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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]

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]

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]

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]

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]