Personal Projects
Overview: This project examined the spread of gentrification across bordering neighborhoods, exploring how changes in one area influence surrounding communities. The goal was to effectively calculate neighborhood migration flows, and utilize this measurement to effectively forecast the spread of gentrification.
I developed a directed network between Barcelona's neighborhoods using open-source human mobility data to analyze cross neighborhood influence. Additionally, I explored predictive modeling techniques to identify early indicators of displacement and assess the accuracy of gentrification forecasts.
Technologies Used: PostgreSQL, geopandas (Python), matplotlib (Python), QGIS
Affiliation: This project was a part of the University of Minnesota Knowledge Computing Lab. It was conducted under the guidance of Jina Kim and professor Yao-Yi Chiang.
Overview: The PradoEditor mobile app aimed to promote the study of classical Spanish art by bringing to personalization to the museum's pieces. Our team wanted to design a user-friendly UX which would enable visitors and art students to search for and digitally customize paintings in the Prado Museum's collection for into personal albums. To accomplish this, we designed a platform for searching the museum's complete painting collection, with editing tools to display and alter the images.
Technologies Used: C++, Python, cURL Library, Museo del Prado Artworks API
Affiliation: This project was completed through the partnership of the University of Minnesota Learning Abroad Center and la Fundación Ortega-Marañón. This project was the final presentation of "MADR 3081W: Program Design and Development".
Overview: I created weather and Spotify widgets for my personal computer desktop to provide real-time access to information (and look pretty!!). I pulled data directly from public APIs and utilized Node.js and Electron to create lightweight, asynchronous desktop widgets that run efficiently in the background without disrupting my workflow :).
Technologies Used: HTML/CSS, JavaScript, Node.js, Electron, Open Meteo API, Spotify API
Overview: Our team analyzed domestic migration trends in China, focusing on movement between eastern megacities like Shanghai and Beijing and western rural regions. Using PostgreSQL to query large aggregate population datasets, we identified key urban-rural migration patterns and explore the demographic, economic, and policy-driven factors influencing these shifts. By tracking and predicting transient worker movement, we contributed to policy discussions on migration, resource distribution, and economic development in global urbanization.
Technologies Used: PostgreSQL, geopandas
Affiliation: This project was a collaboration between the University of Minnesota Knowledge Computing Lab and the Carlson School of Business and Management. It was conducted under the direction of professor Yao-Yi Chiang.