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Power BI Dashboard Ideas for Air Quality Dataset from Uci.edu

Recently we explained the dataset : Air Quality Measurements Dataset in one of our blog.

Here are some Power BI Dashboard Ideas for Air Quality Dataset from Uci.edu

Here are some Power BI dashboard ideas for your air quality dataset:

1. Overview Dashboard

Purpose: Provide a high-level summary of air quality trends.

  • Visuals:
    • Line chart: Show pollutant levels (e.g., CO, NOx, Benzene) over time.
    • Card visuals: Display average, max, and min values for key pollutants.
    • Map visualization: Display pollution hotspots based on geographic data (if available).
  • Filters:
    • Date and time slicer.
    • Dropdown to filter specific pollutants.

2. Pollutant Trends

Purpose: Analyze trends in pollutant levels over different time frames.

  • Visuals:
    • Line charts: Show temporal patterns for each pollutant (e.g., hourly, daily, monthly).
    • Stacked area chart: Compare multiple pollutants over time.
  • Filters:
    • Time frame (hour, day, week, month).
    • Pollutant selection.

3. Sensor Performance

Purpose: Evaluate sensor readings and their correlation with actual pollutant measurements.

  • Visuals:
    • Scatter plot: Sensor readings (e.g., PT08.S1(CO), PT08.S3(NOx)) vs. pollutant concentrations.
    • Heatmap: Correlation matrix between all measured features.
    • KPI cards: Highlight sensors with the highest accuracy.
  • Filters:
    • Pollutant type.
    • Specific sensors.

4. Air Quality Index (AQI) Analysis

Purpose: Present air quality in an easy-to-understand format.

  • Visuals:
    • Gauge chart: Show current AQI values based on pollutant thresholds.
    • Bar chart: Frequency distribution of AQI categories (Good, Moderate, Unhealthy, etc.).
    • Map: Display AQI levels by location (if geospatial data is available).
  • Filters:
    • AQI range filter.
    • Date and time slicer.

5. Missing Data Analysis

Purpose: Identify and address gaps in the dataset.

  • Visuals:
    • Bar chart: Frequency of missing values for each feature.
    • Line chart: Time-series plot highlighting missing data periods.
    • Table: Detailed records with missing data (-200 values).
  • Filters:
    • Feature selection.

6. Comparative Analysis

Purpose: Compare pollutant levels across different conditions (e.g., time, location).

  • Visuals:
    • Box plot: Compare pollutant distributions across different times of day or months.
    • Clustered bar chart: Compare pollutant levels across days of the week.
    • Pie chart: Contribution of each pollutant to overall air quality levels.
  • Filters:
    • Time period.
    • Pollutant type.

7. Health Impact Dashboard

Purpose: Highlight potential health impacts based on pollutant levels.

  • Visuals:
    • Conditional formatting table: Flag high pollutant values exceeding health thresholds.
    • Line chart: Track how often pollutants exceed safe levels.
    • Infographic: Visualize health impact zones (e.g., safe, caution, hazardous).
  • Filters:
    • Health threshold level.
    • Date and time slicer.

8. Interactive Storytelling

Purpose: Create a dynamic presentation for stakeholders.

  • Features:
    • Narrative visual: Add contextual insights for data trends.
    • Bookmarks: Show predefined scenarios, such as “highest pollution day.”
    • Animation: Demonstrate changes in air quality over time.