Abstract
Abstract. The increased usage of the environmental monitoring system and sensors, installed on a day-to-day basis to explore information and monitor the cities’ environment and pollution conditions, are in demand. Sensor networking advancement with quality and quantity of environmental data has given rise to increasing techniques and methodologies supporting spatiotemporal data interactive visualisation analyses. Moreover, Visualisation (Vis) and Visual Analytics (VA) of spatiotemporal data have become essential for research, policymakers, and industries to improve energy efficiency, environmental management, and cities’ air pollution planning. A platform covering Vis and VA of spatiotemporal data collected from a city helps to portray such techniques’ potential in exploring crucial environmental inside, which is still required. Therefore, this work presents Vis and VA interface for the spatiotemporal data represented in terms of location, including time, and several measured attributes like Particular Matter (PM) PM2.5 and PM10, along with humidity, and wind (speed and direction) to assess the detailed temporal patterns of these parameters in Stuttgart, Germany. The time series are analysed using the unsupervised HDBSCAN clustering on a series of (above mentioned) parameters. Furthermore, with the in-depth sensors nature understanding and trends, Machine Learning (ML) approach called Transformers Network predictor model is integrated, that takes successive time values of parameters as input with sensors’ locations and predict the future dominant (highly measured) values with location in time as the output. The selected parameters variations are compared and analysed in the spatiotemporal frame to provide detailed estimations on how average conditions would change in a region over the time. This work would help to get a better insight into the urban system and enable the sustainable development of cities by improving human interaction with the spatiotemporal data. Hence, the increasing environmental problems for big industrial cities could be alarmed and reduced for the future with proposed work.
Highlights
The cities generate and store a lot of spatial and temporal information continuously using sensors that collect a large set of real-time spatial data stream and responses
The elevated levels of pollution parameters are incorporated with both local emission sources, and regional transportation (Chen and Zhao, 2011, Jasen et al, 2013)
The current study proposes HDBSCAN clustering and sensors nature monitoring queries with the following contributions: (i) interactive temporal visualisation of unsupervised cluster identifications to support the user in the interpretation of the meteorological and pollution parameters, (ii) predicting sensor nature using Transformers Network, supported with visualisation of designed model dynamic training, testing and accuracy metrics assessments
Summary
The cities generate and store a lot of spatial and temporal information continuously using sensors that collect a large set of real-time spatial data stream and responses. The surrounding air quality and well being fluctuate with these parameters atmospheric concentrations(Chen and Zhao, 2011). The elevated levels of pollution parameters are incorporated with both local emission sources, and regional transportation (Chen and Zhao, 2011, Jasen et al, 2013). Regional transportation with diesel vehicles are the primary sources of particular matters and contribute significantly to their levels (Wallace and Hobbs, 1977, Hardin and Kahn, 1999). Sensors’ (spatial and temporal) data is a combination of the georeferenced geographical entity represented in terms of location, dimensions, attributes, and time as continuous more extensive size data.
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