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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.

The visit paths, dwell time, and taking pictures are all variables of great significance to our understanding of tourists&rsquo, 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&rsquo, 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&rsquo, stay and photo-taking behavior, which provided a theoretical basis for defining tourist photo behavior as tourists&rsquo, 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.

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.

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.

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.

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., Highlights • 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”.

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.