Spatio-Temporal Analysis for Smart City Data

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(From the article abstract)

The data gathered from smart cities can help citizens and city manager planners know where and when they should be aware of the repercussions regarding events happening in different parts of the city. Most of the smart city data analysis solutions are focused on the events and occurrences of the city as a whole, making it difcult to discern the exact place and time of the consequences of a particular event. We propose a novel method to model the events in a city in space and time. We apply our methodology for vehicular trafic data basing our models in (convolutional) neuronal networks.


The key contribution of this paper is to identify the spatio-temporal correlations in smart cities data streams and to include the correlation metrics in a suitable analysis of real world data streams.

Maria Bermudez-Edo & Payam Barnaghi

University of Granada, Granada, University of Surrey

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