Enhancing Exploratory Analysis across Multiple Levels of Detail of Spatiotemporal Events

João Moura Pires (superv.), Universidade NOVA de Lisboa, May 2017.
Keywords: Spatiotemporal events, granularity, level of detail, visual analytics

Abstract:Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its spatial location, time and related attributes are known with high levels of detail (LoDs). The LoD of analysis plays a crucial role in the user’s perception of phenomena. From one LoD to another, some patterns can be easily perceived or different patterns may be detected, thus
requiring modeling phenomena at different LoDs as there is no exclusive LoD to study them.

Granular computing emerged as a paradigm of knowledge representation and processing, where granules are basic ingredients of information. These can be arranged in a hierarchical alike structure, allowing the same phenomenon to be perceived at different LoDs. This PhD Thesis introduces a formal Theory of Granularities (ToG) in order to have granules defined over any domain and reason over them. This approach is more general than the related literature because these appear as particular cases of the proposed ToG. Based on this theory we propose a granular computing approach to model spatiotemporal phenomena at multiple LoDs, and called it a granularities-based model. This approach stands out from the related literature because it models a phenomenon through statements rather than just using granules to model abstract real-world entities. Furthermore, it formalizes the concept of LoD and follows an automated approach to generalize a phenomenon from one LoD to a coarser one.

Present-day practices work on a single LoD driven by the users despite the fact that the identification of the suitable LoDs is a key issue for them. This PhD Thesis presents a framework for SUmmarizIng spatioTemporal Events (SUITE) across multiple LoDs. The SUITE framework makes no assumptions about the phenomenon and the analytical task. A Visual Analytics approach implementing the SUITE framework is presented, which allow users to inspect a phenomenon across multiple LoDs, simultaneously, thus helping
to understand in what LoDs the phenomenon perception is different or in what LoDs patterns emerge.

Presentation and PhD Thesis Document

Videos

SUITE Basic Features (Video 1)

Objective: To show the basic features of the SUITE-VA tool.
Dataset: Robberies in the City of Chicago between 2001 and 2015
Description: This video presents a first video about the three main areas of the Visual Analytics SUITE tool (SUITE-VA) that are coordinated with each other.
Observations: You must be familiar with the concepts of the SUITE Framework, Granularity, LoD, and Spatiotemporal LoD used in the PhD Thesis.

 

SUITE Basic Features (Video 2)

Objective: To show the interaction involving the Spatial Abstracts and the corresponding Compact Spatial Abstracts.
Dataset: Robberies in the City of Chicago between 2001 and 2015
Description: This video presents a second video about the three main areas of the Visual Analytics SUITE tool (SUITE-VA) that are coordinated with each other.
Observations: You must be familiar with the concepts of the SUITE Framework, Granularity, LoD, and Spatiotemporal LoD used in the PhD Thesis.

 

SUITE Basic Features (Video 3)

Objective: To show the interaction involving the Temporal Abstracts and the corresponding Compact Temporal Abstracts.
Dataset: Robberies in the City of Chicago between 2001 and 2015
Description: This video presents a third video about the three main areas of the Visual Analytics SUITE tool (SUITE-VA) that are coordinated with each other.
Observations: You must be familiar with the concepts of the SUITE Framework, Granularity, LoD, and Spatiotemporal LoD used in the PhD Thesis.

 

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