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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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A spatio-temporal noise map completion method based on crowd-sensing.

Min Huang¹, Lina Chen¹, Yilin Zhang¹

¹School of Software Engineering, South China University of Technology, Guangzhou, China.

Environmental Pollution (Barking, Essex : 1987)

|November 25, 2020

Summary

This summary is machine-generated.

This study introduces a new method for creating urban noise maps using crowd-sensing and a Latent Factor Model (LFM). The Spatial-Temporally Related LFM (STR-LFM) improves accuracy and stability, especially with sparse data.

Keywords:

Crowd-sensing Map completion Noise map Urban noise

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Area of Science:

Environmental Science
Urban Planning
Geographic Information Systems

Background:

Traditional noise map construction is costly and infrequent, hindering urban sustainability and public health.
Effective noise mapping requires frequent updates and cost-efficient methods.

Purpose of the Study:

To develop a novel noise map completion method addressing data sparseness.
To enhance the accuracy and stability of noise maps using crowd-sensing data.

Main Methods:

Proposed Spatial-Temporally Related Latent Factor Model (STR-LFM) integrating crowd-sensing.
Utilized geographic features (POI, road networks, building outlines) and temporal correlations.
Employed k-nearest neighbor algorithm for data imputation and weighted fusion.

Main Results:

STR-LFM demonstrated lower recovery error compared to existing methods.
The method showed superior stability in handling varying levels of data sparseness.

Conclusions:

STR-LFM offers a more efficient and accurate approach to noise map completion.
This method supports urban sustainability and resident health through improved noise monitoring.