The repository serves as a portfolio for geospatial analysis with Python. Python code presented in the repository is completed as part of academic work and reflects the usage of near real-time data (Jan - May 2023). I took Geog 573 during Spring 2023, and the course was taught by Professor Song Gao, a renowned professor in the geospatial data science domain and director of GeoDS Lab in the department of Geography, University of Wisconsin - Madison. The repository is made public for geospatial enthusiasts and students seeking knowledge in the intersectoral domain of Geography + Computer Science.

1. Observations from application of Moran's I, Geographic Weighted Regression (GWR) using PySAL geospatial analysis library. The below image shows the distribution of wasted votes ^[1] for Democrats during the presidential election 2020 in Madison, WI. Where a hotspot showcases strong spatial autcorrelation of wasted votes and coldspot showcases a weak spatial correlation of wasted votes.

2. Snapshot of Airbnb median price variation based on mean bedrooms and review score values in Austin, Texas using GWR

3. Google Earth Engine (GEE) map is a powerful geospatial analysis tool that uses power of Google data to create distinct and large-scale map products developed by Professor Quisheng Wu from University of Tennessee, Knoxville. ^[2] As part of analysis, I was able to create a timelapse of Las Vegas, Nevada that depicts urban growth and how urban analysis is important.