Search

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Momo, a popular mobile social application, has attracted a significant number of malicious users engaged in illegal online activities such as fraud, online gambling, and illegal lending. The detection of these malicious users is of great importance. However, the loose social relationships between users and the limited textual content on Momo have made it challenging for existing methods to accurately characterize user features, resulting in low detection accuracy. To address this issue, a malicious user detection method based on the analysis of user spatio-temporal behavior and static attributes was proposed. Firstly, a user spatio-temporal information dataset was constructed by collecting user online time, location, and abnormal labels data provided by Momo’s location service. Then, the differences in online time and location distribution between normal users and malicious users with abnormal labels in the dataset were compared to analyze the spatio-temporal behavior characteristics of malicious users. Subsequently, the temporal behavior characteristics of users were characterized by the online probability of users in different time periods, and the spatial behavior characteristics of users were characterized by the degree of clustering of users’ historical location distribution. Finally, the obtained spatio-temporal behavior features and gender attributes were combined to train a user classification model for malicious user detection. A malicious user detection experiment was conducted based on 1,894,917 data points collected from 67,280 Momo users. The results demonstrate that the proposed method could effectively detect malicious users, especially for those with fewer historical content publications. The proposed method exhibits significantly better performance in correctly classifying malicious users compared to representative methods such as SybilSCAR and DatingSec, with an improvement of over 4.6% in AUC-ROC and over 19.88% in AUC-PR.

Exploring the spatial distribution of tourist attractions and comprehending the spatio-temporal behaviors of tourists within tourist attractions can provide local planning agencies, destination marketing organizations, and government departments with essential evidence for decision-making processes. This study examines the spatio-temporal behavior patterns of tourists in the Kushan Scenic Area by analyzing GPS trajectory data acquired from social media platforms. The investigation primarily utilizes three research methodologies: grid analysis, Markov chain, and K-means clustering. The grid analysis results reveal three spatial distribution patterns within the scenic area, while the outcomes from the Markov chain and K-means clustering delineate six tourist movement patterns, along with three choices regarding travel time. This finding holds significant practical implications for enhancing the attractiveness of scenic areas, optimizing spatial layout, and improving tourists' experiences. [ABSTRACT FROM AUTHOR]

Exploring the spatial distribution of tourist attractions and comprehending the spatio-temporal behaviors of tourists within tourist attractions can provide local planning agencies, destination marketing organizations, and government departments with essential evidence for decision-making processes. This study examines the spatio-temporal behavior patterns of tourists in the Kushan Scenic Area by analyzing GPS trajectory data acquired from social media platforms. The investigation primarily utilizes three research methodologies: grid analysis, Markov chain, and K-means clustering. The grid analysis results reveal three spatial distribution patterns within the scenic area, while the outcomes from the Markov chain and K-means clustering delineate six tourist movement patterns, along with three choices regarding travel time. This finding holds significant practical implications for enhancing the attractiveness of scenic areas, optimizing spatial layout, and improving tourists’ experiences.

During the global pandemic, COVID-19 patients' activity trajectories and actions emerge as revelatory conduits elucidating their spatiotemporal behavior and transmission dynamics. This study analyzes COVID-19 patients' behavior in Nanjing and Yangzhou, China, by using patient activity trajectory data in conjunction with complex network theory. The main findings are as follows: (1) The evaluation of the activity network structure of patients revealed that “residential areas” and “vegetable markets” had the highest betweenness centrality, indicating that these are the primary nodes of COVID-19 transmission. (2) The power-law distribution of the degree distribution of nodes for different facility types revealed that residential areas, vegetable markets, and shopping malls had the most scale-free characteristics, indicating that a large number of patients visited these three facility types at a few access points. (3) Community detection showed that patient visitation sites in Nanjing and Yangzhou were divided into five or six communities, with the largest community containing the outbreak origin and several residential areas surrounding it. (4) Patients had fewer activities across administrative regions but more activities across the life circle when the pandemic broke out in the suburbs, and more activities across administrative regions but fewer activities across the life circle when the pandemic broke out in the central city. Based on these findings, this paper makes recommendations for future pandemic preparedness in an effort to achieve effective pandemic control and reduce the damage caused by pandemics. Overall, this study provides insights into understanding the transmission patterns of COVID-19 and may inform future pandemic control strategies.

Problems such as global environmental pollution and climate change have made the public's desire for nature and closeness to greenery increasingly strong amid rapid urbanization. Improving the ability of experiential environmental interpretation products and services is the basis for national parks to meet the public's needs, and the evaluation of their effectiveness is a necessary basis for optimizing the quality of environmental interpretation services in response to the current problems of unsynchronized environmental interpretation facilities and service levels. Using Pudacuo National Park as a case study, 365 visitors' spatio-temporal trajectories with GPS devices and questionnaire data were collected, and the interaction changes of visitors' external spatiotemporal behaviors and internal emotional experiences were analyzed using cluster analysis, GPS geoprocessing model construction, and emotional mean calculation methods, and the results showed that (1) Pudacuo National Park visitors mainly comprise four types of visitor clusters, which are the sightseeing type, trekking + sightseeing type, cruise type, and hiking + cruise type, as well as four types of spatio-temporal behavior patterns; the differences of visitors' spatiotemporal behavior patterns are reflected in spatial movement, time allocation, and stopping behavior, and correspond to different emotional experience intensity. (2) Emotional experience value is positively correlated with location stay time, and emotional experience intensity is higher, corresponding to spatio-temporal behavior patterns with longer trajectory distance, longer dwell time, and more stopping behaviors, indicating that environmental services promote longer visitor stopping time and generate high-quality recreation experiences. (3) Finally, we proposed the optimization of environmental interpretation mode according to the spatiotemporal emotional differences of different types of visitor clusters. This study provides a basis for improving the quality of visitor experience and optimizing the quality of environmental interpretation services and provides a useful reference for guiding the construction of high-quality and diverse ecological experiences in national parks. [ABSTRACT FROM AUTHOR]

By using CiteSpace software to create a knowledge map of authors, institutions and keywords, the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database (CNKI) was analyzed and discussed. It is found that there was a lack of communication and cooperation among research institutions and scholars; the research hotspots involved four main areas, including "application in tourism research", "application in traffic travel research", "application in work-housing relationship research", and "application in personal family life research". [ABSTRACT FROM AUTHOR]

Competition, predation, and diseases are key factors shaping animal communities. In recent decades, lagomorphs in Europe have been impacted by virus-borne diseases that have caused substantial declines in their populations and, subsequently, in many of their predators. We examined activity and habitat-use patterns of sympatric European rabbits (Oryctolagus cuniculus L.) and Iberian hares (Lepus granatensis R.) in Doñana National Park, Spain, (DNP) during two periods of disease outbreak. In the first period (1984–1985), fecal pellet counts and roadside counts indicated that lagomorph species were segregated, with rabbits occurring in scrublands and hares in marshlands. Both species also occupied rush and fern belt ecotones. Roadside counts at sunrise, midday, sunset, and midnight revealed that rabbits and hares had the same activity patterns (crepuscular and nocturnal) in the zone of sympatry. During the second period (2005–2016), roadside counts showed that rabbits and hares were mainly nocturnal in scrublands and border marshlands. Hares occupied scrublands; a habitat previously occupied only by rabbits. These results are interpreted in light of the competition theory and predation pressure. The disease-caused decline of rabbits has likely favored hares that moved into scrublands, a vegetation type previously occupied exclusively by rabbits. The decline of rabbits in DNP has also caused the almost disappearance of this area of the Iberian lynx (Lynx pardinus), a rabbit specialist, thus enabling generalist predators to increase. Generalist predators have subsequently increased predation pressure on both rabbits and hares, causing them to switch to nocturnal activity. [ABSTRACT FROM AUTHOR]

Influence maximization, as one of the major problems in Location-based Social Networks (LBSN), aims to determine a subset of influential users to maximize the influence spread through the "word-of-mouth" effect. Although many recent studies have focused on the influence maximization problem in LBSN, a majority part of the concern is shed on the influence spread in the whole network, with an underestimation in the importance of the community structure. In this paper, we propose a Community-based Influence Maximization model to study the influence maximization problem in LBSN, with consideration of both community structure and users' spatio-temporal behavior. Two community-based algorithms are developed to maximize the influence spread, which encompass two components: 1) detecting communities in LBSN based on users' mobility; and 2) selecting the most influential individuals based on communities. In the first phase, we calculate the similarity between users according to their historical check-in data and design a Weighted Distance algorithm to detect communities based on the similarity. In the second phase, we select candidates based on local network structure and propose two different methods to calculate the precise influence spread of each candidate based on communities. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Distributive real-time autonomous systems like cyber-physical systems (CPS) and biology systems have become a hot topic nowadays. The high-level design of these systems shares a common characteristic that the behavior of agents is always space-related: i.e., agents are involved in an environment where the space relations between agents count. Agents are always sensitive to the discrete change of space relations among them at explicit time. We call such behavior the spatio-temporal behavior, which has become a critical research point recently. In this paper, we propose a process algebra called Communicating Sequential Process with Qualitative calculus (QCSP) as a formal language for modeling the spatio-temporal behavior. The key feature of our algebra is that it can reason about the space relations between agents by a finite set of binary relations that obey certain mathematical conditions - a qualitative calculus. In QCSP, each agent is confined in a space region. The space region (with an agent in it) can move as a whole and interact with other space regions. The topological structure of space regions forms the outer environment of agents, and the agents react timely according to their environment from time to time. We built our calculi by conservatively extending Timed CSP with qualitative calculus embedded in it, and illustrate how QCSP can be used to model the spatio-temporal behavior by analyzing three examples. We propose a special process algebra as a formal model for behavior that is sensitive to qualitative changing space and time. The key feature of our algebra is that it can reason about the relative location between agents by using qualitative calculus. Each agent is confined in a space region. The space region can move as a whole along with the agent confined in it and interact with other space regions. The behavior of agent depends on the interaction of space regions at an explicit time, shown by that certain actions can be triggered (or allowed) under certain space region relations and time condition. Such spatio-temporal behavior is a critical research point in nowadays distributive real-time autonomous systems like CPS or intelligent transportation systems (ITS). We propose our calculi by conservatively extending Timed CSP with qualitative calculus embedded in it to capture space region relations among agents.

During the global pandemic, COVID-19 patients' activity trajectories and actions emerge as revelatory conduits elucidating their spatiotemporal behavior and transmission dynamics. This study analyzes COVID-19 patients' behavior in Nanjing and Yangzhou, China, by using patient activity trajectory data in conjunction with complex network theory. The main findings are as follows: (1) The evaluation of the activity network structure of patients revealed that "residential areas" and "vegetable markets" had the highest betweenness centrality, indicating that these are the primary nodes of COVID-19 transmission. (2) The power-law distribution of the degree distribution of nodes for different facility types revealed that residential areas, vegetable markets, and shopping malls had the most scale-free characteristics, indicating that a large number of patients visited these three facility types at a few access points. (3) Community detection showed that patient visitation sites in Nanjing and Yangzhou were divided into five or six communities, with the largest community containing the outbreak origin and several residential areas surrounding it. (4) Patients had fewer activities across administrative regions but more activities across the life circle when the pandemic broke out in the suburbs, and more activities across administrative regions but fewer activities across the life circle when the pandemic broke out in the central city. Based on these findings, this paper makes recommendations for future pandemic preparedness in an effort to achieve effective pandemic control and reduce the damage caused by pandemics. Overall, this study provides insights into understanding the transmission patterns of COVID-19 and may inform future pandemic control strategies., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Copyright of New Architecture is the property of New Architecture Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In real space and cyberspace, people leave both online and offline traces of their daily lives, which might affect the behavior of other people. This pattern is similar to insect foraging behaviors based on pheromone reaction. Previous landscape evaluation only used questionnaires, network data, or GPS data, ignoring the relationship between online and offline activities in this mobile internet era, as well as overlooking the influence of previous activities on future behaviors. In this paper, the new model, based on the idea of pheromone, couples big website data with GPS trajectory data to analyze spatio-temporal recreational behaviors in real space and cyberspace, and finally evaluates the attractiveness of locations. To test the method, twenty-eight landscapes in Beijing were evaluated, using big website data and GPS trajectory datasets collected from 181 users over a period of about five years. The results show that this new method could be very promising, which outperforms the methods that only consider online data or offline data. [ABSTRACT FROM AUTHOR]

Due to the influence of the urban inherent regional functional distribution, the daily activities of the residents presented some spatio-temporal patterns (periodic patterns, gathering patterns, etc.). In order to further understand the spatial and temporal characteristics of urban residents, this paper research takes the taxi trajectory data of Beijing as a sample data and studies the spatio-temporal characteristics of the residents' activities on the weekdays. At first, according to the characteristics of the taxi trajectory data distributed along the road network, it takes the Voronoi generated by the road nodes as the research unit. This paper proposes a hybrid clustering method -- based on grid density, which is used to cluster the OD(origin and destination) data of taxi at different times. Then, combining with the POI data of Beijing, this research calculated the density of the POI data in the clustering results, and analyzed the relationship between the activities of residents in different periods and the functional types of the region. The final results showed that the residents were mainly commuting on weekdays. And it found that the distribution of travel density showed a concentric circle of the characteristics, focusing on residential areas and work areas. The results of cluster analysis and POI analysis showed that the residents' travel had experienced the process of" spatial relative dispersion - spatial aggregation - spatial relative dispersion" in one day. [ABSTRACT FROM AUTHOR]

The explosive growth of ride-hailing as a new mode of transportation worldwide has attracted great attention from both academic and planning communities. Ride-hailing services have induced profound changes in residential travel patterns. However, the mechanisms and spatio-temporal roles of the influence the built environment imposes on ride-hailing services remain unclear. Based on ride-hailing GPS data and Web map big data, in this study, spatio-temporal entropy indicators of ride-hailing travel behavior and fine-scaled built environment indicators are constructed. Based on these, global and local regression models are developed to explore the spatio-temporal impact mechanisms of various built environments and socio-economic factors on ride-hailing travel in Chengdu, China. A strong correlation is found between various types of built environment and the spatio-temporal entropy of ride-hailing travel. Specifically, whether working on weekdays or weekends, building density and education facilities impose a significant inhibitory effect on ride-hailing spatio-temporal entropy, while mixed land use, house prices, catering, corporate businesses, lifestyle facilities, tourist attractions, and distance to transit significantly increase ride-hailing spatio-temporal entropy. In addition, the uncertain geographic context and the differences in residents' activities on different days of the week cause significant spatio-temporal heterogeneity in the impact the built environment has on ride-hailing spatio-temporal entropy. Noteworthy, mixed land use exerts the strongest promoting effect on ride-hailing and a positive effect on ride-hailing pick-ups and drop-offs in the whole region. The intensity of this effect tends to decay gradually from the urban center to the periphery. Furthermore, ride-hailing can both substitute for and complement public transport. More interestingly, the study also found an "olive-shaped" social structure between the socio-economic level and the demand for ride-hailing. This study thus not only enriches the theory of spatial behavior within the context of the new era, the results also help planners to reduce car travel dependence by optimizing the built environment. This holds important practical significance for sustainable urban development. • Constructed a spatial-behavioral analysis framework based on multi-source spatio-temporal big data. • Proposing a spatio-temporal entropy model that can evaluate of the regularity of human mobility within an activity period. • Use GWR models to investigate the relation between built environment and ride-hailing travel in different temporal contexts. • There is significant spatio-temporal heterogeneity in the effects of various built environments on ride-hailing O Ds travel. [ABSTRACT FROM AUTHOR]

Human disturbance is of growing concern owing to the increase of human activities in natural areas. Animal responses are manifold (immediate and/or delayed, short and/or long-lasting, with numerous behaviors affected) so that comprehensive studies are few. Here, we contrasted days with low or high tourism or hunting pressures to assess direct (daytime) and indirect (nighttime) responses of 66 GPS-collared Mediterranean mouflon Ovis gmelini musimon × Ovis sp. from the Caroux-Espinouse massif (southern France) in terms of movement, habitat use and daily activity. We took advantage of the fact that both human activities occurred during different periods and with different intensities in 3 contiguous areas (among which a protected area without hunting and with limited tourism) to compare their influence on mouflon behavior. Mouflon response to tourism was limited to the area where tourism pressure was intense with a decrease in diurnal activity compensated during nighttime by an increase of nocturnal activity. Hunting had marked consequences in the two hunted areas, with a similar shift in activity between day and night, a decrease in movement sinuosity during daytime by females and an increase in nocturnal use of the best foraging habitats by males, all suggesting an increase in foraging activities during nights following disturbance. The diurnal activity of mouflon living in the protected area was also modified during hunting period, but without nocturnal compensation. These findings revealed that the impact of hunting was higher than tourism, with several components of animal behavior affected. This calls for further research on hunting side-effects in terms of disturbance, especially as it occurs during both the adverse climatic season and the breeding period. [ABSTRACT FROM AUTHOR]

Although much has been published regarding street protests on social media, few works have attempted to characterize social media users' spatial behavior in such events. The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest location, and collective posting rhythm on variations in users' spatial detectability and density during a protest in Mexico City. The best-obtained model, together with explaining the spatial density of users, shows that there is high variability in the probability of detection of social media user protest supporters and that the collective posting rhythm and the day of observation are significant explanatory factors. The implication is that studies of collective spatial behavior would benefit by focussing on users' activity centres and their urban environment, rather than their physical proximity to the protest location, the latter being unable to adequately explain spatial variations in users' probability of detection and density during the protest event. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a direct effective computation method is proposed on stability analysis of the bifurcating solutions in symmetric delay differential systems. Mechanism about multistability (there coexist multiple nontrivial equilibria/limit cycles) is analyzed. [Copyright &y& Elsevier]

This study developed a new approach to link residents' daily travel behavior and their socio-economic attributes by inferring residents' income level from their activity space, activity sequence, and geographic exposure. A classification and regression tree (CART) analysis on data from Guangzhou, China provided three key outcomes. First, residents' income level can be inferred with considerable accuracy through their daily activity and geographic exposure. Second, geographic exposure and activity sequence variables are useful in the classification process, with vital information possibly overlooked if only mobility features are used. Third, people in different income groups can be identified by different characteristics, with high-income earners identified by their preference for particular socio-economic environments, low-income earners identified by their fixed lifestyles and dependence on affordable and convenient environments, and middle-income earners identified by more comprehensive characteristics. This study has improved our understanding of social group diversity from a spatio-temporal behavior perspective. It's also instructive in merging the attributes of conventional data with those of anonymized big data. This benefits future big data research and the development of policies supported by big data. • Residents' income level can be inferred with considerable accuracy through their daily activity and geographic exposure. • Including only mobility features in inferring residents' socio-economic attributes possibly overlooked vital information. • People in different income groups can be identified by different daily behavior characteristics • Through the linkage from residents' daily behavior to their socio-economic attributes, information of big data and conventional data can be merged. [ABSTRACT FROM AUTHOR]

Geo-located travel blogs, a new data source, enable to achieve more detailed analysis of tourists' spatio-temporal behavior. Taking Chinese tourists in Nordic countries as the research object, this paper focuses on their behavior, seasonal patterns and complex network effects by using geo-located travel blog data collected from Qunar.com. The results show that: (1) Chinese tourists visiting Nordic countries are often experienced in traveling. The local climate during the cold season does not prevent them from pursuing the aurora scenery. (2) The travel behavior of Chinese tourists is spatially heterogeneous. The network analysis reveals that Iceland showcases stronger, compared to the other Nordic countries, community independence and small world effect. (3) During the warm season, Chinese tourists choose a variety of destinations, while in cold season, they tend to choose destinations with higher chances for spotting the northern lights. These results provide helpful information for the tourism management departments of Nordic countries to improve their marketing and development efforts directed for Chinese tourists. • Focus is laid on the behavior of Chinese tourists in Nordic countries. • Complex network theory is introduced to explore the flow patterns of tourists. • Scale-free and small world effects are observed in the tourist flow network. • There is no obvious tourism off-season in Nordic countries due to "aurora chasers". [ABSTRACT FROM AUTHOR]

The visit paths, dwell time, and taking pictures are all variables of great significance to our understanding of tourists' spatio-temporal behavior. Does having a large number of visitors mean that tourists are interested in a tourist location? What is the relationship between the dwell time and taking pictures? Are there differences in tourist behavior in different seasons? These issues are of great significance to tourism research but they have not been rigorously analyzed yet. This paper aims to understand the relationship between tourists' visit path, dwell time, and taking pictures, and test whether there are differences in tourist behavior in different seasons. We used open global positioning systems (GPS) trajectory data at Yuanmingyuan Park from January 2014 to August 2020. Using Python and ArcGIS tools, we found hot spots of tourist passing, hot spots of tourist gathering, high average dwell time areas, and tourist interest areas. It is further found that: (1) passenger flow strongly explains dwell time, while the correlation between passenger flow and average dwell time is weak. (2) There was a close relationship between tourists' stay and photo-taking behavior, which provided a theoretical basis for defining tourist photo behavior as tourists' stay behavior. (3) Seasons did not significantly affect tourist behavior in Yuanmingyuan Park. This study presents a grid-based open GPS trajectory data processing framework that clarified the potential of an open GPS trajectory in tourist behavior research. Furthermore, our study explored the relationship between essential indicators and found that there is a strong consistency in tourist behavior across seasons. [ABSTRACT FROM AUTHOR]