2013.11WhenBigDataMeetsBigSmog-ABigSpatio-temporal DataFrameworkforChinaSevereSmogAnalysis.pdf
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2013.11WhenBigDataMeetsBigSmog-ABigSpatio-temporal DataFrameworkforChinaSevereSmogAnalysis
When Big Data Meets Big Smog: A Big Spatio-temporal
Data Framework for China Severe Smog Analysis
Jiaoyan Chen
College of Computer Science,
Zhejiang University,
Yuquan Campus, Yugu Road,
Hangzhou, China
jiaoyanchen@
Huajun Chen
College of Computer Science,
Zhejiang University,
Yuquan Campus, Yugu Road,
Hangzhou, China
huajunsir@
Jeff Z. Pan
Department of Computing
Science,
The University of Aberdeen,
Aberdeen, UK
jeff.z.pan@abdn.ac.uk
Ming Wu
College of Computer Science,
East China University of
Science and Technology
Shanghai, China
ecustwm@
Ningyu Zhang
College of Computer Science,
Zhejiang University,
Yuquan Campus, Yugu Road,
Hangzhou, China
zhangningyu@
Guozhou Zheng
College of Computer Science,
Zhejiang University,
Yuquan Campus, Yugu Road,
Hangzhou, China
zzzgz@
ABSTRACT
Recently, the appearing disaster of severe smog has been
attacking many cities in China such as the capital Beijing.
The chief culprit of China smog, namely PM2.5, is affected
by various factors including air pollutants, weather, climate,
geographical location, urbanization, etc. To analyze the fac-
tors, we collect about 35,000,000 air quality records and
about 30,000,000 weather records from the sensors in 77
China’s cities in 2013. Moreover, two big data sets named
Geoname and DBPedia are also combined for the data of cli-
mate, geographical location and urbanization. To deal with
big spatio-temporal data for big smog analysis, we propose
a MapReduce-based framework named BigSmog. It mainly
conducts parallel correlation analysis of the factors and scal-
able training of artificial neural networks for spatio-temporal
approximation of the concentration of PM2.5. In the ex-
periments, BigSmog displays high scalability for big smog
analysis with big spatio-temporal data. The analysis result
shows that the air pollutants influence the short-term con-
centration of PM2.5 more than the weather and the factors of
geographical location and climate rather than urbanization
play a major role in deter
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