Spatial and Temporal Visualization of Environmental Air Quality Datasets: a Step-by-step Guide
Year: 2025
Type: article
Abstract: Harnessing the power of data visualization is crucial for representing the dynamic and multifactor environmental datasets concerning air quality on various scales. This guide illustrates a structured approach to spatial and temporal visualization, enabling a better understanding of air quality and pollution patterns. By integrating diverse data sources from monitoring stations, satellite imagery, and meteorological records—and employing geospatial and temporal techniques, this article can reveal pollution hotspots and their evolution over time. The spatial and temporal techniques and guidelines helps to understand how pollutant and air data changes, evolves, or behaves over different time scales and geographical regions. Emphasizing data visualization, this methodology empowers environmentalist, researchers, or person to identify trends, analyses correlations, and assess the impact of air pollution on public health and the environment. Through interactive dashboards and dynamic visualizations, it transforms raw data into a compelling narrative, fostering informed decisions for effective environmental management and public safety.
Authors: Kumar Pushkar, Dr. Mahua Banerjee
Institution: Birla Institute of Technology, Mesra
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410838559