Location-based Social Network Data Research Articles

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.

Spatial Urban Analysis of Attractions in Riyadh City Using Location-Based Social Network Data (LBSNs)

Spatial Urban Analysis of Attractions in Riyadh City Using Location-Based Social Network Data (LBSNs)

Spatial Urban Analysis of Attractions in Riyadh City Using Location-Based Social Network Data (LBSNs)

With the proliferation of technology and the advancement of mobile devices and the internet, the use of Location-Based Social Networks (LBSNs) has become popular in urban communities. That providing a rich source of data for analyzing social and urban behaviour in cities. Which allows for the tracking of urban interaction patterns and human activity to understand visitor behaviour in these places. This paper aims to identify spatial patterns and the distribution the attractions using LBSNs. Density estimation, spatial direction monitoring, and identification of spatial correlation of attractions within Riyadh city were conducted. Heat maps were utilized to reveal the relative density of attraction spots in Riyadh. On the other hand, the distribution trend was analysed using the SDE tool. Moreover, Moran's I index was employed to measure the spatial correlation of attractions within the city. The results of directional and density analysis of attractions indicate a tendency for attraction spots to concentrate in the city centre. Additionally, the results reflect variations between types of places across the studied categories, including parks, shopping centres, entertainment venues, and tourist attractions. The Moran index indicates spatial correlation for the attractions. Overall, LBSNs can be considered an additional and reliable source of big data for social media to monitor urban places and interactions within the city. Future integration of temporal analysis with spatial analysis can be pursued to identify spatiotemporal changes over time. Key words: GIS, Spatial Analysis, LBSNs, Google maps.

Learning Time Slot Preferences via Mobility Tree for Next POI Recommendation

Next Point-of-Interests (POIs) recommendation task aims to provide a dynamic ranking of POIs based on users' current check-in trajectories. The recommendation performance of this task is contingent upon a comprehensive understanding of users' personalized behavioral patterns through Location-based Social Networks (LBSNs) data. While prior studies have adeptly captured sequential patterns and transitional relationships within users' check-in trajectories, a noticeable gap persists in devising a mechanism for discerning specialized behavioral patterns during distinct time slots, such as noon, afternoon, or evening. In this paper, we introduce an innovative data structure termed the ``Mobility Tree'', tailored for hierarchically describing users' check-in records. The Mobility Tree encompasses multi-granularity time slot nodes to learn user preferences across varying temporal periods. Meanwhile, we propose the Mobility Tree Network (MTNet), a multitask framework for personalized preference learning based on Mobility Trees. We develop a four-step node interaction operation to propagate feature information from the leaf nodes to the root node. Additionally, we adopt a multitask training strategy to push the model towards learning a robust representation. The comprehensive experimental results demonstrate the superiority of MTNet over eleven state-of-the-art next POI recommendation models across three real-world LBSN datasets, substantiating the efficacy of time slot preference learning facilitated by Mobility Tree.

Understanding the Spatial Distribution of Ecotourism in Indonesia and Its Relevance to the Protected Landscape

Ecotourism, a dynamic force in global tourism, holds promise for conserving the environment while ensuring benefits for local economies. In this study, we developed an ecotourism distribution map of Indonesia. We utilized location-based social networks (LSBNs) data derived from Google Maps API to map 172 ecotourism sites in Indonesia. Furthermore, we investigated the distribution patterns of ecotourism within Indonesia’s protected landscapes and ecoregions. The factors that influenced ecotourism distribution in the region were analyzed using the MaxEnt model (because of its application for presence-only data). The key findings revealed that ecotourism sites are predominantly distributed across national parks and protected forest areas, and generally consist of mountainous and hilly terrain according to the ecoregion types. The MaxEnt model results indicated that population density was the most influential factor in ecotourism distribution. The significance of our study lies in its methodologies and results, which offered novel approaches to nationwide mapping and addressed the lack of an ecotourism site map of Indonesia. Notably, the proposed model can be customized for other regions with limited ecotourism data; thus, our study can serve as a foundation for future interdisciplinary studies on ecotourism, sustainability, and landscape planning.

Revisiting city tourism in the longer run: an exploratory analysis based on LBSN data

ABSTRACT This study addresses the methodological gap in tourism research regarding the long-term monitoring of tourism activities in urban settings. We propose an analytical framework that uses data from location-based social networks (LBSN) to derive tourists’ digital footprints resulting in a sustained, yet partial, overview of tourist activity and mobility in urban destinations. Significantly, we found that LBSN data might signal changes in the geography of city tourism over time. This study pioneers the use of LBSN data to gain knowledge about city tourism in the longer run, thereby providing a means to review the development of tourism cities. The proposed framework abstracts the geographic dimension of tourism cities and extends spatial analysis to the study of tourism destinations. Moreover, the materials and methods used can be reproduced in other case studies, offering spatial measurements for comparative study, and potentially informing urban planning and design in tourism destinations.

A two-stage stochastic programming approach for dynamic OD estimation using LBSN data

Estimating origin–destination (OD) demand is essential for urban transport management and traffic control systems. With the ubiquity of smartphones, location based social networks (LBSN) data has emerged as a new rich data source with broad urban spatial and temporal coverage highly suitable for OD estimation. Its nature of confirmed trip purpose (activity) and activity chain host makes it more advantageous than other data (e.g., household travel surveys and traffic network detection). On the other hand, LBSN data is a more direct and accurate representation of demand patterns and can remove the significant burden of developing traffic models and estimating simulation-based objective functions. However, thus far, most LBSN-based estimation models only focus on static (day-level) OD estimation, making less use of those characteristics. To this end, this paper establishes a two-stage stochastic programming (TSSP) framework integrating the activity chains to model activity-level dynamic mobility flows using LBSN data. The first-stage model aims to minimize the errors introduced by the inter-zone OD flows alongside the expected errors of the check-in patterns. The second-stage model attempts to minimize the errors produced by the considered check-in pattern scenarios. Markov chain Monte Carlo (MCMC) sampling is used to generate plausible check-in scenarios. A generalized Benders decomposition (GBD) algorithm is presented to solve the two-stage stochastic programming model. We conduct the experiments on the case study of Tokyo, Japan, under the employment of the generalized least squares (GLS) estimator. The results show that algorithm convergence can be guaranteed within several iterations. The approach can provide satisfactory estimations of check-in patterns, zonal production and attraction, and OD flows. Furthermore, multiple objective function states are tested for evaluating the completeness of the proposed framework and exploring its potential for simplification and extension. Incorporating specific penalty terms into the objective function also provides a way to verify the reliability of the two-stage structure and validate the effectiveness of the model. Finally, we discuss the model enhancement from the perspectives of online OD estimation by integrating with LBSN simulations, network-wide OD extrapolation using appropriate scaling methods, and removing the user-side data requirement by leveraging activity chain modeling. The proposed framework provides a novel and effective approach to OD demand estimation using LBSN data.

Location Recommendation Based on Mobility Graph With Individual and Group Influences

With the rapid development of mobile technology, it is very convenient to share people's current locations by checking-in on Location-Based Social Networks (LBSNs). Using users' check-in histories to study mobility preferences and recommend new locations is a typical application to LBSNs. Most existing models explore reasonable representations for users and locations. However, a lack of behavioral mobility modeling would hamper a better understanding of users' mobility patterns. This paper proposes a location recommendation model to serve the personalized LBSNs application, called Spatio-temporal Individual mobility graph encoding network with Group Mobility Assistance (SIGMA). We design a spatio-temporal interaction enhanced graph neural network to encode the mobility graphs to represent individual mobility behaviors. Furthermore, we provide a novel stacked scoring approach to generate the recommendation score by combining the stacked individual mobility graphs with the group influences. We conduct extensive experiments on two real-world LBSNs data, Foursquare and Gowalla. The result demonstrates SIGMA outperforms ten state-of-the-art models and further confirms that both the individual and the group mobility behaviors play essential roles in the practical scenario of location recommendation.

Next Point-of-Interest Recommendation Based on Joint Mining of Spatial–Temporal and Semantic Sequential Patterns

With the widespread use of the location-based social networks (LBSNs), the next point-of-interest (POI) recommendation has become an essential service, which aims to understand the user’s check-in behavior at the current moment by analyzing and mining the correlations between the user’s check-in behaviors within his/her historical trajectory and then recommending the POI that the user is most likely to visit at the next time step. However, the user’s check-in trajectory presents extremely irregular sequential patterns, such as spatial–temporal patterns, semantic patterns, etc. Intuitively, the user’s visiting behavior is often accompanied by a certain purpose, which makes the check-in data in LBSNs often have rich semantic activity characteristics. However, existing research mainly focuses on exploring the spatial–temporal sequential patterns and lacks the mining of semantic information within the trajectory, so it is difficult to capture the user’s visiting intention. In this paper, we propose a self-attention- and multi-task-based method, called MSAN, to explore spatial–temporal and semantic sequential patterns simultaneously. Specifically, the MSAN proposes to mine the user’s visiting intention from his/her semantic sequence and uses the user’s visiting intention prediction task as the auxiliary task of the next POI recommendation task. The user’s visiting intention prediction uses hierarchical POI category attributes to describe the user’s visiting intention and designs a hierarchical semantic encoder (HSE) to encode the hierarchical intention features. Moreover, a self-attention-based hierarchical intention-aware module (HIAM) is proposed to mine temporal and hierarchical intention features. The next POI recommendation uses the self-attention-based spatial–temporal-aware module (STAM) to mine the spatial–temporal sequential patterns within the user’s check-in trajectory and fuses this with the hierarchical intention patterns to generate the next POI list. Experiments based on two real datasets verified the effectiveness of the model.

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants’ behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user’s activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people’s circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users’ inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion.

Using a Flexible Model to Compare the Efficacy of Geographical and Temporal Contextual Information of Location-Based Social Network Data for Location Prediction

In recent years, next location prediction has been of paramount importance for a wide range of location-based social network (LBSN) services. The influence of geographical and temporal contextual information (GTCI) is crucial for analyzing individual behaviors for personalized point-of-interest (POI) recommendations. A number of studies have considered GTCI to improve the performance of POI prediction algorithms, but they have limitations. Moreover, reviewing the related literature revealed that no research has investigated and evaluated the GTCI of LBSN data for location prediction in the form presented in this study. Here, we extended the gated recurrent unit (GRU) model by adding additional attention gates to separately consider GTCI for location prediction based on LBSN data and introduced the extended attention GRU (EAGRU) model. Furthermore, we used the flexibility of the EAGRU architecture and developed it in four states to compare the efficacy of GTCI for location prediction for LBSN users. Real-world, large-scale datasets based on two LBSNs (Gowalla and Foursquare) were used for a complete review. The results revealed that the performance of the EAGRU model was higher than that of competitive baseline methods. In addition, the efficacy of the geographical CI was significantly higher than the temporal CI.

Prediction and Classification of User Activities Using Machine Learning Models from Location-Based Social Network Data

The current research has aimed to investigate and develop machine-learning approaches by using the data in the dataset to be applied to classify location-based social network data and predict user activities based on the nature of various locations (such as entertainment). The analysis of user activities and behavior from location-based social network data is often based on venue types, which require the input of data into various categories. This has previously been done through a tedious and time-consuming manual method. Therefore, we proposed a novel approach of using machine-learning models to extract these venue categories. In this study, we used a Weibo dataset as the main source of research and analyzed machine-learning methods for more efficient implementation. We proposed four models based on well-known machine-learning techniques, including the generalized linear model, logistic regression, deep learning, and gradient-boosted trees. We designed, tested, and evaluated these models. We then used various assessment metrics, such as the Receiver Operating Characteristic or Area Under the Curve, Accuracy, Recall, Precision, F-score, and Sensitivity, to show how well these methods performed. We discovered that the proposed machine-learning models are capable of accurately classifying the data, with deep learning outperforming the other models with 99% accuracy, followed by gradient-boosted tree with 98% and 93%, generalized linear model with 90% and 85%, and logistic regression with 86% and 91%, for multiclass distributions and single class predictions, respectively. We classified the data using our machine-learning models into the 10 classes we used in our previous study and predicted tourist destinations among the data to demonstrate the effectiveness of using machine learning for location-based social network data analysis, which is vital for the development of smart city environments in the current technological era.

Developing A Contextual Combinational Approach for Predictive Analysis of Users Mobile Phone Trajectory Data in LBSNs

Today, smartphones, due to their ubiquity, have become indispensable in human daily life. Progress in the technology of mobile phones has recently resulted in the emergence of several popular services such as location-based social networks (LBSNs) and predicting the next Point of Interest (POI), which is an important task in these services. The gathered trajectory data in LBSNs include various contextual information such as geographical and temporal contextual information (GTCI) that play a crucial role in the next POI recommendations. Various methods, including collaborating filtering (CF) and recurrent neural networks, incorporated the contextual information of the user’ trajectory data to predict the next POIs. CF methods do not consider the effect of sequential data on modeling, while the next POI prediction problem is inherently a time sequence problem. Although recurrent models have been proposed for sequential data modeling, they have limitations such as similarly considering the effect of contextual information. Nonetheless, they have a separate impact as well. In the current study, a geographical temporal contextual information-extended attention gated recurrent unit (GTCI-EAGRU) architecture was proposed to separately consider the influence of geographical and temporal contextual information on the next POI recommendations. In this research, the GRU model was developed using three separate attention gates to consider the contextual information of the user trajectory data in the recurrent layer GTCI-EAGRU architecture, including timestamp, geographical, and temporal contextual attention gates. Inspired by the assumption of the matrix factorization method in CF approaches, a ranked list of POI recommendations was provided for each user. Moreover, a comprehensive evaluation was conducted by utilizing large-scale real-world datasets based on three LBSNs, including Gowalla, Brightkite, and Foursquare. The results revealed that the performance of GTCI-EAGRU was higher than that of competitive baseline methods in terms of Acc@10, on average, by 42.11% in three datasets.

Evacuation Scenario Simulator with Location-Based Social Network Data Support

Disaster management systems apply different evacuation models to provide transportation responses to disaster situations. The main goal of the evacuation models is to reduce evacuation times to save lives.The significant increase in the use of location-based social networks (LBSN) as a major media factor can provide data for inferring the Origin-Destination matrix that represents the demands for trips during routine times. To achieve the goal of reducing overall evacuation travel times, an innovative methodology was developed that supports LBSN data for both demand prediction and decentralized personal destination recommendation.A modular multi-dimensional tool for evacuation scenario simulation (MMDT-ESS) was developed for supporting this methodology. This tool can handle multiple scenarios and compute performance evaluation values of VhT (Vehicle-Hours Traveled). MMDT-ESS can be applied using both conventional evacuation models (i.e., evacuation to predefined shelters) or decentralized personalized evacuation models and can provide different recommendations: either self-evacuation to nearest family or best friend that is situated outside the affected area or to a predefined shelter area.A test case using data from a U.S. metropolitan agency showed significant reduction in VhT when choosing evacuation scenarios with LBSN data for decentralized evacuation. Sensitivity analysis on main model parameters illustrate the robustness of the results.

A Context Awareness Hierarchical Attention Network for Next POI Recommendation in IoT Environment

The rapid increase in the number of sensors in the Internet of things (IoT) environment has resulted in the continuous generation of massive and rich data in Location-Based Social Networks (LBSN). In LBSN, the next point-of-interest (POI) recommendation has become an important task, which provides the best POI recommendation according to the user’s recent check-in sequences. However, all existing methods for the next POI recommendation only focus on modeling the correlation between POIs based on users’ check-in sequences but ignore the significant fact that the next POI recommendation is a time-subtle recommendation task. In view of the fact that the attention mechanism does not comprehensively consider the influence of the user’s trajectory sequences, time information, social relations and geographic information of Point-of-Interest (POI) in the next POI recommendation field, a Context Geographical-Temporal-Social Awareness Hierarchical Attention Network (CGTS-HAN) model is proposed. The model extracts context information from the user’s trajectory sequences and designs a Geographical-Temporal-Social attention network and a common attention network for learning dynamic user preferences. In particular, a bidirectional LSTM model is used to capture the temporal influence between POIs in a user’s check-in trajectory. Moreover, In the context interaction layer, a feedforward neural network is introduced to capture the interaction between users and context information, which can connect multiple context factors with users. Then an embedded layer is added after the interaction layer, and three types of vectors are established for each POI to represent its sign-in trend so as to solve the heterogeneity problem between context factors. Finally reconstructs the objective function and learns model parameters through a negative sampling algorithm. The experimental results on Foursquare and Yelp real datasets show that the AUC, precision and recall of CGTS-HAN are better than the comparison models, which proves the effectiveness and superiority of CGTS-HAN.

Complementing Location-Based Social Network Data With Mobility Data: A Pattern-Based Approach

Location-Based Social Networks can be profitably exploited to characterize citizens’ activities in urban environments. However, collecting LBSN is potentially challenging due to privacy concerns, connectivity issues, and potential imbalances in LBSN service usage. We propose to complement LBSN data with mobility data in the analysis of citizens’ activities in urban areas. Unlike the explicit insights provided by LBSN users, mobility data give implicit feedback on citizens’ habits. This paper explores the spatial and temporal conditions under which user habits are coherent according to both sources and reports the most reliable common sequences of visited categories of Points-Of-Interests. To this aim, it relies on a multidimensional model in which recurrent citizens’ activities are described by a new pattern type, namely the generalized activity pattern. It also detects the eventual presence of bias between LBSN and mobility user activities by customizing the established Statistical Parity metric. The motivations behind the detected bias are explained in terms of combinations of POI categories that are most likely to be the main causes. We evaluate the proposed approach on real-world data achieved from Foursquare check-ins, taxi service, and free-floating car sharing. The results highlight not only the complementarity of the data sources regarding specific POI categories, but also their interchangeability in many spatio-temporal conditions.

Forecasting commercial real estate indicators under COVID-19 by adopting human activity using social big data

Dependence of the real estate sector on human activity has been unveiled during the COVID-19 pandemic. In addition, it is assumed that trends emitted from the location-based social networks (LBSNs) successfully reflect human activities, hence commercial property trends. This study examined the use of social media to forecast commercial real estate figures during COVID-19 in Istanbul and determined the potential of social media data for forecasting the future rent/price levels of retail properties. Instagram and Twitter, two major LBSN platforms, were selected as social media data sources. First, 17 million geo-tagged Instagram posts and 230 thousand geo-referenced tweets were collected. Then, the data sets were superposed on COVID-19 key points in Turkey and the relationships observed. Finally, the data sets were combined with the commercial real estate data to monitor increases in the accuracy of rent and price predictions. Beşiktaş District of Istanbul was chosen as the pilot region to test the methodology. The results showed that the LBSN-supported models outperformed baseline models most of the time for price predictions and occasionally for rent predictions. Also, both Instagram and Twitter were found essential to the study and could not be omitted. This study demonstrates the significance and leveraging potential of applying human activities to the decision-making processes of the commercial real estate sector under COVID-19 conditions. This is the first study to adopt LBSN data to forecast commercial property prices.

Categorisation of cultural tourism attractions by tourist preference using location-based social network data: The case of Central, Hong Kong

In contrast to traditional surveys collecting tourists' stated responses, location-based social network data provide information about tourists' observed behaviours. This study proposes a methodology to categorise cultural tourism attractions based on tourists' preferences detected by their citywide travel trajectories. It was applied to Central, a district in Hong Kong, by using Instagram data of tourists who geotagged certain attraction(s) in the study area from May 2018 to April 2020. Four categories were identified through cluster analysis, and a typology of cultural tourism attractions was proposed based on the importance of historical and contemporary features. The results show significant differences among the clusters and within Hong Kong's short-haul markets. Japanese tourists prefer ‘Heritage centres’ and ‘Art galleries/Performance venues’ while tourists from Thailand prefer ‘Historic sites’ and ‘Street markets’. The study contributes an innovative approach to differentiate cultural tourism attractions, which is helpful in targeting potential tourists.

Comparing global tourism flows measured by official census and social sensing

A better understanding of the behavior of tourists is strategic for improving services in the competitive and important economic segment of global tourism. Critical studies in the literature often explore the issue using traditional data, such as questionnaires or interviews. Traditional approaches provide precious information; however, they impose challenges to obtaining large-scale data, making it hard to study worldwide patterns. Location-based social networks (LBSNs) can potentially mitigate such issues due to the relatively low cost of acquiring large amounts of behavioral data. Nevertheless, before using such data for studying tourists’ behavior, it is necessary to verify whether the information adequately reveals the behavior measured with traditional data — considered the ground truth. Thus, the present work investigates in which countries the global tourism network measured with an LBSN agreeably reflects the behavior estimated by the World Tourism Organization using traditional methods. Although we could find exceptions, the results suggest that, for most countries, LBSN data can satisfactorily represent the behavior studied. We have an indication that, in countries with high correlations between results obtained from both datasets, LBSN data can be used in research regarding the mobility of the tourists in the studied context.

Measuring Similarity Between Any Pair of Passengers Using Smart Card Usage Data

Recent years have witnessed considerable progress in the application of Internet of Things (IoT) technology in smart transportation systems. The wider presence of Wi-Fi networks in subway gates allows passengers to use the quick response (QR) code of mobile phone applications for entrance. The network established by gates has become a medium which connects stations and passengers. However, in addition to directly monitoring the passenger flow, the potential application of the smart card usage data collected by the gates remains an open topic. Although there are several clustering-based works devoted to revealing passengers’ travel behavior patterns, research on the social attributes of subway passengers is very limited. To fill the gap, this article proposes a novel method to mine similarity information of passengers by leveraging passengers’ communication behaviors hidden in subway card usage data. Passengers are first organized as a graph, which not only reflects the interactions between them but also incorporates the context information of subway stations. Then, the node embedding is used to encode the information contained in the graph and with the use of cosine similarity, the similarity between two passengers is measured. Extensive experiments on two real-world location-based social network data sets and extended experiments on a Shanghai subway data set are conducted. The results show that the proposed method can effectively improve the accuracy of similarity measurement and provide social features that are distinguishable from travel behavior patterns.