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Cities' gasps: Urban rhythms and development as shown by PM2.5 concentrations of Chinese cities

Jingyuan Cheng; Akash Sethi; Bing Wang; Yanpeng Ma; Paul Lukowicz
In: The 2015 Conference on Technologies and Applications of Artificial Intelligence. Conference on Technologies and Applications of Artificial Intelligence (TAAI-2015), November 20-22, Tainan, Taiwan, Province of China, Pages 533-540, ISBN 978-1-4673-9606-6, IEEE, 2015.


In this paper we present a novel point of view: checking large scale human activity (viz. cities' work/rest rhythm and the overall economical development) through one single and simple parameter (viz. PM2.5 value of air pollution). Instead of investing on a large number of sensors for individuals or into infrastructure and then analyzing the “big data”, this method utilizes just one phenomenon, the PM2.5, which is already the result of the large scale activity, thus frees the decision makers from implementing, installing and managing large scale wearable and/or ambient sensor networks and complex data storage and processing. Based on PM2.5 concentrations of 190 Chinese cities in 10.8 months, we not only built the cities' daily models which are related to collective activity rhythms, but also demonstrate the link between these models and the cites' economic mode and the overall productivity.

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