Sustainability Spotlight | Storymap
I developed Toxic Release Inventory (TRI) maps based on U.S. congressional districts and state-level environmental policy support rates. In this story map, I utilized ArcGIS Online, Mapbox, Story Map, and JavaScript, HTML, and CSS to create interactive, user-friendly interfaces for public engagement. The goal of this web tool is to raise awareness of environmental impacts across politically governed regions and advocate for stronger environmental policy support.
Experience Builder
Los Angeles Public Lands for Urban Farming
As part of my capstone project, I designed the Activating Public Land Dashboard using ArcGIS Experience Builder, integrating interactive features to support user engagement and data analysis. Additionally, I utilized ArcGIS Pro to create a comprehensive Composite Index tool, enabling users to assess complex factors and derive insights essential for informed urban agriculture planning and community-focused initiatives.
R Shiny
In this project, I used R Studio to identify toxic release sites in Wayne County, MI, by parsing and analyzing a JSON file. I conducted both temporal and spatial impact analyses of emissions on surrounding areas, incorporating data retrieved via the Census API. The project culminated in an interactive web application using R Shiny, with comprehensive reports highlighting environmental justice issues and visualizing the findings in an accessible format.
Multivariate Statistics
Ever thought about how your food choices shape the environment? This project explores the environmental costs of food production—from emissions to land use—using global data. It identifies sustainable dietary practices, revealing the surprising impacts of animal-based and plant-based foods. Let's rethink our plates for a greener tomorrow!
Python
This project aims to create a simple model to simulate forest fires. Forest Fire and Disease Spread Model This repository contains code for a simulation model developed as part of a class project in Earth System Modeling. The model is initially designed to simulate the spread of forest fires but can be extended to represent the spread of infectious diseases (e.g., Ebola), illustrating the concept of universality in modeling. Universality suggests that similar rules or equations can describe seemingly different phenomena, such as fire spread and disease transmission.
Machine learning | UAV Data
This project leveraged UAV data to classify forest canopy at the genus level, enhancing ecological monitoring and conservation efforts through geospatial analysis techniques. I integrated high-resolution UAV imagery with extensive field data, optimizing classification precision through advanced spatial analysis. Additionally, I evaluated multiple classification methods to ensure accuracy, contributing valuable insights to ongoing scientific discussions on methodological advancements in forest canopy classification.
Python
This project checked the quality of wave height data from the ICESat-2 satellite in the Great Lakes area, aiming to fill gaps where buoys can’t measure during winter. I used Python to process satellite data, matched it with buoy data, and analyzed how well they align, especially during high-wave events. This work helps show if satellite data can be reliable for wave monitoring when buoys aren’t available.