VERUS

Vulnerability Evaluation for Resilient Urban Systems

Description

VERUS (Vulnerability Evaluation for Resilient Urban Systems) is a Python library for extracting points of interest from OpenStreetMap, clustering them based on spatial proximity and time-based vulnerability indices, and analyzing urban vulnerability patterns.

Documentation

Comprehensive documentation is available at https://les2feup.github.io/verus/

Publications

• Bittencourt, J. C. N., Costa, D. G., Portugal, P., Peixoto, M. L. M., & Vasques, F. (2025). On the spatiotemporal knowledge-driven vulnerability assessment of urban areas: A clustering-based approach. International Journal of Disaster Risk Reduction, 127, 105681. https://doi.org/10.1016/j.ijdrr.2025.105681

Installation

pip install verus

Reproducing Results

To reproduce the results from the latest article, run the following Jupyter Notebooks:

• Porto, Portugal
• Lisbon, Portugal
• Paris, France

Basic Usage

from verus import VERUS
from verus.data import DataExtractor, TimeWindowGenerator
from verus.grid import HexagonGridGenerator

# Extract default urban data from OpenStreetMap
extractor = DataExtractor(region="Porto, Portugal")
poi_data = extractor.run()

# Define default time-based vulnerability scenarios
twg = TimeWindowGenerator(reference_date="2023-11-06")
time_windows = twg.generate_from_schedule()

# Generate analysis grid
grid = HexagonGridGenerator(region="Porto, Portugal", edge_length=250)
hex_grid = grid.run()

# Run vulnerability assessment
assessor = VERUS(place_name="Porto")

# Load extracted data
assessor.load(
    potis_df=poi_data,
    time_windows_dict=time_windows,
    zones_gdf=hex_grid,
)

# Perform assessment and get results
evaluation_time = tw_gen.to_unix_epoch("2023-11-06 17:30:00")
results = assessor.run(evaluation_time=evaluation_time)

# Visualize results
map_obj = assessor.visualize()

# Save results to file
assessor.save("./results/porto/")

Features

• Extract points of interest from OpenStreetMap for any city
• Create hexagonal grid systems for spatial analysis
• Implement OPTICS density-based clustering for identifying urban patterns
• Apply K-means clustering with Haversine distance for geographic data
• Generate time-dependent vulnerability assessments
• Create interactive maps for visualization with folium
• Export results in various formats (GeoJSON, CSV, HTML)

Contributions

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit changes: git commit -m 'Add amazing feature'
4. Push to branch: git push origin feature/amazing-feature
5. Open a pull request

Please make sure to update tests and documentation as appropriate.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Research developed at the Laboratory of Emerging Smart Systems (LES2) at the Faculty of Engineering of University of Porto
• This work was supported by the Associate Laboratory Advanced Production and Intelligent Systems – ARISE LA/P/0112/2020 (DOI 10.54499/LA/P/0112/2020), by the Base Funding (UIDB/00147/2020) and Programmatic Funding (UIDP/00147/2020) of the R&D Unit Center for Systems and Technologies -- SYSTEC, and by the Fundação para a Ciência e a Tecnologia (FCT) through the PhD scholarship (2024.02446.BD).