Movement Patterns Of Groups Research Articles

Collaborative trajectory representation for enhanced next POI recommendation

Point-of-Interest (POI) recommendation stands as the cornerstone within a variety of location-based applications and services that intend to anticipate upcoming movements that users may be interested in. The current state-of-the-art methods have effectively explored spatio-temporal contextual features and users’ long-term and short-term preference patterns. Nevertheless, most existing work lacks the ability to effectively capture group movement patterns from trajectory collaboration. Additionally, they pay close attention to the accuracy of personalized recommendations, neglecting recommendation diversity, which refers to offering broader location options that extend beyond a user’s typical preferences, thereby avoiding excessive homogeneity or repetition. To address these gaps, this study proposes a Collaborative Trajectory Representation model (CTRNext), which enhances the diversity of recommendations while maintaining the precision of personalized preferences. To be specific, we first design two trajectory embedding layers to extract joint semantic interactions and the explicit spatiotemporal context-aware representation. Then, a trajectory semantic similarity calculation module that captures collaborative signals from potentially similar-minded users and eliminates barriers caused by trajectory length is proposed. Next, the implicit correlation and further updated representation between different check-in records are achieved through a multi-head self-attention aggregation module. Finally, we put forward a dual-driven user preference matching module to generate the preference-based next POI recommendation while enhancing diversity. Our approach demonstrates its remarkable recommendation accuracy through extensive experimentation on four real-world datasets, surpassing the performance of state-of-the-art methodologies.

Coherent movement patterns of female northern elephant seals across the NE Pacific Ocean

Northern elephant seals engage in large-scale foraging migrations traveling up to 15,000 Km over 8 months in the northeast Pacific. While traditionally considered solitary migrants, we demonstrate here that female seals migrate in a surprisingly coherent manner, for individual northern elephant seals traveling in over such a large region of the ocean. Animal movement remained coherent, in terms of the direction of individual swimming relative to group movement, throughout much of their migrations. Movement coherence remained well above the value expected if the movement was independent until the migrating seals were further than 1,000 Km from the colony, beyond which movement coherence declined. Migrating seals presented regional aggregations consisting of female seals traveling within the center of the aggregation, closely following the main migration pathway, with individuals isolated on the extremes of the aggregation. These formations were preserved in the out-and-return migration trips. Animals at the edges of the group show an absence of correlation in their movement with the rest of animals. The observed movements exhibited a lag in the group movement patterns that was greater for female animals > 1,000 Km apart. A model that reproduced movement based on the average individual movement properties failed to reproduce the observed movement patterns. In turn, when a parameter was introduced that reflected group behavior, the resulting modelled movement conformed to the observed patterns, thereby demonstrating the presence of coherent, or synchronized, movement. Whereas the duration of female migration is ultimately constrained by reproductive biology, the coherent movement may involve both endogenous and exogenous cues determining the timing of the initiation of return across 25 million Km2 in the northeast Pacific.

Group Animation Motion Capture Method Based on Virtual Reality Technology

In order to improve the group animation motion capture effect and enhance the design technology of group animation, this paper studies the method of group animation motion capture combined with virtual reality technology. This paper constructs a crowd motion capture system based on virtual reality technology and describes the attributes by organizing crowd profiles of different levels and structures. Moreover, this paper conducts collaborative modeling of multiple attributes and constructs a model based on structured interactive attributes. The strength of the model is that it takes into account scene scale, chaos, and crowding properties to effectively characterize the potential interactions of group movement patterns. Combined with the simulation research, it can be seen that the group animation motion capture method based on virtual reality technology proposed in this paper can play an important role in group animation motion capture and animation design.

Heterogeneity-driven collective-motion patterns of active gels

Biological swarms produce movement patterns that enhance their viability and functionality. We investigate the importance of internal heterogeneity within a group for the generation and transformation of group movement patterns. We find that an increase in the activity difference between a pair of chemically reactive gels and the rest causes the gel group to evolve from irregular random motion to ordered periodic swing-forward and circular motions. Our results imply that internal heterogeneity within a group is a key factor in generating ordered motion patterns, such as linear or curved locomotion, and disperse or compact population distributions. Dynamical analysis of collective pattern transitions reveals that the location and level of activity of a few “leaders” act as control parameters for bifurcations of collective-motion patterns. Our results suggest a possible origin of swarm motion patterns and may also be used to tailor robot swarms to enhance flexibility and robustness. • The essential role of internal heterogeneity in complex collective motion • Control of transitions between collective-motion patterns in an active group • The relationship between traveling wave dynamics and group motion patterns The generation of spontaneous collective-motion patterns in active groups, such as bird flocks, is a fascinating process. Teng et al. use a chemical model to explore the role of internal heterogeneity, i.e., the existence of a small number of “leaders,” in generating and switching between such patterns, including irregular uncoordinated motion and coordinated swing-forward and circular motions.

Human movement patterns of different racial-ethnic and economic groups in U.S. top 50 populated cities: What can social media tell us about isolation?

ABSTRACT Many studies have proven that human movement patterns are strongly impacted by individual socioeconomic and demographic background. While many efforts have been made on exploring the influences of age and gender on movement patterns using social media, this study aims to analyse and compare the movement patterns among different racial-ethnic and economic groups using social media (i.e. geotagged tweets) from the U.S. top 50 populated cities. Results show that there are significant differences in number of activity zones and median travel distance across cities and demographic groups, and that power-laws tend to be captured in both spatial and demographic aspects. Additionally, the analysis of outbound-city travels demonstrates that some cities have slightly stronger interaction with others, and that economically disadvantaged populations and racial-ethnic minorities are more restricted in long distance travels, indicating that their spatial mobility is more limited to the local scale. Lastly, an economically-segregated movement pattern is discovered – upper-class neighbourhoods are mostly visited by the upper-class, while lower-class neighbourhoods are mainly accessed by the lower-class – but some racial-ethnic groups can diversify this segregated pattern in the local scale.

Systematic Analysis of Emergent Collective Motion Produced by a 3D Hybrid Zonal Model.

Emergent patterns of collective motion are thought to arise from local rules of interaction that govern how individuals adjust their velocity in response to the relative locations and velocities of near neighbours. Many models of collective motion apply rules of interaction over a metric scale, based on the distances to neighbouring group members. However, empirical work suggests that some species apply interactions over a topological scale, based on distance determined neighbour rank. Here, we modify an important metric model of collective motion (Couzin et al. in J Theor Biol 218(1):1-11, 2002), so that interactions relating to orienting movements with neighbours and attraction towards more distant neighbours operate over topological scales. We examine the emergent group movement patterns generated by the model as the numbers of neighbours that contribute to orientation- and attraction-based velocity adjustments vary. Like the metric form of the model, simulated groups can fragment (when interactions are influenced by less than 10-15% of the group), swarm and move in parallel, but milling does not occur. The model also generates other cohesive group movements including cases where groups exhibit directed motion without strong overall alignment of individuals. Multiple emergent states are possible for the same set of underlying model parameters in some cases, suggesting sensitivity to initial conditions, and there is evidence that emergent states of the system depend on the history of the system. Groups that do not fragment tend to stay relatively compact in terms of neighbour distances. Even if a group does fragment, individuals remain relatively close to near neighbours, avoiding complete isolation.

A Survey on OD Data Visualization

<p indent=0mm>OD data is a type of data that provides origin and destination positions, and sparsely describes the movement trajectory of objects. Being the research hotspots in data visualization and geospatial analysis, visual analysis of OD data contributes to discovering group movement patterns, exploring spatial anomalies and hidden relationships, and in-depth analysis of various statistics attributes. It is started from the pair-point structure of OD data in geospatial space, and classical OD data visualization methods and principles are elaborated, such as OD matrix, OD map, OD flow, etc. Then from the perspective of spatial-temporal feature exploration, spatial and spatial-temporal joint dimensions, the visual analysis of OD data is performed, while its application scenarios and enhanced significance of feature expression, perception, extraction and presentation are researched. To solve the problem of visual clutter in large-scale OD data visualization, the visual simplification methods such as filtering, aggregation, and sampling are sorted out in the research methods of visual analysis of OD data. Finally, the applications and specific cases of OD data visual analysis technology are investigated in different domains, the challenging problems in the research of OD data visual analysis are summarized, and the trend of its future development is prospected.

Price analysis between commodity groups of inflation in Banten province from 2008 to 2018

Purpose: This study aimed to see how each commodity group's movement pattern that forms inflation between one commodity group and another, and to see any major linkages between groups of inflation-forming commodities in Banten Province. Research methodology: The research is undergone by using the Vector Auto Regressive (VAR) model approach through testing of Impulse Response Function (IRF) and Variance Decomposition (VD). Results: The results show that the inflation rate in the goods/services commodity groups in Banten Province have a dynamic relationship between one another. The group itself dominates inflationary movements in all commodity groups. Monthly time series data is used for 2008-2018 time spans. Limitation: The linkages presented as the results are performed in commodity groups; thus, further research would inform more about inter-linkage between commodities. Contribution: The insight of knowing the pattern is beneficial for implication policy in regional inflation targeting especially in Banten Province. Keywords: Inflation, IRF, VAR, VD

Discovery of evolving companion from trajectory data streams

The widespread use of position-tracking devices leads to vast volumes of spatial–temporal data aggregated in the form of the trajectory data streams. Extracting useful knowledge from moving object trajectories can benefit many applications, such as traffic monitoring, military surveillance, and weather forecasting. Most of the knowledge gleaned from the trajectory data illustrates different kinds of group patterns, i.e., objects that travel together for some time. In the real world, the trajectory of the moving objects can change with time. Thus, existing approaches can miss a new pattern because they have a stringent requirement for moving object participators in a group movement pattern. To address this issue, we introduced a new type of moving object group pattern called an evolving companion. It allows some members of the group to leave and join anytime if some participators stay connected for all time intervals. In this pattern discovery, we model an incremental discovery solution to retrieve the evolving companion efficiently over the data stream. We evaluated the efficiency and effectiveness of our approach on two real vehicles and one synthetic dataset. Our method performed well compared with existing pattern discovery methods; for example, it was about 50% faster than Tang et al.’s buddy-based clustering method.

Are path choices of people moving through urban green spaces explained by gender and age? Implications for planning and management

Neighbourhood green space is an important asset for the urban population, providing valuable ecosystem services such as supporting human health. Distance or access to urban green areas is well established as being important for the potential use of the area but how different demographic groups move within greenspace is still somewhat unknown.Previous studies have shown that there are gender as well as age differences in visual perception, audio experience and recreational activities as well as estimated well-being experienced from use of urban green space. Here we explored people´s movement within their local green space in the city of Gothenburg, Sweden. The results showed that movement took place over large parts of the green spaces studied, not only along paths. The data further showed that the movement pattern differed significantly between men and women as well as between young adults and old adults. Movement patterns of demographic groups could provide support to planning for sustainable urban green areas, help avoid conflict, identify areas for solitude and ensure equal access for people of different gender and age.

Discovery of Loose Group Companion From Trajectory Data Streams

The general usability of location tracking devices has been generated a high volume of spatial-temporal data in the form of trajectory. Exploring useful knowledge from these trajectory data can contribute to understanding many real-world applications, such as traffic monitoring and weather forecasting. The main task of trajectory data analysis is the tracking of an object group movement pattern. Existing algorithms, studying the evolving structure of moving object trajectories, have high computational complexity, particularly when tracking loose group companions. To address this problem, we describe a loose group companion tracking framework over trajectory data streams in an incremental manner, which reduces computational time. Loose group companion is the moving objects group that travels together. However, some members are allowed to leave at some timestamp. A crucial part of our framework is the micro-group based loose group companion discovery. It follows a moving object group and then incrementally detects the loose group companions. We validated our techniques using two real vehicle data sets and one synthetic data set. Our approach was, on average, 45% faster than previous algorithms.

Group diagrams for representing trajectories

ABSTRACT Given the trajectories of one or several moving groups, we propose a new framework, the group diagram (GD) for representing these. Specifically, we seek a minimal GD as a concise representation of the groups maintaining the spatio-temporal structure of the groups’ movement. A GD is specified by three input values, namely a distance threshold, a similarity measure and a minimality criterion. For several variants of the GD, we give a comprehensive analysis of their computational complexity and present efficient approximation algorithms for their computation. Furthermore, we experimentally evaluate our algorithms on GPS data of migrating geese. Applying the proposed methods on these data sets reveals how the GD concisely represents the movement of the groups. This representation can be used for further analysis and for the formulation of new hypotheses for further ecological research, such as differences in movement patterns of groups on different surfaces or the shift of migration routes over several years. We use different similarity measures to summarize the migration routes of (i) a goose family for one migration period and to summarize (ii) the migration routes of one individual for several migration periods or (iii) the migration routes of several independent individuals for one migration period.

Deep Learning Resolves Representative Movement Patterns in a Marine Predator Species

The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research.

Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

Identifying group movement patterns of crowds and understanding group behaviors are valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

Impact of mass drug administration campaigns depends on interaction with seasonal human movement.

BackgroundMass drug administration (MDA) is a control and elimination tool for treating infectious diseases. For malaria, it is widely accepted that conducting MDA during the dry season results in the best outcomes. However, seasonal movement of populations into and out of MDA target areas is common in many places and could potentially fundamentally limit the ability of MDA campaigns to achieve elimination.MethodsA mathematical model was used to simulate malaria transmission in two villages connected to a high-risk area into and out of which 10% of villagers traveled seasonally. MDA was given only in the villages. Prevalence reduction under various possible timings of MDA and seasonal travel was predicted.ResultsMDA is most successful when distributed outside the traveling season and during the village low-transmission season. MDA is least successful when distributed during the traveling season and when traveling overlaps with the peak transmission season in the high-risk area. Mistiming MDA relative to seasonal travel resulted in much poorer outcomes than mistiming MDA relative to the peak transmission season within the villages.ConclusionsSeasonal movement patterns of high-risk groups should be taken into consideration when selecting the optimum timing of MDA campaigns.

Individual-level movement bias leads to the formation of higher-order social structure in a mobile group of baboons

In mobile social groups, influence patterns driving group movement can vary between democratic and despotic. The arrival at any single pattern of influence is thought to be underpinned by both environmental factors and group composition. To identify the specific patterns of influence driving travel decision-making in a chacma baboon troop, we used spatially explicit data to extract patterns of individual movement bias. We scaled these estimates of individual-level bias to the level of the group by constructing an influence network and assessing its emergent structural properties. Our results indicate that there is heterogeneity in movement bias: individual animals respond consistently to particular group members, and higher-ranking animals are more likely to influence the movement of others. This heterogeneity resulted in a group-level network structure that consisted of a single core and two outer shells. Here, the presence of a core suggests that a set of highly interdependent animals drove routine group movements. These results suggest that heterogeneity at the individual level can lead to group-level influence structures, and that movement patterns in mobile social groups can add to the exploration of both how these structures develop (i.e. mechanistic aspects) and what consequences they have for individual- and group-level outcomes (i.e. functional aspects).

Visualizing museum visitors’ behavior: Where do they go and what do they do there?

Museum curators and personnel are interested in understanding what is happening at their museum: what exhibitions and exhibits do visitors attend to, what exhibits visitors spend most time at, what hours of the day are most busy at certain areas in the museum and more. We use automatic tracking of visitors' position, movements and interaction at the museum to log visitor information. Using this information, we provide an interface that visualizes individual and small group movement patterns, presentations watched, and aggregated information of overall visitor engagement at the museum. We utilized a user centered design approach in which we gathered requirements, iteratively designed and implemented a working prototype and evaluated it with the help of domain experts (museum curators and other museum personnel). We describe our efforts and provide insights from the design and evaluation of our system, and outline how it might be generalized for other indoor domains such as supermarkets or shopping malls.

Bottlenose dolphin Tursiops truncatus group dynamics, site fidelity, residency and movement patterns in the Madeira Archipelago (North-East Atlantic)

The bottlenose dolphin Tursiops truncatus is one of the most frequently sighted cetacean species in the Madeira Archipelago (North-East Atlantic); however, little is known about its population ecology in these waters. Photo-identification undertaken during systematic, non-systematic and opportunistic surveys conducted between 2001 and 2012 was used to assess group dynamics, site fidelity, residency and movement patterns of bottlenose dolphins in the archipelago. Three different patterns of residency were assigned, based on individual sighting histories. Group size ranged between 2 and 90 individuals (median 12), with no significant monthly difference in group size. All resident dolphins formed a core, complex network with migrant and transient dolphins. In contrast, satellite clusters were formed exclusively by transient individuals. The lagged identification rate (LIR) model indicated that a dolphin remained in the area for an average of 90 days, whereas the average time an individual spent outside the study area was approximately 313 days. The Madeira Archipelago appears to be only a section of a much larger home range for this population, with the dolphins exhibiting three different patterns of occurrence that associate with each other. The information gained about this poorly studied population contributes to the overall knowledge of bottlenose dolphins in insular oceanic habitats.

Influence of Experience and Training on Dental Students’ Examination Performance Regarding Panoramic Images

Physician training has greatly benefitted from insights gained in understanding the manner in which experts search medical images for abnormalities. The aims of this study were to compare the search patterns of 30 fourth-year dental students and 15 certified oral and maxillofacial radiologists (OMRs) over panoramic images and to determine the most robust variables for future studies involving image visualization. Eye tracking was used to capture the eye movement patterns of both subject groups when examining 20 panoramic images classified as normal or abnormal. Abnormal images were further subclassified as having an obvious, intermediate, or subtle abnormality. The images were presented in random order to each participant, and data were collected on duration of the participants' observations and total distance tracked, time to first eye fixation, and total duration and numbers of fixations on and off the area of interest (AOI). The results showed that the OMRs covered greater distances than the dental students (p<0.001) for normal images. For images of pathosis, the OMRs required less total time (p<0.001), made fewer eye fixations (p<0.01) with fewer saccades (p<0.001) than the students, and required less time before making the first fixation on the AOI (p<0.01). Furthermore, the OMRs covered less distance (p<0.001) than the dental students for obvious pathoses. For investigations of images of pathosis, time to first fixation is a robust parameter in predicting ability. For images with different levels of subtlety of pathoses, the number of fixations, total time spent, and numbers of revisits are important parameters to analyze when comparing observer groups with different levels of experience.

Retraction: The role of compensatory movements patterns in spontaneous recovery after stroke.

[This retracts the article on p. 2671 in vol. 27.].