Open AccessOrigin-Destination Data: a prototype and related scenarios
Author(s) -
Yussef Parcianello,
Nádia P. Kozievitch,
Keiko V. O. Fonseca,
Marcelo de Oliveira Rosa
Publication year - 2021
Publication title -
revista brasileira de computação aplicada
Language(s) - English
Resource type - Journals
ISSN - 2176-6649
DOI - 10.5335/rbca.v13i2.12351
Subject(s) - computer science , visualization , novelty , cluster analysis , context (archaeology) , perspective (graphical) , data visualization , data science , analytics , data mining , big data , creative visualization , presentation (obstetrics) , public transport , spatial contextual awareness , space (punctuation) , transport engineering , geography , machine learning , artificial intelligence , engineering , medicine , philosophy , theology , archaeology , radiology , operating system
The Public Transportation System and its operation management require the processing of large amount of data (like bus routes, user data and bus schedules). In particular, origin-destination data serves to indicate citizens’ travel patterns, providing insights related to the dynamic of the urban space occupation. Given this scenario, this paper presents a prototype of origin-destination data visualization, maintaining the spatial and temporal context. The novelty relies on visualization through clustering of georreferenced data, allowing the analysis of different regions of interests (neighborhood, regionals or mathematical regions using K-means algorithm). We demonstrate the prototype through several scenarios, and interviews done to local citizens.Challenges related to meaningful presentation of results are discussed under the perspective of visualization and analytics.