Diagnosing New York city's noises with ubiquitous data

By Yu Zheng, Tong Liu, Yilun Wang, Yanmin Zhu, Yanchi Liu, Eric Chang. 2014

227 citations recorded by the source corpus.

Subfields: Building and Construction, Computational Mathematics, Transportation

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In Building and Construction

In Computational Mathematics

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Citations in the corpus

Methodologies for Cross-Domain Data Fusion: An Overview (2015, methodology): “[87] infer the fine-grained noise situation by using 311 complaint data together with”
Tensors for Data Mining and Data Fusion (2016, background): “Zheng et al. [2014] analyze noise complaint data from New York City in order to identify the major sources of noise pollution in the city for different times of the day and the week.”
Big Data Meet Green Challenges: Big Data Toward Green Applications (2016, background): “[9] diagnosed the noise pollution in New York city using four data sources (complaint data,”
Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017, methodology): “…other trafﬁc related information including weather, social events, and road features, we utilize the context-aware matrix factorization schema, which can effectively integrate multiple related matrices to improve the performance of matrix factorization [Karatzoglou et al. 2010; Zheng et al. 2014b].”
Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data (2014, background): “Recently, Zheng et al. proposed a multi-source approach, based on human mobility and geographical data, to infer noise pollution [49] and gas consumption [29] in large metropolitan areas.”
Citywide traffic congestion estimation with social media (2015, methodology): “Road features are widely used for tra ﬃ c estimation and predictition [18, 25].”
Transfer Knowledge between Cities (2016, methodology): “The techniques capable of aligning embrace translation [22], canonical correlation analysis [6], matrix factorization [16], manifold alignment [37], coupled dictionary learning [31], and multimodal deep learning [18].”
Trajectory Data Mining (2015, methodology): “To and leave an event frequently, the gathering pattern [Zheng et al. 2013a, 2014a] further loses the constraints of the aforementioned patterns by allowing the membership of a group to evolve gradually.”
Urban Computing: Concepts, Methodologies, and Applications (2014, methodology): “Recent research [Hong and Zheng et al. 2014] has started to detect from a spatiotemporal graph some black holes (or volcanos), which represent regions with the streaming-in traf.c .ow much larger than that streaming out.”