Recent research suggests that, as in offline scenarios, spatial proximity increases the likelihood that two individuals establish an online social connection, and geographic closeness could therefore influence the formation of online communities. In this work we present a study of communities in two online social networks with location-sharing features and analyze their social and spatial properties. We study the places users visit to understand whether communities revolve around places or whether they exist independently. Our results suggest that community structure in social networks may arise from both social and spatial factors, so that exploiting information about the places where people go could benefit the definition of new community detection methods and community evolution models.

The popularity of the Web has allowed individuals to communicate and interact with each other on a global scale: people connect both to close friends and acquaintances, creating ties that can bridge otherwise separated groups of people. Recent evidence suggests that spatial distance is still affecting social links established on online platforms, with online ties preferentially connecting closer people. In this work we study the relationships between interaction strength, spatial distance and structural position of ties between members of a large-scale online social networking platform, Tuenti. We discover that ties in highly connected social groups tend to span shorter distances than connections bridging together otherwise separated portions of the network. We also find that such bridging connections have lower social interaction levels than ties within the inner core of the network and ties connecting to its periphery. Our results suggest that spatial constraints on online social networks are intimately connected to structural network properties, with important consequences for information diffusion.

Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to "check-in'' at geographical locations and share such experiences with their friends. Millions of "check-in'' records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user's social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user's check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user's check-in behavior. In particular, our model captures the property of user's check-in history in forms of power-law distribution and short-term effect, and helps in explaining user's check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user's check-ins and shows how social and historical ties can help location prediction.

Successful analysis of video data requires an integration of techniques from KR, Computer Vision, and Machine Learning. Being able to detect and to track objects as well as extracting their changing spatial relations with other objects is one approach to describing and detecting events. Different kinds of spatial relations are important, including topology, direction, size, and distance between objects as well as changes of those relations over time. Typically these kinds of relations are treated separately, which makes it difficult to integrate all the extracted spatial information. We present a uniform and comprehensive spatial representation of moving objects that includes all the above spatial/temporal aspects, analyse different properties of this representation and demonstrate that it is suitable for video analysis.

This paper presents an efficient topology information extraction algorithm that is capable of extracting primary topological relations, such as, interior, boundary, and exterior from a single spatial or spatio-temporal object stored in a linear constraint database. Any non-spatial constraints will be preserved so that the input spatio-temporal object’s temporal constraints will not be sacrificed by the algorithm. Based on the three primary topological relations, more topological relations between regions, lines, and points can be defined in a constraint database for future spatial analysis.

This paper investigates spatiotemporal interpolation methods for the application of air pollution assessment. The air pollutant of interest in this paper is fine particulate matter PM2.5. The choice of the time scale is investigated when applying the shape function-based method. It is found that the measurement scale of the time dimension has an impact on the interpolation results. Based upon the comparison between the accuracies of interpolation results, the most effective time scale out of four experimental ones was selected for performing the PM2.5 interpolation. The paper also evaluates the population exposure to the ambient air pollution of PM2.5 at the county-level in the contiguous U.S. in 2009. The interpolated county-level PM2.5 has been linked to 2009 population data and the population with a risky PM2.5 exposure has been estimated. The risky PM2.5 exposure means the PM2.5 concentration exceeding the National Ambient Air Quality Standards. The geographic distribution of the counties with a risky PM2.5 exposure is visualized. This work is essential to understanding the associations between ambient air pollution exposure and population health outcomes.

Video games are complex simulation environments with many real-world properties that need to be addressed in order to build robust intelligence. In particular, real-time strategy games provide a multi-scale challenge which requires both deliberative and reactive reasoning processes. Experts approach this task by studying a corpus of games, building models for anticipating opponent actions, and practicing within the game environment. We motivate the need for integrating heterogeneous approaches by enumerating a range of competencies involved in gameplay and discuss how they are being implemented in EISBot, a reactive planning agent that we have applied to the task of playing real-time strategy games at the same granularity as humans.

In order to collaborate with people in the real world, cognitive systems must be able to represent and reason about spatial regions in human environments. Consider the command "go to the front of the classroom". The spatial region mentioned (the front of the classroom) is not perceivable using geometry alone. Instead it is defined by its functional use, implied by nearby objects and their configuration. In this paper, we define such areas as context-dependent spatial regions and propose a method for a cognitive system to learn them incrementally by combining qualitative spatial representations, semantic labels, and analogy. Using data from a mobile robot, we generate a relational representation of semantically labeled objects and their configuration. Next, we show how the boundary of a context-dependent spatial region can be defined using anchor points. Finally, we demonstrate how an existing computational model of analogy can be used to transfer this region to a new situation.

In this paper, we construct and investigate a hierarchy of spatio-temporal formalisms that result from various combinations of propositional spatial and temporal logics such as the propositional temporal logic PTL, the spatial logics RCC-8, BRCC-8, S4u and their fragments. The obtained results give a clear picture of the trade-off between expressiveness and computational realisability within the hierarchy. We demonstrate how different combining principles as well as spatial and temporal primitives can produce NP-, PSPACE-, EXPSPACE-, 2EXPSPACE-complete, and even undecidable spatio-temporal logics out of components that are at most NP- or PSPACE-complete.

A big challenge for creating human-level game AI is building agents capable of operating in imperfect information environments. In real-time strategy games the technological progress of an opponent and locations of enemy units are partially observable. To overcome this limitation, we explore a particle-based approach for estimating the location of enemy units that have been encountered. We represent state estimation as an optimization problem, and automatically learn parameters for the particle model by mining a corpus of expert StarCraft replays. The particle model tracks opponent units and provides conditions for activating tactical behaviors in our StarCraft bot. Our results show that incorporating a learned particle model improves the performance of EISBot by 10% over baseline approaches.