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The rise of new sensor and communication technologies and the Internet of Things (IoT) have led to the availability of ever increasing amounts of data concerning the position, movement, and status of individuals in space and time. But individuals do not act in isolation, and much of this data only makes sense when referred to higher-level groupings or collectives, which can themselves be regarded as participants in actions, processes, and events. There is thus a pressing need for reliable and properly codified methods for deriving information at the collective level from masses of low-level spatio-temporal data concerning individuals. We need theories, tools, and techniques for handling the relationship between collectives and their members and the relationship between collectives and their environment, addressing such issues as the identification and classification of collectives, their spatio-temporal characteristics (e.g., movement, growth, topological dynamics) and how these are related to the movements of their constituent individuals, and the causal relations of collectives, both `horizontal' (i.e., between one collective and another) and `vertical' (between a collective and its members). Investigation of such phenomena is proceeding along a number of fronts but as yet we have no united theoretical framework and common vocabulary to support the development of tools and techniques for handling them. Increasingly, the problem is compounded by the fact that the source data from which high-level information is to be extracted is distributed amongst multiple, possibly heterogeneous, datasets.

This workshop aims to bring together researchers interested in the general area described above, including the following topics:

• representation techniques (e.g., Voronoi diagrams; lifelines, histories and trajectories)

1. •algorithms (e.g., spatial footprint generation and tracking, decentralized spatial
   

  algorithms, identification algorithms)

• formal theories (e.g., spatio-temporal calculi, process and event algebras, bigraphs)

• ontology (e.g., identification and classification of collectives and collective movement)

1. •simulation (e.g., can we come to understand the dynamics of collectives through
   

  simulations based on some plausible set of principles?)

1. •applications (e.g., emergency management, security, traffic management, behavior
   

  monitoring, environmental monitoring, ubiquitous computing, privacy)

1. •visualization and interaction (e.g., mobile and ubiquitous devices, natural language
   

  interaction, volunteered / crowd-sourced geographic information, ambient intelligence).