Details on article
|Author||Salinas K.; Goncalves T.; Barella V.; Vieira T.; Nonato L.G.
|Title||CityHub: A Library for Urban Data Integration|
Salinas K.; Goncalves T.; Barella V.; Vieira T.; Nonato L.G. CityHub: A Library for Urban Data Integration,Proceedings - 2022 35th Conference on Graphics, Patterns, and Images, SIBGRAPI 2022
|Keywords||Data visualization; Graph theory; Visualization; 'current; Analytic tools; Datatypes; Features vector; Integrated data; Multiple data sources; Spatial domains; Street graph; Urban data; Visual analytics; Data integration
|Link to article|| https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146440282&doi=10.1109%2fSIBGRAPI55357.2022.9991775&partnerID=40&md5=37c7a52c8e276e85a05ef20c716521a6
|Abstract||The current availability of urban big data provides novel opportunities to perform data-rich urban planning and to handle a variety of issues in a city. However, the way multiple data sources are provided, represented and integrated is one of the main challenges to developing intelligent urban analytic tools. In this context, we present CityHub, a library to handle multiple urban datasets. Specifically, CityHub integrates distinct urban data types in a layer-based architecture that considers four different types of layers. The integrated data is preprocessed to a common spatial domain: the set of nodes of a city street graph. The resulting data structure may be easily used to export feature vectors associated to the nodes of a street graph, enabling a multitude of analytical procedures. To demonstrate the potential of the proposed library, we propose an interactive visualization tool that may be used to perform several visual analytic tasks. We also present cases studies to demonstrate how the proposed library and visualization tool can be used to uncover complex urban data patterns in the city of São Paulo. © 2022 IEEE.