Social Network Analysis and NetworkX

🗺️ Road Network of Ancona

This repository contains the analysis and scripts related to the road network of Ancona, based on the Estratti OpenStreetMap (OSM) Italia dataset. The project uses the road network graph for analysis and visualization purposes.

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📊 Dataset

The dataset of the Ancona road network is available at the following link:
🌐 Estratti OpenStreetMap Italia - Wikimedia Italia.

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🚀 Usage Instructions

Follow these instructions to make the best use of this project. 🛠️

📥 Download the Dataset

To download the dataset related to the Ancona road network:
1️⃣ Select the region Marche.
2️⃣ Choose the province Ancona.
3️⃣ Finally, select the city Ancona.
4️⃣ Save the file from the right column by clicking on the 💾 GPKG button.

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📦 Installing Dependencies

To run the project, it is necessary to install some essential Python libraries.

The required libraries are:

• fiona
• folium
• geopandas
• matplotlib
• networkx
• numpy
• osmnx
• pandas
• pillow
• pyogrio
• tqdm

You can install them one by one by running the following command in the terminal:

pip install library_name

For example, to install fiona:

pip install fiona

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🌐 Visualizing the Graph and Dataset Information

🔍 Exploring the dataset is easy! Follow these steps:

1. 📈 Visualizing the Interactive Graph
   Run the file Graph_Ancona.py to generate and visualize the interactive graph related to the first point.

2. 📋 Visualizing the Information Table
   Run the file Info_initial_graph.py to display a table containing detailed information about the dataset.

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✂️ Preparing the Dataset

💡 Before starting any analysis, run the script Ancona_center.py.
This script filters the dataset to include only the central area of Ancona.

➡️ You can customize the filter by directly modifying the geographic coordinates (longitude and latitude) in the script.

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🔬 Running Analyses

After downloading and preparing the dataset, follow these simple steps:

1️⃣ Open the script corresponding to the analysis you want to perform.
2️⃣ Update the initial path in the script to point to the directory where you saved the downloaded dataset.
3️⃣ Run the script to obtain your results! ✅

🧠 Overview of Scripts and Functionalities

Below is a detailed overview of the scripts in this repository and their functionalities:

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🎯 Centrality

• 📌 Betweenness Centrality
  Computes nodes' importance based on their position in the shortest paths.
  Code file: betweenness_centrality.py

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• 📌 Closeness Centrality
  Measures how close a node is to all other nodes.
  Code file: closeness_centrality.py

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• 📌 Degree Centrality
  Calculates node importance based on the number of direct connections.
  Code file: degree_centrality.py

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• 📌 Eigenvector Centrality
  Identifies influential nodes based on their neighbors' importance.
  Code file: eigenvector_centrality.py

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🔬 Other Analyses

• 🌟 Cliques
  Finds groups of fully connected nodes in the graph.
  Code file: cliques_analysis.py

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• 🌐 Ego Networks
  Generates subgraphs centered on a single node with its direct connections.
  Code file: ego_networks.py

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• 📈 Shortest Paths
  Identifies the shortest paths between selected pairs of nodes.
  Code file: shortest_paths.py

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• 🌍 Nodes with More Neighbors
  Identifies nodes with the highest number of direct neighbors.
  Code file: nodes_with_more_neighbors.py

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• 🗺️ Nodes with Less Neighbors
  Identifies nodes with the fewest number of direct neighbors.
  Code file: nodes_with_less_neighbors.py

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👩‍💻👨‍💻 Authors

This project is developed and maintained by the following authors:

• Antonio Antonini - GitHub Profile 🌟
• Zazzarini Micol - GitHub Profile ✨
• Fiorani Andrea - GitHub Profile 🚀

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📜 License

This project is released under the MIT License. 📝