Uma abordagem para a investigação de padrões de movimento e de comportamento de indivíduos empregando análise visual de Predictive Suffix Trees

• Ano de defesa: 2019
• Autor(a) principal: Leite Júnior, Antonio José Melo
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Predictive Suffix Tree; Análise visual; Sensemaking; Branching time; Padrões de movimento; Padrões de comportamento
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/47040

Predictive Suffix Trees (PSTs) are data structures capable of simultaneously represent space, time, and probability. They can be used to predict when a person would leave her current position to move to a new probable location. Although they are usually complex to read, PSTs can help crime investigation; management road traffic; or location-based advertising, for example. This work proposes the application of visual analytics to simplify the task of finding movement patterns and possible behaviors of a person, using data stored in PSTs. For that, we introduce an approach that applies sensemaking and branching time to provide a less abstract character to PSTs, allowing analysts to explore the dynamics of space-time combinations (space, time and space versus time relations) considering probabilities. To validate the proposed solution, we developed a visualization tool and performed three distinct user studies, with a total of 77 participants and two different datasets. The obtained results demonstrated the feasibility of applying the solution, allowing expert and non-experts to solve initial problems, but letting they propose their own questions to find new answers.