MobApp: A Data Visualization Tool for Trajectory Analysis
Resumo
In this paper, we present a demo called MobApp, a data visualization application to facilitate spatio-temporal trajectories analysis. It aims to help domain analysts and practitioners/scientists to analyze and get insights from real-world trajectories. The tool supports: (1) exploratory analysis of trajectories, which allows users to visualize selected trajectories on a map and provides some statistics about trajectories’ points; (2) visualization of anomalous trajectories and the regions where the anomalies occur, detected by an anomaly detection model; and (3) evaluation of anomaly detection models to compare their performance.
Palavras-chave:
Data visualization, trajectory analysis
Referências
Belhadi, A., Djenouri, Y., Lin, J. C.-W., and Cano, A. (2020). Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges. ACM Transactions on Management Information Systems (TMIS), 11(3):1–29.
Bessa, A., Silva, F. d. M., Nogueira, R. F., Bertini, E., and Freire, J. (2016). Riobusdata: Outlier detection in bus routes of rio de janeiro. arXiv preprint arXiv:1601.06128.
Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems.
Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E. K., Swayne, D. F., and Wickham, H. (2009). Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1906):4361–4383.
Chen, C., Zhang, D., Castro, P. S., Li, N., Sun, L., Li, S., and Wang, Z. (2013). iboat: Isolation-based online anomalous trajectory detection. IEEE Transactions on Intelligent Transportation Systems, 14(2):806–818.
Cruz, M. and Barbosa, L. (2020). Learning gps point representations to detect anomalous bus trajectories. IEEE Access, 8:229006–229017.
Liu, K., Gao, S., Qiu, P., Liu, X., Yan, B., and Lu, F. (2017). Road2vec: Measuring traffic interactions in urban road system from massive travel routes. ISPRS International Journal of Geo-Information, 6(11):321.
Liu, Y., Zhao, K., Cong, G., and Bao, Z. (2020). Online anomalous trajectory detection with deep generative sequence modeling. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 949–960. IEEE.
Pappalardo, L., Simini, F., Barlacchi, G., and Pellungrini, R. (2019). scikit-mobility: A python library for the analysis, generation and risk assessment of mobility data. arXiv preprint arXiv:1907.07062.
Bessa, A., Silva, F. d. M., Nogueira, R. F., Bertini, E., and Freire, J. (2016). Riobusdata: Outlier detection in bus routes of rio de janeiro. arXiv preprint arXiv:1601.06128.
Bose, R. (2009). Advanced analytics: opportunities and challenges. Industrial Management & Data Systems.
Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E. K., Swayne, D. F., and Wickham, H. (2009). Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367(1906):4361–4383.
Chen, C., Zhang, D., Castro, P. S., Li, N., Sun, L., Li, S., and Wang, Z. (2013). iboat: Isolation-based online anomalous trajectory detection. IEEE Transactions on Intelligent Transportation Systems, 14(2):806–818.
Cruz, M. and Barbosa, L. (2020). Learning gps point representations to detect anomalous bus trajectories. IEEE Access, 8:229006–229017.
Liu, K., Gao, S., Qiu, P., Liu, X., Yan, B., and Lu, F. (2017). Road2vec: Measuring traffic interactions in urban road system from massive travel routes. ISPRS International Journal of Geo-Information, 6(11):321.
Liu, Y., Zhao, K., Cong, G., and Bao, Z. (2020). Online anomalous trajectory detection with deep generative sequence modeling. In 2020 IEEE 36th International Conference on Data Engineering (ICDE), pages 949–960. IEEE.
Pappalardo, L., Simini, F., Barlacchi, G., and Pellungrini, R. (2019). scikit-mobility: A python library for the analysis, generation and risk assessment of mobility data. arXiv preprint arXiv:1907.07062.
Publicado
25/09/2023
Como Citar
CRUZ, Michael O.; NETO, Fernando; BARBOSA, Luciano.
MobApp: A Data Visualization Tool for Trajectory Analysis. In: DEMONSTRAÇÕES E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 96-101.
DOI: https://doi.org/10.5753/sbbd_estendido.2023.233392.
