Exploring the relationship between coffee shop review counts and neighborhood demographics, this project visualizes how household income and racial composition vary across counties using scatterplots and color gradients.

This project examines whether the spatial distribution of hospitals in Metro Atlanta is equitable. Using 2023 ACS tract-level data and hospital point-of-interest (POI) data, median rent was classified into high, medium, and low categories. Spatial joins and buffer analyses measured hospital accessibility, while regression models tested the relationship between rent, demographics, and healthcare access. Results show that hospitals are more concentrated in high-rent and high-density neighborhoods, while low-rent and minority-heavy tracts face significant disadvantages.

This project analyzed Google Places data on pharmacies and drugstores in Griffin, GA using R. The workflow involved cleaning and restructuring semi-structured data, extracting key attributes like ZIP codes, handling duplicates and missing values, and converting addresses into spatial points. POIs were filtered to the city boundary and visualized on an interactive map, with ratings and review counts used to compare their distribution, popularity, and service coverage across the city.

This project collects and visualizes pharmacies and drugstores in Griffin, Georgia using the Google Places API. POI data, including location, rating, and price level, was retrieved and converted into spatial points (sf objects). Buffers were generated to assess spatial coverage, and an interactive map was created with tmap showing the city boundary, POI locations, and attributes such as user rating counts and price levels. The analysis provides insights into accessibility and popularity patterns for users.