Scipediacontent: Scipediacontent moved page Draft Content 959985263 to Tse et al 2017a

2021-02-08T09:24:20Z

Scipediacontent moved page Draft Content 959985263 to Tse et al 2017a

Scipediacontent: Created page with " == Abstract == In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activitie..."

2021-02-08T09:24:17Z

Created page with " == Abstract == In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activitie..."

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== Abstract ==

In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activities. Consequently, valuable insights can be gained by applying data mining techniques to the data generated through social networks. In this work, a practical approach to combine data mining techniques with statistical analysis is proposed to implement crowd sensing in a smart city. A case study to analyze the relationship between weather conditions and traffic congestion in Beijing based on tweets posted on Sina Weibo platform is presented to demonstrate the proposed approach. Following the steps of raw dataset pre-processing, target dataset processing and statistical data analysis, analytic corpus containing tweets related to different weather conditions, traffic congestion and human outdoor activity is selected to test causal relationships by Granger Causality Test. The mediation analysis is also implemented to verify human outdoor activity as a mediator variable significantly carrying the influence of good weather to traffic congestion. The result demonstrates that outdoor activity serves as a mediator transmitting the effect of good weather on traffic congestion.

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/11585/617654 http://hdl.handle.net/11585/617654]

* [http://link.springer.com/content/pdf/10.1007/978-3-319-61949-1_37 http://link.springer.com/content/pdf/10.1007/978-3-319-61949-1_37],
: [http://dx.doi.org/10.1007/978-3-319-61949-1_37 http://dx.doi.org/10.1007/978-3-319-61949-1_37] under the license http://www.springer.com/tdm

* [https://link.springer.com/chapter/10.1007/978-3-319-61949-1_37 https://link.springer.com/chapter/10.1007/978-3-319-61949-1_37],
: [https://dblp.uni-trier.de/db/conf/goodtechs/goodtechs2016.html#TseZL016 https://dblp.uni-trier.de/db/conf/goodtechs/goodtechs2016.html#TseZL016],
: [https://doi.org/10.1007/978-3-319-61949-1_37 https://doi.org/10.1007/978-3-319-61949-1_37],
: [https://rd.springer.com/chapter/10.1007/978-3-319-61949-1_37 https://rd.springer.com/chapter/10.1007/978-3-319-61949-1_37],
: [https://academic.microsoft.com/#/detail/2735552782 https://academic.microsoft.com/#/detail/2735552782]

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Tse et al 2017a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 959985263 to Tse et al 2017a

Scipediacontent: Created page with " == Abstract == In recent years, the growing prevalence of social networks makes it possible to utilize human users as sensors to inspect city environment and human activitie..."