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26 avril 2017
- handle: 10670/1.ag0edf
- hal: hal-01897760
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MULTI-SCALE AGGREGATION WEBMAPPINGSujets proches
Geographic atlases Atlases, Geographic Intelligence, Computational Nets, Neural (Computer science) Artificial neural networks Neural nets (Computer science) Networks, Neural (Computer science) Papers Plans Learning, Machine Biblical cosmogony Use ofCiter ce document
Etienne Come et al., « Multiscale Spatio-Temporal Data Aggregation and Mapping for Urban Data Exploration », HAL-SHS : géographie, ID : 10670/1.ag0edf
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Résumé
Maps seem the most intuitive way to visualize massive urban data but they also raise some well-known graphical problems (such as visual clutter, etc.). This paper focuses on processing massive spatio-temporal data in order to ease multi-scale exploration. To this end, we describe a preprocessing tool that enables the automatic creation of a multi-resolution grid from a high resolution grid of spatio-temporal data in a format compatible with webmapping applications (vector tiles). The use of this tool is exemplified through a prototype that offers the possibility to navigate into a massive itinerary request dataset collected in the Ile-de-France region.