This Streamlit dashboard visualizes insights from Indonesian schools and population data, providing an interactive interface to explore the datasets.

The dashboard includes:

1. Overview Tab: Shows key metrics and distributions of the data

   • Population grids and school counts
   • Average enrollment and population density
   • Distribution plots for key metrics

2. Population Tab: Analyzes population density patterns

   • Population density by province
   • Top populated grid cells
   • Geographic distribution insights

3. Schools Tab: Examines educational institutions

   • School distribution by status (public/private)
   • School levels and counts
   • Average enrollment by school level

4. Correlations Tab: Explores relationships between variables

   • School enrollment vs. population density
   • Student-teacher ratios by school level
   • Gender distribution in education

5. Anomalies Tab: Highlights unusual patterns

   • Schools with no students but teachers
   • Schools with students but no teachers
   • Schools with extremely high ratios
   • Largest schools by enrollment

1. Filters: Use the sidebar to select specific provinces for focused analysis
2. Tabs: Navigate between different analysis perspectives
3. Visualizations: Hover over charts for additional details
4. Data Tables: Scroll through detailed data tables for specific information

The dashboard uses:

• Streamlit for the web interface
• Pandas for data manipulation
• PyArrow for reading parquet files
• Matplotlib for visualizations
• Caching to improve performance

To run the dashboard locally:

pip install streamlit pandas pyarrow matplotlib seaborn
streamlit run dashboard_app.py

The dashboard will be available at http://localhost:8501

• data/popgrid.parquet: Contains population density data with province, regency, and location information
• data/sekolah.parquet: Contains school information including enrollment, staff, accreditation, and location data

The dashboard reveals several important patterns:

• High concentration of private schools in dense urban areas
• Significant variation in student-teacher ratios across school types
• Gender imbalances in teaching staff
• Geographic disparities in education infrastructure
• Population density correlations with school enrollment