Spatiotemporal Co-occurrences Research Articles

Spatiotemporal-social association predicts immunological similarity in rewilded mice.

Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal co-occurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. In addition, we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.

Spatiotemporal co-occurrence of predators and prey in a neotropical mammal community in southern Mexico

AbstractPredator-prey interactions are one of the central themes in ecology due to their importance as a key mechanism in structuring biotic communities. In the predator-prey systems, the behaviours of persecution and avoidance impact on the ecosystem dynamics as much as the trophic interactions. We aimed to analyse the spatiotemporal co-occurrences between prey and predators in a community of medium- and large-sized mammals in southern Mexico. We predict prey will avoid sites where a predator previously passed. Contrarily, we expect a search behaviour by predators and a synchronization in activity patterns among them. We found prey does not occur either in time or space where predators have passed, suggesting an avoidance behaviour. Contrary to our expectations, we did not find significant search behaviours from predators to prey. Synchronization in the daily temporal overlap between predators was higher (Δ = 0.77–0.82) than with their prey (Δ = 0.43 – 0.81). The results suggest prey perceives the risk of predation and displays avoidance behaviour both spatially and temporally, which is consistent with the fear theory. This study provides a complementary approach to understanding the behaviour mechanism between predators and prey through camera-trapping or similar data of spatiotemporal co-occurrences.

RelationLines

The increased accessibility of urban sensor data and the popularity of social network applications is enabling the discovery of crowd mobility and personal communication patterns. However, studying the egocentric relationships of an individual can be very challenging because available data may refer to direct contacts, such as phone calls between individuals, or indirect contacts, such as paired location presence. In this article, we develop methods to integrate three facets extracted from heterogeneous urban data (timelines, calls, and locations) through a progressive visual reasoning and inspection scheme. Our approach uses a detect-and-filter scheme such that, prior to visual refinement and analysis, a coarse detection is performed to extract the target individual and construct the timeline of the target. It then detects spatio-temporal co-occurrences or call-based contacts to develop the egocentric network of the individual. The filtering stage is enhanced with a line-based visual reasoning interface that facilitates a flexible and comprehensive investigation of egocentric relationships and connections in terms of time, space, and social networks. The integrated system, RelationLines, is demonstrated using a dataset that contains taxi GPS data, cell-base mobility data, mobile calling data, microblog data, and point-of-interest (POI) data from a city with millions of citizens. We examine the effectiveness and efficiency of our system with three case studies and user review.

Measuring the Significance of Spatiotemporal Co-Occurrences

Spatiotemporal co-occurrences are the appearances of spatial and temporal overlap relationships among trajectory-based spatiotemporal instances with region-based geometric representations. Assessing the significance of spatiotemporal co-occurrences plays an important role in the spatiotemporal frequent pattern mining applications of moving region objects. A spatiotemporal version of the popular Jaccard measure has been used for measuring the strength of spatiotemporal co-occurrences. We will demonstrate the shortcomings of the Jaccard ( J ) measure when it is used for assessing the significance of co-occurrences among spatiotemporal instances with highly different spatiotemporal evolution characteristics. We will present two extended novel measures ( J + and J * ) that address the problems linked to the J measure. Our work includes algorithms for the significance measure calculations, the proofs and explanations about the key properties of measures, and a detailed experimental evaluation section. Our experiments include in-depth relevancy and running time analyses demonstrating the suitability of our proposed measures for spatiotemporal frequent pattern mining algorithms.

Mining spatiotemporal co-occurrence patterns in non-relational databases

Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of feature types whose instances are frequently co-occurring both in space and time. Spatiotemporal co-occurrences reflect the spatiotemporal overlap relationships among two or more spatiotemporal instances both in spatial and temporal dimensions. STCOPs can be potentially used to predict and understand the generation and evolution of different types of interacting phenomena in various scientific fields such as astronomy, meteorology, biology, geosciences. Meaningful and statistically significant data analysis for these scientific fields requires processing sufficiently large datasets. Due to the computationally expensive nature of spatiotemporal operations required for mining spatiotemporal co-occurrences, it is increasingly difficult to identify spatiotemporal co-occurrences and discover STCOPs in centralized system settings. As a solution, we developed a cloud-based distributed mining system for discovering STCOPs. Our system uses Accumulo, a column-oriented non-relational database management system as its backbone. In order to efficiently mine the STCOPs, we propose three data models for managing trajectory-based spatiotemporal data in Accumulo. We introduce an in-memory join-index structure and a join algorithm for effectively performing spatiotemporal join operations on spatiotemporal trajectories in non-relational databases. Lastly, with the experiments with artificial and real life datasets, we evaluate the performance of the proposed models for STCOP mining.

Co-occurrence prediction in a large location-based social network

Location-based social network (LBSN) is at the forefront of emerging trends in social network services (SNS) since the users in LBSN are allowed to "check-in" the places (locations) when they visit them. The accurate geographical and temporal information of these check-in actions are provided by the end-user GPS-enabled mobile devices, and recorded by the LBSN system. In this paper, we analyze and mine a big LBSN data, Gowalla, collected by us. First, we investigate the relationship between the spatio-temporal co-occurrences and social ties, and the results show that the co-occurrences are strongly correlative with the social ties. Second, we present a study of predicting two users whether or not they will meet (co-occur) at a place in a given future time, by exploring their check-in habits. In particular, we first introduce two new concepts, bag-of-location and bag-of-time-lag, to characterize user's check-in habits. Based on such bag representations, we define a similarity metric called habits similarity to measure the similarity between two users' check-in habits. Then we propose a machine learning formula for predicting co-occurrence based on the social ties and habits similarities. Finally, we conduct extensive experiments on our dataset, and the results demonstrate the effectiveness of the proposed method.

Network analysis of acoustic tracking data reveals the structure and stability of fish aggregations in the ocean

Aggregations in the distribution of individuals are an almost universal phenomenon in living organisms. Groups of animals that display collective coordinated movement without forming stable social bonds such as fish schools are a special type of aggregation. In tropical tuna fisheries, aggregating behaviour is directly exploited through the use of artificial fish aggregating devices (FADs). Hence, understanding the dynamics of schooling behaviour and the potential impacts of FADs upon it may have ramifications for tuna management. As a novel way of quantifying spatiotemporal co-occurrences of animals, we applied network statistics to acoustic tracking data to identify the co-occurrences of individual yellowfin tuna, Thunnus albacares , in an array of FADs and determine the frequency and temporal dynamics of these co-occurrences. We observed large interannual variation in movement rates of tuna between FADs, and corresponding interannual variability in the mean number of spatiotemporal associates for each individual as well as the temporal stability of associations. When movement rates were high, associations within FAD aggregations decayed to randomness three times faster than when movement rates were lower. This raises the possibility that if FADs are sufficiently close for fish to perform frequent between-FAD movements, school mixing may be increased and cohesion reduced. ► Network analysis is suited to tracking data. ► Increased movement of tuna between FADs lowers social cohesion. ► High FAD density may increase school mixing.

Social Network Discovery by Mining Spatio-Temporal Events

Knowing patterns of relationship in a social network is very useful for law enforcement agencies to investigate collaborations among criminals, for businesses to exploit relationships to sell products, or for individuals who wish to network with others. After all, it is not just what you know, but also whom you know, that matters. However, finding out who is related to whom on a large scale is a complex problem. Asking every single individual would be impractical, given the huge number of individuals and the changing dynamics of relationships. Recent advancement in technology has allowed more data about activities of individuals to be collected. Such data may be mined to reveal associations between these individuals. Specifically, we focus on data having space and time elements, such as logs of people's movement over various locations or of their Internet activities at various cyber locations. Reasoning that individuals who are frequently found together are likely to be associated with each other, we mine from the data instances where several actors co-occur in space and time, presumably due to an underlying interaction. We call these spatio-temporal co-occurrences events, which we use to establish relationships between pairs of individuals. In this paper, we propose a model for constructing a social network from events, and provide an algorithm that mines these events from the data. Experiments on a real-life data tracking people's accesses to cyber locations have also yielded encouraging results.