Abstract
The aim is to integrate the processing and visualization of geographic data related to air pollution within a massive data architecture, to optimize processing and response times. Air pollutant data and climatic variables from Quito, Ecuador, measured by the Atmospheric Monitoring Network of the city, were used. After cleaning, Quito Air Pollution Index was calculated. The proposed architecture is open-source and is made up of a cluster with a master node and two worker-nodes. This cluster consists of a unified analysis computational system in Spark, managed by Yarn, and linked to a graphical interface provided by Zeppelin. It processes data and displays it visually through geographic maps. To verify that this architecture improves response times, a comparison was made between using the system and not using it. The geographic interpolation results were 4.52 s with a Geographical Information System, while the proposed system showed an execution time of 2.0 s, indicating a reduction of 56%. This architecture showed an improvement in the traditional interpolation and map visualization processes, and generated a new open-source alternative with resources and time optimization. In addition, this research work could contribute to the making of strategic decisions through a new way of analyzing environmental problems.
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Acknowledgements
We want to thank Francisco Gallegos and Brigitte Balón for their valuable ideas in the graphical interface integration into architecture. Since this article is derived from the postgraduate thesis of one of the authors, a special thanks to Lorena Recalde PhD. For her thesis writing review.
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Mora-Villacís, G., Calle-Jimenez, T. (2023). Big Data Architecture for Air Pollution Spatial Visualization: Quito, Ecuador. In: Maldonado-Mahauad, J., Herrera-Tapia, J., Zambrano-Martínez, J.L., Berrezueta, S. (eds) Information and Communication Technologies. TICEC 2023. Communications in Computer and Information Science, vol 1885. Springer, Cham. https://doi.org/10.1007/978-3-031-45438-7_5
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