Spatial Analysis of Property Prices in Philadelphia

📖 Overview

This project analyzes single-family properties in Philadelphia to explore how proximity to transit lines, crime incidents, and job locations influences sale prices. Using spatial data, the R script filters properties, calculates distances, computes metrics, runs regression models, and generates visualizations and reports.

Key Features

• Data Processing: Loads and standardizes spatial shapefiles for properties, transit, crime, and jobs.
• Spatial Analysis: Filters properties within 800m of transit lines and calculates distances to key features.
• Metrics: Computes job accessibility and crime density, with transformations for analysis.
• Modeling: Runs regression models for properties built in 1990–2000 and 2010–2024.
• Outputs: Produces a Word document with regression tables, t-test results, and boxplot analysis, plus PNG visualizations.

🚀 Getting Started

Prerequisites

• R: Version 4.0 or higher.
• R Packages: sf, dplyr, ggplot2, sp, car, flextable, officer, broom, tidyr.
• Shapefiles: Spatial data files (e.g., Opa_Properties_LivingAreas.shp, HighSpeed_Lines.shp, etc.) in a specified directory.

Installation

1. Clone the repository:

   https://github.com/ArnoldMuchene/Class_Project   

Property Analysis Project

This project provides an R script for spatial data analysis, focusing on property data processing, distance calculations, regression modeling, and visualization generation using shapefiles.

Setup

1. Place Shapefiles: Store shapefiles in the ~/ArcGIS/Inputs directory or update the file path in the script (analysis.R).
2. Dependencies: Ensure R and required packages (e.g., sf, dplyr, ggplot2, stargazer) are installed. Install missing packages using install.packages().

Usage

1. Open analysis.R in RStudio or your preferred R environment.
2. Update the working directory in the setwd() function to point to your shapefile location.
3. Run the script to:
   • Load and preprocess spatial data.
   • Filter properties and calculate distances (e.g., to transit).
   • Generate metrics, regression models, and visualizations.
   • Save outputs to the outputs/ directory.

Outputs

• Word Document: regression_tables_scaled_polynomial.docx containing formatted regression tables, t-test results, and boxplot analysis.
• Visualizations:
  • filtered_properties_map.png: Map of filtered properties colored by sale price.
  • boxplot_all_variables_faceted.png: Faceted boxplot comparing property characteristics by year built.
• Console Outputs: Summary tables for t-tests and distance-to-transit effects.

Repository Structure

├── analysis.R # Main R script for the analysis

├── README.md # Project documentation

├── ArcGIS/Inputs/ # Directory for shapefiles (not included)

└── outputs/ # Directory for generated Word doc and PNGs

Customization

• Shapefile Paths: Update setwd() and st_read() paths in analysis.R to match your data location.
• Filters: Modify the 800m transit buffer or other distance conditions in the script.
• Models: Add or adjust predictors in regression models for alternative analyses.

Contributing

Contributions are welcome! To contribute:

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

License

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

Contact

For questions or feedback, open an issue or contact arnoldnjengabiz@gmail.com

If you find this project useful, please give it a star on GitHub!