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On the Analysis of Urban Computing Heterogeneous Data


This project aims to investigate scientific research in Urban Computing, where new methods for analyzing social sensing data (participatory sensing, social network analysis, and opportunistic sensing) and remote sensing in the context of urban environments will be proposed. Initially, we will investigate new ways of representing time-series collected through social sensing based on the representation of ordinal patterns. Then, we will propose novel metrics from the proposed representations to advance the state-of-the-art of data mining of time-series for Urban Computing data. In summary, this project presents the proposal for scientific research in four lines: (i) mining of time series for Urban Computing, (ii) analysis of social sensing and urban mobility, (iii) data analysis of vehicle networks, Internet of things and wireless sensor networks, and (iv) remote sensing image analysis for urban environments. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CARDOSO-PEREIRA, ISADORA; BORGES, JOAO B.; BARROS, PEDRO H.; LOUREIRO, ANTONIO F.; ROSSO, OSVALDO A.; RAMOS, HEITOR S. Leveraging the self-transition probability of ordinal patterns transition network for transportation mode identification based on GPS data. NONLINEAR DYNAMICS, v. 107, n. 1 NOV 2021. Web of Science Citations: 0.

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