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Work in Progress

While key foundational features of Listro have been implemented in v0.0.1, the project remains under active development. Features such as persistent state saving are not yet available, and due to limited testing, users may encounter bugs.

About Listro

When you have a lot to get done, keeping track of tasks can quickly become overwhelming. Since I do most of my work in the terminal using tools like Neovim, I set out to build a terminal user interface (TUI) that fits seamlessly into my existing workflow.

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About PyScrape

As a Computer Science student at the University of Arizona, I often found myself navigating course websites and manually downloading content—an inconvenient process that took me out of the terminal environment I preferred working in.

To streamline this task, I began developing PyScrape, a command-line tool for downloading static content from static websites.

Usage

PyScrape is currently a work in progress. At this stage, it supports downloading PDFs from a given webpage. Support for additional file types such as DOCX documents, images, and presentations is planned for future releases, as the project continues to evolve.

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• Formal Report

About This Project

The tools and techniques required for conducting ethical and effective data analysis on real-world datasets are invaluable. To strengthen these skills, I collaborated with two peers from the University of Arizona to analyze multiple public datasets from the City of Tucson.

Datasets

• Tucson Police Reported Crimes
• Tucson Police Arrests
• City of Tucson Streetlight Locations
• Tucson Neighborhood Income

Findings

We found that lower-income neighborhoods—particularly in Wards 3 and 5—experience higher rates of theft and violent crime, supporting the link between income inequality and crime. However, the presence of streetlights did not correspond with reduced crime rates. In fact, streetlights were more common in high-crime areas, suggesting they are likely installed in response to crime rather than as a deterrent. Machine learning models, especially Random Forest, were effective in predicting high-crime areas, but the findings also raise ethical concerns about over-policing and data bias. These concerns underscore the importance of thoughtful, equitable policy interventions.