Global Commuting Origin-Destination Flow Dataset

This dataset contains commuting Origin-Destination (OD) flow data spanning 6 continents, 179 countries, and 1,625 cities worldwide, providing unprecedented coverage of urban dynamics across diverse urban environments.

Dataset Applications

This dataset supports research across multiple Sustainable Development Goals (SDGs):

1. Urban Planning (SDG-11)

   • Identify inefficient long-distance travel patterns
   • Optimize zone allocation
   • Reduce travel costs

2. Urban Resilience (SDGs-9,11)

   • Identify critical commuting corridors
   • Determine vulnerable urban areas
   • Develop targeted resilience strategies

3. Transportation (SDG-9)

   • Travel demand modeling
   • Traffic simulation
   • Infrastructure load analysis
   • Congestion mitigation strategies

4. Public Health (SDG-3)

   • Identify high-risk transmission routes
   • Determine crowded workplace regions
   • Support epidemic control strategies

5. Energy Use (SDG-7) and Environmental Protection (SDG-13)

   • Evaluate and manage energy demands
   • Reduce carbon emissions
   • Assess environmental impact

Details about the dataset and its applications can be found in the dataset paper.

Project Structure

The dataset is organized into two main directories:

.
├── CityAndRegionSplit/        # City and region division data
│   ├── 0_AU_Birdum/
│   │   ├── regions.cpg        # Character encoding for attribute data
│   │   ├── regions.dbf        # Attribute data for each shape
│   │   ├── regions.prj        # Coordinate system information
│   │   ├── regions.shp        # Main file containing geometry data
│   │   ├── regions.shx        # Index file for geometry data
│   │   └── regions.png        # Visualization of the city division
│   └── ...                    # Other cities
│
└── CommutingODFlows/          # Generated commuting OD flow data
    ├── 0_AU_Birdum/
    │   ├── generation.npy     # OD flow matrix in numpy format
    │   └── generation.png     # OD flow visualization
    └── ...                    # Other cities

Usage Note

1. CityAndRegionSplit/

   • Using ArcGIS or QGIS to open the regions.shp file.
   • Using Python to read the regions.shp file via geopandas or shapely.

     • import geopandas as gpd
       gdf = gpd.read_file('CityAndRegionSplit/0_AU_Birdum/regions.shp')

2. CommutingODFlows/

   • Construct lines between the origin and destination regions based on the generation.npy file in ArcGIS or QGIS
     • Create lines referring to this tutorial
     • Give the lines attributes referring to this tutorial
   • Using Python to read the generation.npy file via numpy.

     • import numpy as np
       flows = np.load('CommutingODFlows/0_AU_Birdum/generation.npy')