What makes New York so noisy? Reasoning noise pollution by mining multimodal geo-social big data

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

Noise pollution in modern cities is getting worse and sound sensors are sparse and costly, but it is highly demanded to have a system that can help reason and present the noise pol-lution at any region in urban areas. In this work, we leverage multimodal geo-social media data on Foursquare, Twitter, Flickr, and Gowalla in New York City, to infer and visualize the volume and the composition of noise pollution for ev-ery region in NYC. Using NYC 311 noise complaint records as the approximation of noise pollution for validation, we develop a joint inference and visualization system that inte-grates multimodal features, including geographical, mobil-ity, visual, and social, with a graph-based learning model to infer the noise compositions of regions. Experimental re-sults show that our model can achieve promising results with substantially few training data, compared to state-of-The-Art methods. A NYC Urban Noise Diagnotor system is devel-oped and allowed users to understand the noise composition of any region of NYC in an interactive manner.

原文English
主出版物標題MM 2015 - Proceedings of the 2015 ACM Multimedia Conference
發行者Association for Computing Machinery, Inc
頁面181-184
頁數4
ISBN(電子)9781450334594
DOIs
出版狀態Published - 2015 十月 13
事件23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
持續時間: 2015 十月 262015 十月 30

出版系列

名字MM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Other

Other23rd ACM International Conference on Multimedia, MM 2015
國家/地區Australia
城市Brisbane
期間15-10-2615-10-30

All Science Journal Classification (ASJC) codes

  • 媒體技術
  • 電腦繪圖與電腦輔助設計
  • 電腦視覺和模式識別
  • 軟體

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