A comprehensive framework for assessing climate vulnerability across major global cities using multiple data sources, advanced analytics, and reproducible R workflows.
This project develops a standardized climate vulnerability index for cities worldwide, combining:
- Climate Risk Assessment: Temperature and precipitation extremes
- Economic Resilience Analysis: GDP, economic diversity, gender inclusion
- Social Vulnerability Mapping: Demographics and adaptive capacity
- Composite Vulnerability Scoring: 0-1 scale with categorical rankings
| City | Vulnerability Score | Category | Key Risk Factors |
|---|---|---|---|
| Mumbai | 0.630 | High | Precipitation risk, economic constraints |
| Tokyo | 0.363 | Moderate | Natural disaster exposure |
| Los Angeles | 0.249 | Low | Heat risk, strong economy |
| São Paulo | 0.199 | Low | Balanced risk profile |
| New York | 0.108 | Low | Economic resilience |
| London | 0.030 | Low | Temperate climate, strong adaptive capacity |
- Precipitation risk (r=0.902) is the strongest vulnerability predictor
- Economic resilience (r=0.747) provides significant protection
- Developing economies show higher vulnerability despite varying climates
- 🇬🇧 London (Oceanic climate)
- 🇺🇸 Los Angeles (Mediterranean climate)
- 🇮🇳 Mumbai (Tropical monsoon climate)
- 🇺🇸 New York (Humid continental climate)
- 🇧🇷 São Paulo (Subtropical climate)
- 🇯🇵 Tokyo (Humid subtropical climate)
Raw Data Sources → Collection Functions → Quality Checks → Vulnerability Index → Analysis & Visualization
- Language: R 4.0+
- Data Sources: NASA POWER, World Bank API, US Census ACS
- Key Packages:
tidyverse,nasapower,wbstats,tidycensus - Architecture: Modular, memory-optimized for 100+ cities
- Reproducibility: Fully documented workflow
climate_safe_cities/ ├── 01_setup/ # Environment configuration │ ├── 01_install_packages.R # Package management │ └── 02_api_keys_setup.R # API authentication ├── 02_data_collection/ # Data acquisition modules │ ├── 01_weather_functions.R # NASA POWER integration │ ├── 02_worldbank_functions.R # Economic/social indicators │ ├── 03_census_functions.R # US demographic data │ ├── 04_city_database.R # Global cities database │ └── 05_batch_collection.R # Memory-efficient batch processing ├── 03_data_processing/ # Analysis pipeline │ ├── 01_data_quality_checks.R # Automated QA/QC │ └── 02_vulnerability_index.R # Core vulnerability calculations ├── 99_utilities/ # Support functions │ ├── helper_functions.R # Utility functions │ └── memory_management.R # Large-scale processing tools ├── outputs/ # Generated results │ └── vulnerability_analysis/ # Vulnerability index outputs └── docs/ # Documentation └── methodology.md # Detailed methodology
- R 4.0 or higher
- RStudio (recommended)
- API keys (optional but recommended):
- US Census API
- OpenWeatherMap API (for urban heat island analysis)
- Clone the repository
git clone https://github.com/jackmurphy2351/climate_safe_cities.git cd climate_safe_cities
Set up environment r# Install required packages source("01_setup/01_install_packages.R")
source("01_setup/02_api_keys_setup.R")
Run analysis r# Load and run complete vulnerability analysis source("03_data_processing/02_vulnerability_index.R")
all_data <- load_all_city_data() vulnerability_results <- calculate_vulnerability_index(all_data) rankings <- create_vulnerability_summary(vulnerability_results)
📈 Methodology Vulnerability Index Calculation Vulnerability = Climate Risk - Adaptive Capacity + 0.5 Climate Risk Components:
Temperature extremes and variability Precipitation patterns and extremes Climate trend analysis
Adaptive Capacity Components:
Economic resilience (GDP, diversity, inclusion) Social vulnerability (demographics, health, education)
See detailed methodology for complete technical specifications. 📊 Data Sources SourceData TypeCoverageTemporal RangeNASA POWERWeather/ClimateGlobal1981-presentWorld BankEconomic/Social200+ countries1960-presentUS Census ACSDemographicsUS cities2010-present 🔬 Extending the Analysis Adding New Cities r# Add city to database new_city <- tribble( ~city_name, ~country_name, ~country_iso, ~lat, ~lon, "Paris", "France", "FRA", 48.8566, 2.3522 )
collect_city_climate_vulnerability_enhanced("Paris", 2018:2022) Scaling to 100+ Cities The framework supports memory-efficient processing for large-scale analysis: r# Batch processing with memory management collect_cities_in_chunks(city_list, chunk_size = 15, years = 2018:2022)
📄 License This project is licensed under the MIT License - see the LICENSE file for details.
🤝 Contributing Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change. See CONTRIBUTING.md for contribution guidelines. 📞 Contact John Murphy
GitHub: @jackmurphy2351 Email: [jackmurphy2351@gmail.com] LinkedIn: [linkedin.com/in/jackmurphy2351]
🙏 Acknowledgments
NASA POWER team for global meteorological data World Bank for comprehensive development indicators US Census Bureau for demographic data R community for excellent package ecosystem
📚 Citation If you use this framework in your research, please cite: bibtex@software{murphy2024climate, author = {Murphy, John}, title = {Climate Safe Cities: Global Urban Climate Vulnerability Analysis}, url = {https://github.com/jackmurphy2351/climate_safe_cities}, year = {2024} }
🌍 Building resilient cities through data-driven climate vulnerability assessment