Minnesota Home Analysis
With Python

Project Overview:

The project focuses on analyzing seasonal home ownership in Minnesota, specifically examining the distribution of seasonal homeowners based on their zip codes. By categorizing the data into three ranges (1-5, 5-20, and 20-100), the aim is to gain insights into the concentration of seasonal homeownership across different zip code areas in the state. Through geospatial analysis and visualization techniques, the project aims to provide a comprehensive understanding of seasonal home ownership patterns in Minnesota.

Data Acquisition:

The data for this project was acquired from the Minnesota State Commons GIS, which provided a zip code layer for the state. This dataset was crucial for geographically representing the distribution of seasonal homeowners across different zip code areas in Minnesota. Additionally, clean zip code data was extracted from the provided dataset and used for geocoding to obtain spatial information necessary for subsequent geoprocessing and analysis tasks.

 Geoprocesses and Visualization:

• Geocoded Addresses: The addresses were geocoded, converting them into geographic coordinates for spatial analysis.

• Symbolization with Graduated Symbols: The data was symbolized using graduated symbols, allowing for the visualization of spatial patterns and variations in the dataset. Graduated symbols were chosen to represent different levels or ranges of a particular attribute, providing insights into its distribution across the study area.

• Data Cleaning with Python in Field Calculator: Python scripting was employed within the Field Calculator to clean up the data, ensuring consistency and accuracy in attribute values. This process involved removing duplicates, correcting formatting errors, and standardizing data entries.