Spatial Interpolation Techniques on Participatory Sensing Data

2021 ◽  
Vol 7 (3) ◽  
pp. 1-32
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Spatial Interpolation ◽  
Predictive Accuracy ◽  
Region Of Interest ◽  
Noise Pollution ◽  
Pollution Monitoring ◽  
Participatory Sensing ◽  
Urban Noise ◽  
Map Generation ◽  
Interpolation Techniques ◽  
Iot Devices

Spatial distributions of data of natural phenomena can be estimated by using different spatial interpolation techniques. These techniques can be used for the purpose of developing urban noise pollution monitoring applications, so they can truly describe the actual urban noise pollution scenario of any region of interest to make effective and informed decisions. In this context, our aim is to use IoT-cloud based framework to generate dynamic (i.e., changes in terms of time and space) noise maps as a service with the help of spatial interpolation techniques. Noise map generation is an effective method for visualizing and assessing urban noise pollution. In this article, we have proposed three spatial interpolation techniques (GLIDW, I-GLIDW, GLIDW-OK) that work on participatory sensing-based noise pollution data collected using smartphones as IoT devices to generate dynamic noise maps. Proposed techniques can address diverse scenarios such as sparse datasets , high accuracy , better response time , and so on. Depending on the situation, we can choose an appropriate technique. We evaluate our proposed methods based on a real-world urban noise pollution dataset collected by participants over a period of two years in an urban area of the city Kolkata. The results are compared with inverse distance weighting (IDW) and Ordinary Kriging (OK) methods. The method GLIDW is proposed for a dense dataset. The results validate that in the case of a dense dataset GLIDW dominates over other methods. But, when the data sparsity level is medium, I-GLIDW performs well. However, if the dataset is very sparse, then GLIDW-OK dominates in terms of predictive accuracy. The results also show that Relative Improvement (RI) of I-GLIDW and GLIDW-OK is always positive compared to baseline methods IDW and OK.

A study of the accuracy of mobile technology for measuring urban noise pollution in large scale participatory sensing campaigns

2017 ◽  
Vol 117 ◽  
pp. 219-226 ◽  
Author(s):  
Pierre Aumond ◽  
Catherine Lavandier ◽  
Carlos Ribeiro ◽  
Elisa Gonzalez Boix ◽  
Kennedy Kambona ◽  
...  
Keyword(s):  
Mobile Technology ◽  
Large Scale ◽  
Noise Pollution ◽  
Participatory Sensing ◽  
Urban Noise

Developing an Innovative Urban Traffic Noise Monitoring System

2017 ◽  
Vol 21 ◽  
pp. 551-556 ◽  
Author(s):  
Ilinca Mirela Beca ◽  
Mihai Iliescu
Keyword(s):  
Monitoring System ◽  
Query Language ◽  
Road Traffic ◽  
Traffic Noise ◽  
Noise Pollution ◽  
Urban Traffic ◽  
Pollution Monitoring ◽  
Present Moment ◽  
Urban Noise ◽  
Public Data

The transportation policies focusing on minimizing the environmental impact aim at an improved quality of life and health of the population, the urban one in particular. Noise pollution is one of the biggest problems associated with the European urban environment at the present moment, mainly because of the ever-increasing road traffic. SUNET system (System for Urban Noise and Eco-Traffic) was designed to improve the management of the noise pollution in Cluj-Napoca and to provide up-to-date public data on a user-friendly interface. The implementation of the application over the entire municipality is highly advantageous as it creates a link between the town’s administration (the local authorities, the City Hall of Cluj-Napoca) and the user (the citizens) through this system which is dynamic, modern and compliant with the European regulations. The graphic interface uses the HTML (HyperText Markup Language) code, while the database is set up in an SQL (Structured Query Language) format and contains information about the characteristics of the system and users alike, all organized in specific tables. The need for an online urban noise pollution monitoring system, such as the SUNET one, appears to allow the provision of fairly realistic and up-to-date information and encourage the community to actively assist in the application of proper action plans and the decision-making process when it comes to the population’s exposure to high levels of noise pollution.

Approaches to Urban Noise Management in India

2012 ◽  
Author(s):  
Neelima S. Naik
Keyword(s):  
Urban Areas ◽  
Central Government ◽  
Environmental Concern ◽  
Noise Pollution ◽  
Noise Levels ◽  
Urban Noise ◽  
Master Plans ◽  
The Past ◽  
Metropolitan Cities

Noise pollution in urban areas is recognized as a major environmental concern in India. The lack of infrastructure and fast paced life in major metropolitan cities of India has made the urban environment extremely crowded, busy as well as noisy and as a result the millions of people living in the major metropolitan areas are suffering from the impacts of noise pollution. Noise levels are escalating at such a rate that it has become a major threat to the quality of human lives. Direct links between noise and health have been established by research conducted over the past few decades. There are several causes for urban degradation such as population migration, environmental considerations not adequately being incorporated into master plans, uncoordinated and haphazard development, weak implementation of plans and laws and inadequate institutional competences and resource crunch. This paper discusses the causal factors, impacts and the different approaches adopted by the Central Government as well as some major State Pollution Control Boards to curb the urban noise problem and the need for looking into non-conventional solutions such as Ecocity programme to bring in visible environmental improvement.

scholarly journals Mobile Application for Noise Pollution Monitoring through Gamification Techniques

2012 ◽  
pp. 562-571 ◽  
Author(s):  
Irene Garcia Martí ◽  
Luis E. Rodríguez ◽  
Mauricia Benedito ◽  
Sergi Trilles ◽  
Arturo Beltrán ◽  
...  
Keyword(s):  
Mobile Application ◽  
Noise Pollution ◽  
Pollution Monitoring

Urban Noise Inference Model Based on Multiple Views and Kernel Tensor Decomposition

2021 ◽  
pp. 2150027
Author(s):  
Junlan Nie ◽  
Ruibo Gao ◽  
Ye Kang
Keyword(s):  
Noise Pollution ◽  
Tensor Decomposition ◽  
Prediction Algorithm ◽  
Kernel Matrix ◽  
Multiple Views ◽  
Tensor Factorization ◽  
Urban Noise ◽  
Model Based ◽  
Factorization Model ◽  
Kernel Distance

Prediction of urban noise is becoming more significant for tackling noise pollution and protecting human mental health. However, the existing noise prediction algorithms neglected not only the correlation between noise regions, but also the nonlinearity and sparsity of the data, which resulted in low accuracy of filling in the missing entries of data. In this paper, we propose a model based on multiple views and kernel-matrix tensor decomposition to predict the noise situation at different times of day in each region. We first construct a kernel tensor decomposition model by using kernel mapping in order to speed decomposition rate and realize stable estimate the prediction system. Then, we analyze and compute the cause of the noise from multiple views including computing the similarity of regions and the correlation between noise categories by kernel distance, which improves the credibility to infer the noise situation and the categories of regions. Finally, we devise a prediction algorithm based on the kernel-matrix tensor factorization model. We evaluate our method with a real dataset, and the experiments to verify the advantages of our method compared with other existing baselines.

Sign in / Sign up

Export Citation Format

Share Document