🌿 Climatic Resilience and Fragility Analysis (CRAF)

🇮🇹 Versione in italiano qui

This repository contains the analytical workflow, datasets, and processing scripts used to develop the climatic fragility and resilience assessment for the Bologna metropolitan area.
The work integrates thermal, land-cover, morphological, and socio-environmental indicators to identify areas exposed to heat and to support evidence-based climate adaptation planning.

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🧭 Scope

The analysis combines:

• land surface temperature (LST) data,
• vegetation and land-cover metrics,
• accessibility and structural components,
• socio-environmental indicators,

to produce the Climatic Fragility Index (CFI) and related metrics for climate-resilient urban planning.

Methodological reference:

Investigating Social Vulnerability to Extreme Heat: Heat Islands and Climate Shelters in Urban Contexts: The Case of Bologna
ISPRS International Journal of Geo-Information (2024)
📄 https://tustorage.ulb.tu-darmstadt.de/server/api/core/bitstreams/5655dda2-e4a6-47c5-861a-4aa6102fe31d/content

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📁 Repository Structure

craf/
│
├── notebooks/                      # Jupyter notebooks for each analytical step
│   ├── 01_clip_gdal.ipynb 
│   ├── 02_resampling.ipynb  
│   ├── 03_hot_and_cold_spot.ipynb  
│   ├── 04_NDVI_for_CSI.ipynb  
│   ├── 05_bus_stop.ipynb  
│   ├── 06_disabled_parking.ipynb 
│   ├── 07_accessibility.ipynb  
│   └── ...
│
├── data/                        # Input and processed datasets
│   ├── Climatic_fragility/
│   └── ...
│
├── docs/                        # web mapping
├── requirements.txt             # Python dependencies
└── README.md

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📊 Data Sources

Category	Source	Purpose
Thermal	ECOSTRESS LST (NASA JPL)	Hot/cold spot detection
Land Cover	Sentinel-2, Copernicus Urban Atlas	Vegetation and blue-space extraction
Topography	Copernicus DEM	Slope and morphology
Socio-Environmental	ISTAT Census, Climate Shelter Index	Sensitivity and adaptive capacity
Administrative	Comune di Bologna – Statistical Areas	Spatial zoning for analysis
Meteorological	Meteoblue, ERA5 Reanalysis	LST pattern validation

Large raster and vector files are hosted externally.

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🧮 Processing Workflow

The workflow is executed through a sequence of Jupyter notebooks.
Each step integrates a thematic dataset, leading to the final climatic fragility output.

📘 Processing Chain

Clip/Resample → 03_hot_and_cold_spot.ipynb
           ↓
NDVI computation → 04_NDVI_for_CSI.ipynb
           ↓
Accessibility + Structures → 08_identify_structures_for_CSI.ipynb
           ↓
Combine indicators → 09_new_CSI.ipynb
           ↓
Integrate all components → 10_climatic_fragility.ipynb
           ↓
Final index → 11_fragility.ipynb

📔 Notebook Execution Order

1. 01_clip_gdal.ipynb
2. 02_resampling.ipynb
3. 03_hot_and_cold_spot.ipynb
4. 04_NDVI_for_CSI.ipynb
5. 05_bus_stop.ipynb
6. 06_disabled_parking.ipynb
7. 07_accessibility.ipynb
8. 08_identify_structures_for_CSI.ipynb
9. 09_new_CSI.ipynb
10. 10_climatic_fragility.ipynb
11. 11_fragility.ipynb

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📊 Climatic Fragility Index (CFI) – Summary

The index ranges from 0.0 to 1.0, with higher values indicating higher fragility.

Range	Level	Description
0.00–0.25	Very Low	Strong vegetation and cooling capacity
0.26–0.45	Low	Balanced exposure and resilience
0.46–0.60	Moderate	Intermediate fragility
0.61–0.75	High	Heat-exposed areas with limited mitigation
0.76–1.00	Very High	Dense or industrial urban areas

Observed range in Bologna:

0.18 ≤ CFI ≤ 0.81

Examples:

• Moderate: Triumvirato–Pietra, Casteldebole
• High: Piazza dell’Unità, Villaggio della Barca
• Very Low: Osservanza, San Michele in Bosco

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⚙️ How to Use

1. Clone the repository

   git clone https://github.com/TALEA-platform/craf.git
   cd craf

2. Install dependencies

   pip install -r requirements.txt

3. Run notebooks from the source/ folder.

4. Outputs are saved in:

   data/Climatic_fragility/outputs/
   

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📍 Study Area

Bologna, Italy — area for climatic fragility validation.

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🧩 Project Status

Ongoing (2025) — continuous updates expected.

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🇮🇹 Versione Italiana {#versione-italiana}

Questo repository contiene il workflow analitico, i dataset e gli script utilizzati per sviluppare l’analisi della fragilità e resilienza climatica per l’area metropolitana di Bologna.
L’analisi integra indicatori termici, di uso del suolo, morfologici e socio-ambientali per identificare le aree esposte al calore e supportare la pianificazione climatica basata sull’evidenza.

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🧭 Obiettivo

L’analisi combina:

• dati di temperatura superficiale (LST),
• metriche di vegetazione e copertura del suolo,
• componenti strutturali e di accessibilità,
• indicatori socio-ambientali,

per produrre il Climatic Fragility Index (CFI) e indicatori correlati.

Riferimento metodologico:

Investigating Social Vulnerability to Extreme Heat: Heat Islands and Climate Shelters in Urban Contexts: The Case of Bologna
ISPRS International Journal of Geo-Information (2024)

---

📁 Struttura del Repository

craf/
│
├── notebooks/                      # Jupyter notebooks for each analytical step
│   ├── 01_clip_gdal.ipynb 
│   ├── 02_resampling.ipynb  
│   ├── 03_hot_and_cold_spot.ipynb  
│   ├── 04_NDVI_for_CSI.ipynb  
│   ├── 05_bus_stop.ipynb  
│   ├── 06_disabled_parking.ipynb 
│   ├── 07_accessibility.ipynb  
│   └── ...
│
├── data/                        # Input and processed datasets
│   ├── Climatic_fragility/
│   └── ...
│
├── docs/                        # web mapping
├── requirements.txt             # Python dependencies
└── README.md

---

📊 Fonti Dati

Categoria	Fonte	Uso
Termici	ECOSTRESS LST (NASA JPL)	Rilevamento hotspot e coldspot
Uso del suolo	Sentinel-2, Copernicus Urban Atlas	Estrazione aree verdi e blu
Topografia	Copernicus DEM	Morfologia e pendenza
Socio-ambientali	ISTAT, Climate Shelter Index	Sensibilità e capacità adattiva
Amministrative	Aree statistiche Comune di Bologna	Unità di analisi
Meteorologiche	ERA5	Validazione dei pattern termici

File raster e vector molto grandi sono disponibili su altre sorgenti

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🧮 Flusso di lavoro

La pipeline si sviluppa attraverso una sequenza di notebook dedicati a singoli set di dati tematici.
Ogni fase integra un set di dati tematici, che porta al risultato finale sulla fragilità climatica.

📘 Catena di esecuzione

Clip/Resample → 03_hot_and_cold_spot.ipynb
           ↓
NDVI computation → 04_NDVI_for_CSI.ipynb
           ↓
Accessibility + Structures → 08_identify_structures_for_CSI.ipynb
           ↓
Combine indicators → 09_new_CSI.ipynb
           ↓
Integrate all components → 10_climatic_fragility.ipynb
           ↓
Final index → 11_fragility.ipynb

📔 ordine di esecuzione dei notebook

1. 01_clip_gdal.ipynb
2. 02_resampling.ipynb
3. 03_hot_and_cold_spot.ipynb
4. 04_NDVI_for_CSI.ipynb
5. 05_bus_stop.ipynb
6. 06_disabled_parking.ipynb
7. 07_accessibility.ipynb
8. 08_identify_structures_for_CSI.ipynb
9. 09_new_CSI.ipynb
10. 10_climatic_fragility.ipynb
11. 11_fragility.ipynb

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📊 Sintesi CFI

Indice compreso tra 0.0 e 1.0.
Valori osservati a Bologna: 0.18 ≤ CFI ≤ 0.81.

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Esempi:

• Moderato: Triumvirato–Pietra, Casteldebole
• Alto: Piazza dell’Unità, Villaggio della Barca
• Molto Basso: Osservanza, San Michele in Bosco

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⚙️ Come si usa

1. Clona il repository

   git clone https://github.com/TALEA-platform/craf.git
   cd craf

2. Installa le dipendenze

   pip install -r requirements.txt

3. Esegui i notebook dalla cartella notebooks/.

4. I risultati sono salvati in:

   data/Climatic_fragility/outputs/
   

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📍 Area di Studio

Bologna, Itala - area per la convalida della fragilità climatica.