PathsViewer: Uma Interface para Exploração de Dados Espaço-Temporais

• Arthur J. Simas UNICAMP
• William Q. Melo UNICAMP
• Leonardo H. Guimarães UNICAMP
• Christian E. Rothenberg UNICAMP

DOI: https://doi.org/10.5753/sbrc_estendido.2023.758

Abstract

Spatial-temporal data represents the occupation of objects in space at a given moment in time. An interface for visualizing this type of data is essential in movement patterns analysis and object monitoring. However, despite the great interest in research on this type of data, there are few tools for visualizing object trajectories. In this sense, this article proposes PathsViewer, a tool for visualizing spatial-temporal data. To evaluate the proposed tool, datasets from different sources were used, such as georeferenced 5G traces, and vehicle trajectories.

References

Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., and Rabuffi, A. (2014).

CRAWDAD dataset roma/taxi (v. 2014-07-17). Downloaded from [link].

Chittaro, L., Ranon, R., and Leronutti, L. (2006). VU-Flow: A visualization tool for analyzing navigation in virtual environments. IEEE Transactions on Visualization and Computer Graphics, 12:1475–1485.

Dias, D. and Costa, L. H. M. K. (2018). CRAWDAD dataset coppe-ufrj/RioBuses (v. 2018-03-19). Downloaded from [link].

Scheepens, R., Hurter, C., Wetering, H. V. D., and Wijk, J. J. V. (2016). Visualization, Selection, and Analysis of Traffic Flows. IEEE Transactions on Visualization and Computer Graphics, 22:379–388.

Sobral, T., Galvão, T., and Borges, J. (2019). Visualization of urban mobility data from intelligent transportation systems. Sensors (Switzerland), 19.

Ul Mustafa, R., Esteve Rothenberg, C., and Barakat, C. (2022). YouTube goes 5G: Benchmarking YouTube in 4G vs 5G.

Wang, X., Zhou, X., and Lu, S. (2000). Spatiotemporal data modelling and management: A survey. Proceedings of the Conference on Technology of Object-Oriented Languages and Systems, TOOLS, pages 202–211.