finally, in order to achieve the optimal allocation of privacy budget, the water-filling theorem in the field of communication is introduced. An adaptive privacy budget allocation algorithm based on water-filling theorem is proposed to realize the adaptive privacy budget allocation. In addition, to further improve data availability, a group processing idea based on user set sampling is proposed, which divides users into multiple disjoint subsets randomly, thus reducing the differential privacy noise effectively. Experiments prove that compared with other advanced mechanisms, the WF-LDPSR mechanism can improve the availability of published data by 84.92% while protecting user privacy.

A bottom-up nationwide analysis of sectoral land use reveals spatial heterogeneity across the United States

The high degree of urbanization and economic development in the United States has intensified competition for land resources, leading to land use conflicts and environmental challenges. Addressing these issues first requires an exploration of high-resolution, sector-specific land use patterns. However, existing land classification studies rarely provide spatially explicit data on urban land use across all sectors at a national level. This study aims to fill this gap by analyzing land use areas across various economic sectors in the United States, particularly focusing on urban impervious surfaces. Using a bottom-up approach, we created a full-sector land use inventory, integrating Points of Interest (POI) data, road networks, building characteristics, and geographic information processing techniques. Our primary innovation lies in the development of a spatially explicit database that details sector-specific land use areas nationwide. On this robust foundation, we further explored the heterogeneity of sectoral land use eco-efficiency (SLUEE) among different sectors and states, providing supplementary insights into spatial disparities in urban land use. Our findings reveal significant spatial heterogeneity, with service sectors dominating land use and exhibiting higher SLUEE compared to goods-producing industries. Key sectors such as Professional, Scientific, and Technical Services (441064.58 ha), and Administrative and Support Services (420148.34 ha) are prominent across states according to land use area, reflecting the country's service-oriented economy. Additionally, regional disparities in SLUEE are evident, with jurisdictions like Washington D.C., New York, and California demonstrating more efficient land use. Compared with traditional land use evaluations, our model effectively offers higher spatial resolution, down to the census block level, to sectoral land use evaluation. Building on this robust database, our SLUEE analysis has uncovered notable heterogeneity between sectors and states, offering new insights into cross-sectoral and inter-state land use dynamics that can inform and promote sustainable urban planning.

The Turning Point in Time from the Serbian Perspective: How to Turn the Digital Tide

Abstract To compare and contrast two points in time the article highlights the social impact of the WWW that was still hidden in 1989 and the social impact of the disappearance of the reading of printed books that is still hidden in 2024. These global points of interest for turning the tide are supplemented by some particulars from a Serbian perspective and a question: should world governments buy time for people to read printed books?

Cross border population movements across three East African states: Implications for disease surveillance and response.

The frequent population movement across the five East African Countries poses risk of disease spread in the region. A clear understanding of population movement patterns is critical for informing cross-border disease control interventions. We assessed population mobility patterns across the borders of the East African states of Kenya, Uganda, and Rwanda. In November 2022, we conducted Focus Group Discussions (FGDs), Key Informant Interviews (KIIs), and participatory mapping. Participants were selected using purposive sampling and a topic guide used during interviews. Key informants included border districts (Uganda and Rwanda) and county health officials (Kenya). FGD participants were identified from border communities and travellers and these included truck drivers, commercial motorcyclists, and businesspersons. During KIIs and FGDs, we conducted participatory mapping using Population Connectivity Across Borders toolkits. Data were analysed using thematic analysis approach using Atlas ti 7 software. Different age groups travelled across borders for various reasons. Younger age groups travelled across the border for education, trade, social reasons, employment opportunities, agriculture and mining. While older age groups mainly travelled for healthcare and social reasons. Other common reasons for crossing the borders included religious and cultural matters. Respondents reported seasonal variations in the volume of travellers. Respondents reported using both official (4 Kenya-Uganda, 5 Rwanda-Uganda borders) and unofficial Points of Entry (PoEs) (14 Kenya-Uganda, 20 Uganda-Rwanda) for exit and entry movements on borders. Unofficial PoEs were preferred because they had fewer restrictions like the absence of health screening, and immigration and customs checks. Key destination points (points of interest) included: markets, health facilities, places of worship, education institutions, recreational facilities and business towns. Twenty-eight health facilities (10- Lwakhakha, Uganda, 10- Lwakhakha, Kenya, and 8- Cyanika, Uganda) along the borders were the most commonly visited by the travellers and border communities. Complex population movement and connectivity patterns were identified along the borders. These were used to guide cross-border disease surveillance and other border health strategies in the three countries. Findings were used to revise district response and preparedness plans by strengthening community-based surveillance in border communities.

Performance Evaluation for the Expansion of Multi-Level Rail Transit Network in Xi’an Metropolitan Area: Empirical Analysis on Accessibility and Resilience

As the main form of new urbanization, the coordinated development of cities in metropolitan areas requires reliable and efficient rail transit skeleton support. However, in the rapid development of metropolitan areas, the layout and analysis of multi-level rail transit systems have a certain lag. Taking the Xi’an metropolitan area as an example, this study analyzes the comprehensive accessibility and resilience of the multi-level rail transit network, and proposes an expansion plan accordingly. The traffic analysis zone (TAZ) is divided by towns and streets, and the relationship between points of interest (POIs) and the regional average level is analyzed using DEA. The improved weighted average travel time model is built with the analysis results as regional weights; a site selection model based on multiple construction influencing factors is proposed, and four expansion plans, namely, economic optimal, environmental optimal, transport optimal, and integrated optimal, are designed. The peak passenger flow scenario and the “failure–reparation” scenario during the entire operation period are designed to analyze the resilience of four plans, and the resilience is quantified by the elasticity curve of the maximum connected subgraph ratio (MCSR) changing over time. The research results show that the transport optimal plan has the best comprehensive accessibility and resilience, reducing travel costs in Houzhenzi Town, which has the worst accessibility, by 34%. The expansion model and evaluation method in this study can provide an empirical example for the development of other metropolitan areas and provide a reasonable benchmark and guidance for the development of multi-level rail transit networks in future urban areas.

Decoupling Online Ride-Hailing Services: A Privacy Protection Scheme Based on Decentralized Identity

Online ride-hailing services have become a vital component of urban transportation worldwide due to their convenience and flexibility. However, the expansion of their user base has dramatically heightened the risks of user privacy information leakage. Among these risks, the privacy leakage problem caused by the direct correlation between user (driver and passenger) identity information and location-based ride information is of particular concern. This paper proposes a novel privacy protection scheme for ride-hailing services. In this scheme, decentralized identities are employed for user authentication, separating the identity registration service from the ride-hailing platform, thereby preventing the platform from obtaining user privacy data. The scheme also employs a fuzzy matching strategy based on location Points of Interest (POI) and a ciphertext-policy attribute-based hybrid encryption algorithm to hide the user’s precise location and restrict access to location information. Crucially, the scheme achieves the complete decoupling of identity registration services and location-based ride services on the ride-hailing platform, ensuring that users’ real identities and ride data cannot be directly associated, effectively protecting user privacy. Within the decoupled architecture, regulatory authorities are established to handle emergencies within ride-hailing services. Through simulation experiments and security analysis, this scheme is demonstrated to be both feasible and practical, providing a new privacy protection solution for the ride-hailing industry.

Treading knowledge: a proposal for an interpretive trail for environmental education in the squares of Gurupi – TO

Growing urbanization around the world results in population concentration in urban centers, composed of various artificial structures. This growth, often disorderly, compromises urban planning and the integration between built and vegetated areas, affecting the quality of life. Public areas, such as parks and squares, play a vital role in promoting urban well-being by offering ecological and social benefits. To maintain these green areas, it is essential to foster ecological awareness through environmental education. The interpretive trails emerge as an effective methodology to sensitize the population about socio-environmental issues, promoting interactive and outdoor learning. In Gurupi, Tocantins, three central squares, offer spaces for coexistence and recreation. This work proposes the creation of three interpretive trails in the central squares of the municipality of Gurupi, to promote environmental education for young students in the municipality. The work is characterized as basic research with a qualitative-quantitative approach that has as a procedure the bibliographic and field research with exploratory and investigative objectives. For this, the vegetative inventory of three squares that are in the central region was carried out, surveying the points of interest and themes for the creation of the trails, considering not only the natural aspects but also the sociocultural aspects of the environments studied.

Traffic data imputation via knowledge graph-enhanced generative adversarial network

Traffic data imputation is crucial for the reliability and efficiency of intelligent transportation systems (ITSs), forming the foundation for downstream tasks like traffic prediction and management. However, existing deep learning-based imputation methods struggle with two significant challenges: poor performance under high missing data rates and the limited incorporation of external traffic-related factors. To address these challenges, we propose a novel knowledge graph-enhanced generative adversarial network (KG-GAN) for traffic data imputation. Our approach uniquely integrates external knowledge with traffic spatiotemporal dependencies to improve data imputation quality. Specifically, we construct a fine-grained knowledge graph (KG) that differentiates attributes and relationships of external factors such as points of interest (POI) and weather conditions, facilitating more robust knowledge representation learning. We then introduce a knowledge-aware embedding cell (EM-cell) that merges traffic data with these learned external representations, providing richer inputs for the spatiotemporal GAN. Extensive experiments on a large-scale real-world traffic dataset demonstrate that KG-GAN significantly outperforms state-of-the-art methods under various missing data scenarios. Additionally, ablation studies confirm the superior performance gained from incorporating external knowledge, underscoring the importance of this approach in addressing complex missing data patterns.

Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts

Road cycling races, such as the Tour de France, captivate millions of viewers globally, combining competitive sportsmanship with the promotion of regional landmarks. Traditionally, points of interest (POIs) are highlighted during broadcasts using manually created static overlays, a process that is both outdated and labor-intensive. This paper presents a novel, fully automated methodology for detecting and tracking POIs in live helicopter video streams, aiming to streamline the visualization workflow and enhance viewer engagement. Our approach integrates a saliency and Segment Anything-based technique to propose potential POI regions, which are then recognized using a keypoint matching method that requires only a few reference images. This system supports both automatic and semi-automatic operations, allowing video editors to intervene when necessary, thereby balancing automation with manual control. The proposed pipeline demonstrated high effectiveness, achieving over 75% precision and recall in POI detection, and offers two tracking solutions: a traditional MedianFlow tracker and an advanced SAM 2 tracker. While the former provides speed and simplicity, the latter delivers superior segmentation tracking, albeit with higher computational demands. Our findings suggest that this methodology significantly reduces manual workload and opens new possibilities for interactive visualizations, enhancing the live viewing experience of cycling races.