Analyzing raw data can be complex and time-consuming. However, visualizing this data through charts and graphs simplifies interpretation, making patterns more apparent. The addition of interactive elements further enhances usability, allowing users to explore data effortlessly.
Monitoring air quality in real time and predicting future trends helps both authorities and individuals make informed decisions. This dashboard leverages AI-powered analytics to provide accurate forecasts and historical trends.
This high-performance charting library is designed for WPF applications and offers rich data visualization capabilities. It supports various chart types, including spline and line charts, making it ideal for tracking real-time air pollution levels and forecasting future trends using AI-driven insights. With built-in interactivity features such as zooming, panning, and tooltips, users can explore complex datasets with ease.
This feature-rich mapping control enables dynamic geographical visualization of air quality data. It supports multiple projection types, shapefiles, and data binding, allowing users to analyze pollution levels across different regions. With interactive zooming, panning, and customizable markers, SfMap enhances spatial analysis by providing a clear and insightful representation of environmental data.
This demo showcases an AI-enhanced air pollution monitoring system built using Syncfusion WPF SfChart. It provides:
-
Real-time air quality visualization
-
AI-driven pollution predictions
-
Interactive charts for historical trends
-
Seamless data integration with external sources
Traditional monitoring relies on sensor networks and government reports, which may not always provide real-time insights. By integrating Azure OpenAI with Syncfusion WPF SfChart, our dashboard delivers instant and predictive air quality analytics, thereby improving decision-making for users.
If you are facing a path too long exception when building this example project, close Visual Studio and rename the repository to a shorter name before building the project.
For a step-by-step procedure, refer to the AI-Powered Air Pollution Monitor Dashboard Blog.