This project focuses on conducting a spatial analysis of Airbnb listings in Amsterdam. The analysis aims to explore the impact of Airbnb on residential communities by examining the spatial patterns and relationships within the dataset.
The dataset used in this analysis was collected from Inside Airbnb, a mission-driven project providing data and advocacy regarding Airbnb's influence on residential communities. The data includes information on Airbnb listings in Amsterdam, such as their location, host details, and other relevant attributes. The dataset used for this analysis was last updated on March 9th, 2023.
The analysis is presented in a Jupyter Notebook, a web-based interactive environment for data analysis and visualization. The notebook demonstrates the capabilities of Python for spatial data analytics, showcasing various techniques and methodologies employed in the analysis.
The Jupyter Notebook covers the following key aspects:
- Data loading and initial cleanup.
- Non-Spatial Data Analysis.
- Spatial data visualization and exploration techniques.
- Spatial autocorrelation analysis to identify local clusters and outliers.
The following Python libraries were utilized in this analysis:
- Pandas: Data manipulation and analysis.
- GeoPandas: Spatial data manipulation and analysis.
- Matplotlib & Seaborn: Data visualization.
- PySAL: Spatial analysis library.
- Folium: Interactive mapping.
Alongside a few other as seen in the first cell of the analysis notebook. Ensure that these dependencies are installed before running the Jupyter Notebook.
Techniques for local spatial autocorrelation in Python were learned from the book "Geographic Data Science with Python" (2023) written by Dani Arribas-Bel and Levi John Wolf. I highly recommend this book for those looking to expand their spatial data science toolset.
This project is licensed under the MIT License. Feel free to modify and adapt the code for your own purposes.