Points Of Interest Research Articles

Urban functional area building carbon emission reduction driven by three-dimensional compact urban forms’ optimization

Previous optimization approaches with regard to the overall urban form can no longer meet the demand for accurately implementing building energy consumption carbon emission reduction at small spatial scales. This study constructed building energy consumption-oriented urban functional area division method using points of point-of-interest (POI), three-dimensional building and roadway data with urban functional areas as units. The method linked POI to buildings, using building volume as an auxiliary parameter to determine the functional attributes of blocks while taking into account the building operational energy consumption. The residential functional areas, commercial functional areas and industrial functional areas, which account for more than 90% of the total building operation energy consumption and the top three in terms of share, are selected as the analysis objects. Geographic detector was applied to analyze the mechanism of carbon metabolism in the compact spatial patterns of these three functional areas. The case study in Xiamen, China reveals that (1) Three-dimensional building compactness is an important factor influencing building energy consumption(normalized difference vegetation index q-value is 0.227); (2) the difference in energy consumption between different compactness classes in residential and commercial functional areas is significant, while not in industrial functional areas; (3) according to the three-dimensional building compactness optimization path of this study, the building operation energy consumption in central Xiamen City could be optimized to reduce by 7% in 2015. Based on the self-created building energy consumption-oriented functional area division method, it is concluded at the functional areas scale that different types of urban functional zones have different optimization methods for three-dimensional building compactness. Such optimization can save construction resources and achieve double carbon more effectively.

Vivid London: Assessing the resilience of urban vibrancy during the COVID-19 pandemic using social media data

Since COVID-19, the focus on urban resilience has intensified, particularly on cities' ability to adapt and recover while maintaining essential functions and liveability; however, few studies have examined the resilience of urban vibrancy during such health crises. This study investigates urban vibrancy resilience in Inner London during the COVID-19 pandemic using multi-sourced social media data (geo-tagged Twitter and Flickr). We propose an analytical framework based on space-time permutation scan statistics (STPSS) to identify spatiotemporal urban areas of interest (ST-AOIs), examining their spatial, temporal, and contextual characteristics. Our findings show that central neighbourhoods with transport hubs, educational and healthcare facilities, eateries, and financial centres exhibit greater resilience. These areas adapt by shifting active periods in response to disruptions. Additionally, we assess the varying resilience capacities of different types of points of interest. This research provides actionable insights for urban planners and policymakers by demonstrating how identifying characteristics of robust urban vibrancy can contribute to the resilience of cities and communities, particularly under normal conditions after COVID-19. The findings offer concrete strategies for integrating social media data into urban planning processes, enabling more responsive and adaptive governance that meets the dynamic needs of urban populations.

Heating patterns and temperature distribution of projectile surface in lunar regolith penetration

During penetration, a large quantity of friction-induced heat is generated, significantly increasing the projectile surface temperature. Considering that the temperature variation depends on the physical properties of the target being penetrated, understanding this relationship can aid in extraterrestrial material behavior for detection and analysis efforts. The study investigated the patterns of heat generation and the distribution of temperature on the surface of a projectile as it penetrates lunar regolith. For discrete medium penetration, large deviations appear in temperature prediction due to particle extrusion flow. Thus, a heat flux density model on the projectile surface by introducing a relative velocity factor (RVF) for correction was established. The particle flow characteristics simulation and fitting model of the introduced factor were also obtained. We constructed a theoretical relationship between the resistance and stress model parameters using dynamic modeling. Experimental projectiles recording penetration acceleration and temperature at the points of interest on the projectile surface were designed and tested to obtain recorded data. The temperature field in this process was simulated in COMSOL software to calculate the projectile surface's heat flux density and temperature distribution. The results indicate that the developed model is effective. This research infers the physical characteristics of the penetrating target under specified penetration conditions and provides more dimensional information for lunar regolith exploration.

SimpleSets: Capturing Categorical Point Patterns with Simple Shapes.

Points of interest on a map such as restaurants, hotels, or subway stations, give rise to categorical point data: data that have a fixed location and one or more categorical attributes. Consequently, recent years have seen various set visualization approaches that visually connect points of the same category to support users in understanding the spatial distribution of categories. Existing methods use complex and often highly irregular shapes to connect points of the same category, leading to high cognitive load for the user. In this paper we introduce SimpleSets, which uses simple shapes to enclose categorical point patterns, thereby providing a clean overview of the data distribution. SimpleSets is designed to visualize sets of points with a single categorical attribute; as a result, the point patterns enclosed by SimpleSets form a partition of the data. We give formal definitions of point patterns that correspond to simple shapes and describe an algorithm that partitions categorical points into few such patterns. Our second contribution is a rendering algorithm that transforms a given partition into a clean set of shapes resulting in an aesthetically pleasing set visualization. Our algorithm pays particular attention to resolving intersections between nearby shapes in a consistent manner. We compare SimpleSets to the state-of-the-art set visualizations using standard datasets from the literature.

Pivot: Panoramic-image-based VR User Authentication against Side-Channel Attacks

With metaverse attracting increasing attention from both academic and industry, the application of virtual reality (VR) has extended beyond 3D immersive viewing/gaming to a broader range of areas, such as banking, shopping, tourism, education, etc., which involves a growing amount of sensitive and private user data into VR systems. However, with current password-based user authentication schemes in mainstream VR devices, studies demonstrate that side-channel attacks can pose a severe threat to VR user privacy. To mitigate the threat, we propose a novel Panoramic-image-based VR user authentication system, i.e., Pivot , to defend against such attacks, yet maintain high usability. Specifically, in Pivot , we design an image-random-pivoting-based user interaction mechanism to assist users in quickly and securely selecting memorable points of interest in a panoramic image. Then an image region segmentation algorithm is designed to automatically scatter the points to regions to form the customized graphic password for the user, which could ensure a sufficiently large password space and also reduce the near-region point misclicks. Afterward, the region indexes are used to generate the hashed password for authentication. Both theoretical security analysis and extensive user studies demonstrate that Pivot is secure and user-friendly in practice.

Electric Vehicle Charging Load Demand Forecasting in Different Functional Areas of Cities with Weighted Measurement Fusion UKF Algorithm

The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting.

Coordinating paths and schedules of groups visiting multiple capacitated locations

We study a situation where a large number of people visit a popular venue (e.g., an art gallery, a mall, a theme park, or an exhibition) where points of interest are located (e.g., paintings, shops, attractions, or pavilions). Visitors have a maximum amount of time available for the overall experience. The points of interest have a limiting capacity and, whenever the turnout of visitors exceeds such capacity, queues and service disruptions occur. Given the maximum time available along with the time spent queuing, a selection of the points of interest may become necessary. Visitors usually act as autonomous decision-makers and do not take into account their interaction with other visitors. This leads to remarkable inefficiencies that could be, to a certain extent, overcome through a coordination of the paths and schedules of the different visitors. The resulting optimization problem is modeled as a Mixed-Integer Linear Program (MILP), where the goal is to minimize a weighted combination of the points of interest not selected and the time spent queuing. Computational results show the benefits that can be achieved by using the model proposed as a tool to support decision-making.

Simulation of Urban Carbon Sequestration Service Flows and the Sustainability of Service Supply and Demand

Simulating ecosystem carbon sequestration service (ECSS) flows is crucial for optimizing the carbon cycle in ecosystems and achieving sustainable balance between the supply and demand of the ECSS. This study integrates least-cost path analysis with Kriging interpolation to simulate the dominant wind direction and corrects the interpolated wind speeds to account for terrain and surface conditions. Carbon emissions are spatially distributed using points of interest and road network data. Ultimately, by traversing the carbon emission rate grids along wind directions, the ECSS flows are simulated. The results reveal that the study area has a small carbon sink area but a high total carbon emission, leading to a situation where the supply of ECSS is insufficient to meet demand. The ECSS flows, based on the simulated wind field, demonstrate high spatial resolution and highlight the service flow corridors with distinct spatial heterogeneity. The study area has a significant carbon surplus, requiring a forest area ten times larger than the study area itself to fully sequester this carbon. These findings provide valuable insights for urban sustainable development and carbon emission reduction strategies.

FedGST: An Efficient Federated Graph Neural Network for Spatio-temporal PoI Recommendation

With the proliferation of sensor networks in urban areas, vast amounts of data from location-based social network platforms are now available, thus enabling the stakeholders to develop location-aware services for the end users. PoI (Point of Interests) recommendation is one of the most trending services. PoI recommendation models are developed by considering geographical information and historical records. More recently, with the demand for achieving PoI recommendations on mobile devices, embedded devices, or Internet of Things (IoT) devices, the challenges posed by the limited computational resources, malicious clients, and data silos, the model performance gets immensely affected. To address the above challenges and achieve efficient PoI recommendations, we propose an efficient federated graph learning-based model for mining complex spatiotemporal features to generate recommendations. We implement a GRU-based encoder-decoder to learn the temporal hidden state embeddings. Simultaneously, a 2-layer graph network is used to understand spatial embedding vectors. We also introduce an efficient contribution evaluation method to speed up the training process and improve the recommendation performance. Experiments on the PoI recommendation task based on real-life check-in data validate the effectiveness of our proposed model, and the results indicate that our recommendation model can achieve competitive results with lower computational costs.

A Review on Need of Effective Construction Management for Box Girder Segment Erection Techniques by Under Slung

India is creating nation due to which foundation development massively expanding day by day. As result of advancement keen arrangement required for cites infrastructural development, all of which require to be coordinates with open transport. Different inquires about and improvement are held by analysts to discover financial and time sparing technic of developments. Precast concrete is one of those investigates. The support of precast strategy in development has expanded around the world within the final few decades. This strategy of superstructure casting in urban ventures proposal numerous points of interest like diminished costs, decreased development time, decreased natural impacts, and diminished support of activity. The beneath thrown strategy for propelling box support sections stands as an inventive and effective procedure in advanced bridge development. This strategy includes suspending pre-cast box support sections from transitory underpins underneath the completed parcels of the bridge and incrementally advancing these sections to make the superstructure. The ponder will investigates the complexities and points of interest of this procedure, centering on its execution .To study the part of the transitory pre-stressing framework within the cantilever gathering prepare of the precast section box support bridge, . It is additionally critical the amid the development stage of appropriate usage of administration framework to realize the security quality and opportune completion of ventures, In this paper the need of need of effective construction management is analyzed also the effective construction management is helpful for box segment erection techniques under slag will review from various research papers and literature

58 Recreational walking and perceived environmental qualities in Denmark

Abstract Purpose The objective of this study is to investigate the variety of motivations behind recreational walking among individuals with diverse sociodemographic backgrounds. Additionally, we aim to employ a dynamic, person-centered approach to assess recreational walking behaviors geographically, along with preferences for the quality of places associated with recreational walking. Methods A survey conducted via the map-based platform Maptionnaire included 1838 adult respondents (aged 15-90 years) who engage in recreational walking. Georeferenced data regarding respondents’ home locations, other starting points for walking excursions, and points of interest along their routes were collected. Utilizing Geographic Information System (GIS), distances between home and other starting points, as well as points of interest, were calculated. Traditional questionnaire functions in Maptionnaire were also employed to gather additional information on recreational walking behaviors and motivations. Results Predominant motivations for recreational walking included mental well-being, physical health, enjoyment, and experiential aspects of walking. Individuals with tertiary education exhibited a positive correlation with motivations related to mental well-being, experiences, and outdoor activities with pets and children. Higher income levels were associated with experiences, walking with pets, enjoyment of walking, and socializing while walking. Our map-based approach revealed that recreational walking often commences at locations distant from home and is not confined to the immediate neighborhood. A total of 4598 points of interest were identified, with the most frequently cited place qualities being green spaces, water bodies, wildlife, scenic views, and tranquility. Conclusion Employing a dynamic, person-centered methodology allowed respondents to pinpoint relevant locations for their walking activities, irrespective of their residential neighborhoods. Recreational walking frequently begins away from home or extends beyond the nearest neighborhood. The median distance from home to mapped points of interest ranged between 1.0 and 1.6 km for home-based trips and between 9.4 and 30.6 km for trips originating from other locations. Greenery, water features, wildlife, scenic views, and tranquility emerged as the most favored place qualities associated with the mapped points. Support/Funding Source ‘Moving Denmark’ is funded by the Danish philanthropic foundation NordeaFonden (https://nordeafonden.dk/about-nordea-fonden).

Multi-Source Data-Driven Local-Global Dynamic Multi-Graph Convolutional Network for Bike-Sharing Demands Prediction

The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we examine various spatiotemporal influencing factors associated with bike-sharing and propose the Local-Global Dynamic Multi-Graph Convolutional Network (LGDMGCN) model, driven by multi-source data, for multi-step prediction of station-level bike-sharing demand. In the temporal dimension, we dynamically model temporal dependencies by incorporating multiple sources of time semantic features such as confirmed COVID-19 cases, weather conditions, and holidays. Additionally, we integrate a time attention mechanism to better capture variations over time. In the spatial dimension, we consider factors related to the addition or removal of stations and utilize spatial semantic features, such as urban points of interest and station locations, to construct dynamic multi-graphs. The model utilizes a local-global structure to capture spatial dependencies among individual bike-sharing stations and all stations collectively. Experimental results, obtained through comparisons with baseline models on the same dataset and conducting ablation studies, demonstrate the feasibility and effectiveness of the proposed model in predicting bike-sharing demand.

Targeted nano-energetic material exploration through active learning algorithm implementation

This paper presents an active learning-based method designed to guide experiments towards user-defined specific regions, termed ”regions of interest,” within vast and multi-dimensional thermite design spaces. Thermites composed of metallic reactant coupled to an inorganic oxidizer are non-explosive energetic materials which stay inert and stable until subjected to a sufficiently strong thermal stimulus, after which they undergo fast burning with release of high amount of chemical energy (up to 16 kJ.cm−3). They represent an interesting class of nano-engineered energetic material because of their high adiabatic flame temperature (> 2600 °C) and customizable combustion properties. We introduced a new acquisition function combining linearly two factors with the usual standard deviation of a Gaussian Process Regression algorithm. A first factor guides the sampling towards the defined zone of interest, and a second, is an incentive function that encourages the exploration of under-sampled regions of the design space. We found that our algorithm effectively provides up to several tens of nanothermites that achieve specific desired properties well-distributed within the design space, after only 200 samplings, whereas, Latin Hypercube Sampling procedure samples less than 10 points of interest. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and when only a small dataset is available, as it is the case in thermite materials. But more generally, this research represents a significant advancement for large-scale problems in energetic materials science and engineering, where predicting the effect of feature modification is desired but limited simulations or experiments can be afforded due to their high cost.

Towards SDG 11: Large-scale geographic and demographic characterisation of informal settlements fusing remote sensing, POI, and open geo-data

Informal settlements’ geographic and demographic mapping is essential for evaluating human-centric sustainable development in cities, thus fostering the road to Sustainable Development Goal 11. However, fine-grained informal settlements’ geographic and demographic information is not well available. To fill the gap, this study proposes an effective framework for both fine-grained geographic and demographic characterisation of informal settlements by integrating openly available remote sensing imagery, points-of-interest (POI), and demographic data. Pixel-level informal settlement is firstly mapped by a hierarchical recognition method with satellite imagery and POI. The patch-scale and city-scale geographic patterns of informal settlements are further analysed with landscape metrics. Spatial-demographic profiles are depicted by linking with the open WorldPop dataset to reveal the demographic pattern. Taking the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China as the study area, the experiment demonstrates the effectiveness of informal settlement mapping, with an overall accuracy of 91.82%. The aggregated data and code are released (https://github.com/DongshengChen9/IF4SDG11). The demographic patterns of the informal settlements reveal that Guangzhou and Shenzhen, the two core cities in the GBA, concentrate more on young people living in the informal settlements. While the rapid-developing city Shenzhen shows a more significant trend of gender imbalance in the informal settlements. These findings provide valuable insights into monitoring informal settlements in the urban agglomeration and human-centric urban sustainable development, as well as SDG 11.1.1.

Spatial analysis of the impact of urban built environment on cardiovascular diseases: a case study in Xixiangtang, China

BackgroundThe built environment, as a critical factor influencing residents' cardiovascular health, has a significant potential impact on the incidence of cardiovascular diseases (CVDs).MethodsTaking Xixiangtang District in Nanning City, Guangxi Zhuang Autonomous Region of China as a case study, we utilized the geographic location information of CVD patients, detailed road network data, and urban points of interest (POI) data. Kernel density estimation (KDE) and spatial autocorrelation analysis were specifically employed to identify the spatial distribution patterns, spatial clustering, and spatial correlations of built environment elements and diseases. The GeoDetector method (GDM) was used to assess the impact of environmental factors on diseases, and geographically weighted regression (GWR) analysis was adopted to reveal the spatial heterogeneity effect of environmental factors on CVD risk.ResultsThe results indicate that the built environment elements and CVDs samples exhibit significant clustering characteristics in their spatial distribution, with a positive correlation between the distribution density of environmental elements and the incidence of CVDs (Moran’s I > 0, p < 0.01). Further factor detection revealed that the distribution of healthcare facilities had the most significant impact on CVDs (q = 0.532, p < 0.01), followed by shopping and consumption (q = 0.493, p < 0.01), dining (q = 0.433, p < 0.01), and transportation facilities (q = 0.423, p < 0.01), while the impact of parks and squares (q = 0.174, p < 0.01) and road networks (q = 0.159, p < 0.01) was relatively smaller. Additionally, the interaction between different built environment elements exhibited a bi-factor enhancement effect on CVDs. In the local analysis, the spatial heterogeneity of different built environment elements on CVDs further revealed the regional differences and complexities.ConclusionsThe spatial distribution of built environment elements is significantly correlated with CVDs to varying degrees and impacts differently across regions, underscoring the importance of the built environment on cardiovascular health. When planning and improving urban environments, elements and areas that have a more significant impact on CVDs should be given priority consideration.

Prognostic impact of cytogenetic abnormalities detected by FISH in AL amyloidosis with daratumumab-based frontline therapy

AbstractWe performed an international retrospective study on 283 patients with light chain (AL) amyloidosis to investigate the prognostic impact of cytogenetic abnormalities by fluorescence in situ hybridization, when treated with frontline daratumumab–based therapy. The cytogenetic subgroups of interest were t(11;14), gain/amp(1q) (hereafter, +1q), hyperdiploidy, deletion(13q), del(17p), and myeloma high-risk (HR) translocations (t[4;14], t[14;16], or t[14;20]). The end points of interest were rate of hematologic complete response (heme-CR), very good partial response (VGPR) or better, and hematologic event-free survival (heme-EFS). The incidence of abnormalities was as follows: t(11;14), 53.4%; deletion (13q), 28.9%; +1q, 22.3%; hyperdiploidy, 19.4%; HR translocations, 6.6%; and deletion(17p), 4.5%. The heme-CR rate by cytogenetic subgroups were as follows: t(11;14) vs no t(11;14), 45.2% vs 41.8% (P=0.597); del(13q) vs no del(13q), 46.8% vs 42.8% (P=0.594); +1q vs no +1q, 30.2% vs 47.9% (P=0.022); hyperdiploidy vs no hyperdiploidy, 39.5% vs 44.9% (P=0.541); HR translocations vs none, 45.5% vs 43.1% (P=0.877); and del(17p) vs no del(17p), 50.0% vs 42.9% (P=0.658), respectively. Similarly, +1q was the only subgroup with a significantly lower VGPR or better rate (64.2% vs 79.0%; P=0.033). At a median follow-up of 19.8 months, the median heme-EFS was 49.6 months (95% CI, 24.7-not reached [NR]), and the 2-year overall survival (OS) was 80.98% (95% CI, 75.6-85.4). The presence of +1q was significantly associated with worse heme-EFS on multivariate analysis (HR 2.06, 95% CI, 1.14-3.71; P=0.017). Notably, there was no adverse prognostic impact of t(11;14) on heme-EFS or OS. In conclusion, +1q is associated with worse outcome in the daratumumab-era. Clinical trials testing novel frontline immunotherapies should be enriched in +1q to further improve outcomes in this subgroup.

Numerical prediction of ground-borne vibrations due to continuous vibratory driving of circular closed-ended piles

Various types of pile driving work may induce high-intensity ground-borne vibrations. Predicting vibration intensity before adopting mitigation measures is vital for minimizing the impact of vibration on nearby structures and occupants. Vibratory pile driving is a commonly applied foundation construction method. However, numerical simulation models for ground vibrations during a complete process of vibratory driving have rarely been studied. This study introduces an axisymmetric finite element model that utilizes the arbitrary Lagrangian-Eulerian technique to simulate the continuous vibratory driving of a circular closed-ended pile penetrating from the ground surface to a target depth. The model validity was confirmed by assessing the calculated ground vibrations against the findings documented in earlier research. The results showed that the critical penetration depth of piles, at which the maximum peak particle velocity (PPV) occurs, varied with the radial distance and depth of points of interest, contradicting a common preconception. Moreover, the maximum PPV did not always occur on the ground surface across all radial distances. Parametric analysis revealed that an increase in the soil cohesion strength, pile diameter, or soil-pile friction, or a decrease in the driving frequency or soil damping ratio would increase ground vibrations due to vibratory pile driving.

User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks

With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users’ geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches.

HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalized POI recommendation

The presence of user-generated ratings has dramatically facilitated the development of recommendation systems to aid users in discovering relevant and personalized points of interest (POI). It is worth mentioning that users’ choices and preferences are not static but rather dynamic, reflecting the ever-changing nature of human experiences and influences. Furthermore, the utilization of social influence and geographical proximity of users is still insufficient to capture the homophily effect within networks. In this paper, an interesting Hybrid Gate-based Graph Convolutional Network (HyGate-GCN) combining with feature vectors embedding and interaction, where a modified gated-GCN is proposed for personalized recommendations by adequately employing the behavior of users’ check-ins, temporal properties of users’ decisions, social properties of users, as well as the user/POI profile information data. Specifically, a novel POI graph reflecting the geographical proximity is first established to describe the behavior of users’ check-ins and, at the same time, an improved overlap ratio about POIs is employed to effectively describe temporal properties of users’ decisions. Then, an attention mechanism is developed to encode feature vectors of both the users and POIs, with the objective of assigning higher importance to features that are deemed relevant. Furthermore, a temporal Kalman filter dynamically estimating ratings is developed to exploit the information about the evolving preferences of users over time. Finally, a modified gated-GCN model with merging and refining gates is constructed to effectively acquire the homophily phenomenon in both trust network graphs and spatial adjacency matrix graphs of users and POIs respectively. Experimental results provide evidence of the effectiveness of our approach in improving accuracy and personalization.

Spatiotemporal context transition model based on graph convolutional network and its implementation

PurposeThe purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.Design/methodology/approachTo achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.FindingsExperimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.Practical implicationsThe research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.Originality/valueThe paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.