Example Data

Real-world geographic datasets used in allocator demonstrations and analysis.

Input Data Sources

This directory contains the source datasets used for allocator analysis and demonstrations.

📊 Available Datasets

Delhi Road Network (delhi-roads-1k.csv)

  • Source: OpenStreetMap

  • Location: Delhi, India

  • Coverage: 1,000 road segments

  • Geographic Bounds: 28.414°-28.880°N, 76.870°-77.329°E

  • Road Types: Primary, secondary, tertiary, trunk roads

  • Notable Roads: Mahatma Gandhi Road, Outer Circle, Windsor Place, Grand Trunk Road

Chonburi Road Network (chonburi-roads-1k.csv)

  • Source: OpenStreetMap

  • Location: Chonburi Province, Thailand

  • Coverage: 1,000 road segments

  • Geographic Bounds: 12.623°-13.578°N, 100.867°-101.648°E

  • Road Types: Primary, secondary, tertiary, trunk roads

  • Notable Roads: Sukhumvit Road (ถนนสุขุมวิท), major highways and local roads

📋 Data Format

Both datasets follow this structure:

segment_id,osm_id,osm_name,osm_type,start_lat,start_long,end_lat,end_long
1,5873630,Mahatma Gandhi Road,primary,28.674,77.230,28.679,77.229
...

Column Descriptions

  • segment_id: Unique identifier for road segment

  • osm_id: OpenStreetMap way ID

  • osm_name: Road name (may be empty for unnamed roads)

  • osm_type: Road classification (primary, secondary, tertiary, trunk)

  • start_lat, start_long: Starting coordinates (WGS84)

  • end_lat, end_long: Ending coordinates (WGS84)

🎯 Usage in Scripts

Scripts typically convert this road segment data into point datasets:

import pandas as pd

# Load road data
roads = pd.read_csv('inputs/delhi-roads-1k.csv')

# Convert to analysis points (using start coordinates)
points = pd.DataFrame({
    'longitude': roads['start_long'],
    'latitude': roads['start_lat'],
    'segment_id': roads['segment_id'],
    'road_name': roads['osm_name'].fillna('Unnamed Road'),
    'road_type': roads['osm_type']
})

🌍 Real-World Applications

These datasets enable realistic analysis for:

  • Urban planning: Road maintenance zone creation

  • Logistics: Delivery route optimization

  • Emergency services: Response territory planning

  • Infrastructure: Inspection scheduling

  • Transportation: Public transit route design

📈 Data Quality

  • Completeness: Both datasets provide comprehensive coverage of major urban areas

  • Accuracy: Coordinates accurate to ~1 meter (OpenStreetMap quality)

  • Currency: Data extracted from current OpenStreetMap database

  • Diversity: Mix of road types from major highways to local streets

🔗 Source Attribution

Data sourced from OpenStreetMap under the Open Database License.

© OpenStreetMap contributors