This project analyzes user-generated text data from Reddit to explore how readers discuss and respond to the works of Haruki Murakami, one of my favorite authors, whose writing has inspired me to undertake this study. Murakami has been writing and publishing since the late 1970s, achieving international fame with Norwegian Wood (1987), whose English translations introduced his distinctive blend of magical realism, surrealism, and themes of love, loneliness, and music to a worldwide audience. His literature has historically received mixed reviews - praised for its imaginative storytelling, dreamlike narrative style, and evocative atmospheres, while occasionally critiqued for the perceived emotional detachment in his characters or sometimes the repetition of certain themes. By combining n-gram analysis, sentiment scoring, and NRC-based emotional categorization, this study aims to identify frequently mentioned book titles, reveal patterns in genre recognition, and highlight both positive reception and critical engagement among readers.

This project uses computer vision and Google Street View imagery to measure built-environment features such as building proportions, greenness, visible sky, and sidewalk presence across percieved walkable and unwalkable census tracts. By combining spatial visualization, boxplot comparisons, and t-tests, the analysis reveals how urban form and design patterns shape walkability. The findings show that walkable areas consistently feature lower building-to-street ratios, more greenery, and better street proportions, demonstrating that walkability depends on coherent, human-scale urban design rather than the simple presence of pedestrian infrastructure.

This assignment focuses on simulating a multimodal journey that starts from the centroid of each Census Tract, involves driving to the nearest MARTA station, and continues by taking the MARTA to the Midtown station. The analysis aims to compare how travel times differ across Census Tracts.

This project analyzes the Fatal Encounters dataset to uncover patterns in police-related fatalities across the United States. Using data from 2000 to 2021, we examine trends over time, racial and gender disparities, age distributions, and geographic variations. The analysis includes time series plots, stacked bar charts, histograms, choropleth maps, and dot density maps to visualize fatalities by race, gender, age, and state. The goal is to provide insights into demographic and spatial patterns of fatal encounters, highlighting disparities and informing potential areas for targeted interventions.

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.