📊 City of Fort Worth Development Permits Data Analysis

An analysis of public permit data from the Ciy of Fort Worth using PostgreSQL and Power BI. A 3 part series about this project, including the results of my data analysis is available on my blog at https://medium.com/@sergioramos3.sr/list/exploring-cofw-a8720258688b

🔍 Project Overview

This repository holds a data analysis project based on a public dataset. The data is analyzed using PostgreSQL and visualized in Power BI. Project includes a link to data source and code to replicate processing steps, as well as dashboard files.

🧱 Folder Structure

project-root/
├── Code/                  # SQL queries used for data cleaning and analysis
├── Dashboard_Files/       # Power BI dashboard components
│   ├── PBIP/              # Power BI Project files
│   ├── PBIT/              # Power BI Template
│   └── PBIX/              # Power BI Desktop file
└── Research_Notes/        # Assumptions, findings, research sources, and exploratory notes

🚀 Getting Started

1. Clone the repository

   git clone https://github.com/0sergio-hash/City-of-Fort-Worth-Development-Permits-Data-Analysis.git

2. Load the dataset

   • Download data from COFW site.
   • SQL import and cleaning script is available in /Codeto load into your PostgreSQL instance.

3. Run the analysis

   • SQL queries are modular and labeled inside /Code.

4. Open the dashboard

   • Use the .pbix, .pbip, or .pbit files inside /Dashboard_Files to open the Power BI report.
   • Power BI will prompt for data sources if not already loaded—point it to your PostgreSQL instance.

🧠 Project Goals

• Understand Economic Development in Fort Worth
• Identify key trends in Development Permit data
• Build a clean and reusable Power BI dashboard

📚 Data Source

https://data.fortworthtexas.gov/Development-Infrastructure/Development-Permits/quz7-xnsy/about_data

📌 Notes

• See /Research_Notes as well as notes within SQL scripts in /Code for additional context

✅ Requirements

• Power BI Desktop (or Power BI service)
• PostgreSQL (or an understanding of the SQL used)
• Git (optional but helpful for version control)