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Communication Dans Un Congrès Année : 2017

Multiscale Spatio-Temporal Data Aggregation and Mapping for Urban Data Exploration

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.

Domaines

Géographie
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Dates et versions

hal-01897760 , version 1 (17-10-2018)

Identifiants

  • HAL Id : hal-01897760 , version 1

Citer

Etienne Come, Anaïs Remy. Multiscale Spatio-Temporal Data Aggregation and Mapping for Urban Data Exploration. ESANN 2017, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2017, Bruges, Belgium. pp 429-434. ⟨hal-01897760⟩
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