As cities grow in size and number of inhabitants, continuous monitoring of the environmental impact of sound sources becomes essential for the assessment of the urban acoustic environments. This requires the use of management systems that should be fed with large amounts of data captured by acoustic sensors, mostly remote nodes that belong to a wireless acoustic sensor network. These systems help city managers to conduct data-driven analysis and propose action plans in different areas of the city, for instance, to reduce citizens’ exposure to noise. In this paper, unsupervised learning techniques are applied to discover different behavior patterns, both time and space, of sound pressure levels captured by acoustic sensors and to cluster them allowing the identification of various urban acoustic environments. In this approach, the categorization of urban acoustic environments is based on a clustering algorithm using yearly acoustic indexes, such as Lday, Levening, Lnight and standard deviation of Lden. Data collected over three years by a network of acoustic sensors deployed in the city of Barcelona, Spain, are used to train several clustering methods. Comparison between methods concludes that the k-means algorithm has the best performance for these data. After an analysis of several solutions, an optimal clustering of four groups of nodes is chosen. Geographical analysis of the clusters shows insights about the relation between nodes and areas of the city, detecting clusters that are close to urban roads, residential areas and leisure areas mostly. Moreover, temporal analysis of the clusters gives information about their stability. Using one-year size of the sliding window, changes in the membership of nodes in the clusters regarding tendency of the acoustic environments are discovered. In contrast, using one-month windowing, changes due to seasonality and special events, such as COVID-19 lockdown, are recognized. Finally, the sensor clusters obtained by the algorithm are compared with the areas defined in the strategic noise map, previously created by the Barcelona city council. The developed k-means model identified most of the locations found on the overcoming map and also discovered a new area.
To manage noise pollution, cities use monitoring systems over wireless acoustic sensor networks. These networks are mainly composed of fixed-location sound pressure level sensors deployed in outdoor sites of the city for long-term monitoring. However, due to high economic and human resource costs, it is not feasible to deploy fixed metering stations on every street in a city. Therefore, these continuous measurements are usually complemented with short-term measurements at different selected locations, which are carried out by acoustic sensors mounted on vehicles or at street level. In this research, the application of artificial neural networks is proposed for estimation of the long-term environmental acoustic pattern of a location based on the information collected during a short time period. An evaluation has been carried out through a comparison of eight artificial neural network architectures using real data from the acoustic sensor network of Barcelona, Spain, showing higher accuracy in prediction when the complexity of the model increases. Moreover, time slots with better performance can be detected, helping city managers to deploy temporal stations optimally.
Exposure to environmental noise is related to negative health effects. To prevent it, the city councils develop noise maps and action plans to identify, quantify, and decrease noise pollution. Smart cities are deploying wireless acoustic sensor networks that continuously gather the sound pressure level from many locations using acoustics nodes. These nodes provide very relevant updated information, both temporally and spatially, over the acoustic zones of the city. In this paper, the performance of several data clustering techniques is evaluated for discovering and analyzing different behavior patterns of the sound pressure level. A comparison of clustering techniques is carried out using noise data from two large cities, considering isolated and federated data. Experiments support that Hierarchical Agglomeration Clustering and K-means are the algorithms more appropriate to fit acoustics sound pressure level data.
Many cities around the world are deploying wireless sensor networks to capture information on different environmental parameters. Noise, as one of the main pollutants with negative effects on health and economy, is monitored through sound pressure level. In this work, the application of unsupervised clustering to sound pressure level data from a wireless acoustic sensors network (WASN) is proposed. Data from a sensor network deployed in the city of Madrid are used to show the usefulness of performing a clustering process with the aim of detecting different patterns of behavior of noise levels. The preliminary results obtained have allowed us to divide the city into several acoustic zones, which help city managers to propose improvement plans.
Una baja competencia percibida puede afectar el adecuado desarrollo de las clases de Educación Física, por lo que son necesarios instrumentos válidos y fiables con los que medir esta variable. En este estudio se describe la adaptación y validación al idioma español del cuestionario de Scrabis-Fletcher y Silverman (2010) para medir la Percepción de la Competencia en escolares de sexto curso de Educación Primaria. Han participado 780 escolares de sexto de primaria de 27 centros educativos de Albacete (España) elegidos de manera aleatoria, 389 niños y 391 niñas, edad de 10 a 13 años (media=11.08 y SD=0.43). Se ha realizado un análisis exploratorio de los ítems y un estudio de la consistencia interna mediante alfa de Cronbach, utilizando el paquete Multilevel 2.4. La estructura de los constructos se ha analizado mediante análisis factorial confirmatorio (AFC), utilizando el paquete Lavaan 0.5-11. La consistencia del instrumento ha sido elevada (alfa de Cronbach: 0.74). Existe una elevada correlación entre todos los ítems, incluso de distintos factores. Como conclusión se establecen dos cuestionarios de 2 y 3 factores con 7 y 14 ítems respectivamente, quedando validado el instrumento al contexto español.Palabras clave: competencia percibida; educación física; educación primaria; validez. ResumenCorrespondencia/correspondence: Pedro Gil-Madrona Universidad de Castilla la Mancha. España Email: Pedro.Gil@uclm.es A low perceived competence may affect the proper development of physical education lessons; hence, it is necessary to have valid and reliable instruments to measure this variable. In this study, it is described the adaptation and validation to the Spanish language of the questionnaire by Scrabis-Fletcher y Silverman (2010) to assess the perception of competence in schoolchildren of Primary Education. The sample was 780 schoolchildren from 27 randomly selected schools of Albacete (Spain), 389 boys and 391 girls, age 10-13 years (average=11.08 and SD=0.43). Exploratory analysis of the items and internal consistence study through Cronbach's Alpha were performed, using Multilevel package 2.4. The constructs structure was analyzed through factorial confirmatory analysis (FCA), using Lavaan package 0.5-11. The consistence of the instrument has been high (Cronbach's Alpha: 0.74). There is a high correlation between all items, even those from different factors. As a conclusion, two questionnaires of 2 and 3 factors, using 7 and 14 items respectively, were established and the instrument was validated to the Spanish context. Key words: perceived competence; physical education; primary educatio;validity. AbstractRecibido el 27 de agosto de 2016; Aceptado el 1 de marzo de 2017
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