> ## Documentation Index > Fetch the complete documentation index at: https://docs.nikaplanet.com/llms.txt > Use this file to discover all available pages before exploring further. # River Urbanisation With Major Cities Comparisons > Process satellite imagery to analyze urban development patterns across multiple regions with statistical summaries and comparison plots # River Urbanisation With Major Cities Comparisons River Urbanisation With Major Cities Comparisons ## River Basin Urban Development Analyzer Processes satellite imagery (built-up, water, NDVI) to analyze urban development patterns across multiple regions, generating statistical summaries, comparison plots, and detailed land use classifications. ## Requirements | Requirement | Details | | ------------------- | ------------------------------------------------------------------------- | | **Software** | Python 3.x, rasterio, matplotlib, pandas, numpy | | **Data Sources** | Pre-downloaded satellite imagery (Built-up, Water, NDVI, Satellite) | | **Python Packages** | rasterio, numpy, pandas, matplotlib, seaborn, geopandas, shapely, pathlib | ## Input Data TIF files for each region: `*_buildup.tif`, `*_water.tif`, `*_ndvi.tif`, `*_satellite.tif` ## Files Created | Filename | Description | Format | | ------------------------------------ | -------------------------------------------- | ------------- | | **region\_summary.csv** | Statistical summary for all analyzed regions | CSV | | **comparison\_analysis.png** | 4-panel comparison plots across all regions | PNG (300 DPI) | | **{region}\_detailed\_analysis.png** | Detailed 4-panel analysis for each region | PNG (300 DPI) | | **analysis\_report.md** | Comprehensive markdown report with insights | Markdown | ## High-Level Workflow Steps ### 1. Region Discovery & Data Loading Auto-discover regions from `*_buildup.tif` files in data directory Load corresponding water, NDVI, and satellite imagery for each region Handle nodata values and coordinate transformations ### 2. Statistical Analysis Calculate built-up area coverage and intensity (hectares & percentages) Analyze water occurrence (permanent vs seasonal water bodies) Compute vegetation indices (NDVI-based green cover analysis) Generate composite land use statistics ### 3. Urban Classification * **Dense Urban**: >50% built-up coverage * **Moderate Urban**: 25-50% built-up coverage * **Low Urban**: 10-25% built-up coverage * **Rural/Natural**: \<10% built-up coverage ### 4. Visualization Generation **Comparison Plots**: 4-panel matplotlib figure with scatter plots, bar charts, and pie charts **Regional Details**: Individual 4-panel analysis showing built-up, water, NDVI, and land use classification **Static PNG Output**: High-resolution images suitable for reports and publications ### 5. Report Generation Regional rankings (most urbanized, highest water coverage, most vegetated) Key insights and development patterns Comprehensive data tables with all calculated metrics ## Output Analysis Capabilities This script generates comprehensive urban development assessment using: * **Built-up Intensity Maps** - Identifies urban development patterns and density * **Water Occurrence Analysis** - Distinguishes permanent vs seasonal water bodies * **Vegetation Health (NDVI)** - Maps green cover and vegetation density * **Land Use Classification** - Composite analysis of urban, water, vegetation, and other land uses ## Visualization Outputs ### Comparison Analysis (4-Panel Plot) * **Built-up vs Water Coverage Scatter** - Shows relationship between urbanization and water presence (colored by NDVI) * **Vegetation Distribution Bar Chart** - Compares green cover across all regions * **Urban Development Intensity** - Color-coded by urban classification (Dense/Moderate/Low/Rural) * **Average Land Use Pie Chart** - Overall composition of built-up, water, vegetation, and other land uses ### Detailed Regional Analysis (Per Region) * **Built-up Areas Map** - Red intensity showing urban development density * **Water Occurrence Map** - Blue gradient showing water body frequency (0-100%) * **Vegetation Index (NDVI)** - Green-red gradient showing vegetation health (-0.2 to 0.8) * **Land Use Classification** - Categorical map (Urban=Red, Water=Blue, Vegetation=Green, Other=Black) ## Key Metrics Calculated | Category | Metrics | | -------------- | ----------------------------------------------------------------------------------------------- | | **Built-up** | Mean intensity, max intensity, total area (ha), high-density area (ha), percentage coverage | | **Water** | Mean occurrence, permanent water area (>80%), seasonal water area (20-80%), percentage coverage | | **Vegetation** | Mean NDVI, vegetation area (>0.3), dense vegetation (>0.5), bare soil area, percentage coverage | | **Composite** | Total area, urban classification, development patterns, land use ratios | ## Analysis Insights Generated * **Urban Pressure Assessment** - Identifies regions with high development pressure * **Water Resource Mapping** - Quantifies permanent vs seasonal water availability * **Green Space Evaluation** - Measures vegetation cover and ecological health * **Development Patterns** - Reveals relationships between urbanization and environmental factors ## Result Multi-scale urban development analysis with both regional comparisons and detailed local assessments for sustainable planning and environmental monitoring. ## Output ### Quick Summary | region | buildup\_percentage | water\_percentage | urban\_class | | -------------------- | ------------------- | ----------------- | ------------ | | ahmedabad\_sabarmati | 96.981293 | 5.442177 | Dense Urban | | delhi\_yamuna | 84.551857 | 4.091872 | Dense Urban | | kolkata\_hooghly | 93.197279 | 6.998953 | Dense Urban | | pune\_rivers | 96.072101 | 2.199354 | Dense Urban | ***

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