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Zeyu Li

I am currently seeking internship opportunities or full-time positions. I am a Mater Student in Spatial Data Science at the University of Southern California. My under graduate major is Software Engineering. I have solid software engineer with exceptional programming, analytical and mathematical skills. High proficiency in Android and iOS mobile development, and web development using Java, Go, React, Spring, Hibernate, Vue, MongoDB and Firebase.

Spatial Analysis of House Prices in King County, WA

Key Technologies: data visualization, Generalized Linear Regression (GLR), Geographically Weighted Regression (GWR), Forest-Based Classification and Regression, neural network models

2023

Project Details

  • Mapped housing prices in King County, WA, utilizing local spatial summary metrics to highlight key spatial patterns and relationships.
  • Conducted data visualization to present relationships between house prices and influential variables, providing insights into primary price drivers.
  • Employed Generalized Linear Regression (GLR) and Geographically Weighted Regression (GWR) models, comparing their effectiveness through regression diagnostics.
  • Utilized Forest-Based Classification and Regression tools and neural network models via scikit-learn's MLPRegressor, optimizing predictive accuracy through various topologies and learning rates.
  • Critically assessed and compared all regression models, drawing conclusions on their insights into the King County housing market dynamics.

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    Diabetes and Health-Related Factors Analysis

    Key Technologies: Python, SQL, Machine Learning, ArcGIS Online, JavaScript API, Pandas, NumPy, Web AppBuilder

    2023

    StoryMap Details

  • Analyzed the prevalence and distribution of diabetes across different regions using Python and SQL for data processing
  • Investigated the correlation between health-related factors and the onset of diabetes, leveraging Machine Learning algorithms in Python.
  • Utilized ArcGIS tools and JavaScript API to visualize and interpret complex health data.
  • Collaborated with a team to gather, clean, and process relevant health datasets using Pandas and NumPy.
  • Developed an interactive story map with ArcGIS Online and integrated it with Web AppBuilder to communicate findings and insights to a broader audience.

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    Introduction to Programming Spatial Data Analysis in Python

    Key Technologies: Python libraries, Jupyter Project tools, GeoPandas, Anaconda-Navigator, earthpy, GeoPandas

    2022

    Project Details

  • Utilized open-source Python libraries and Jupyter Project tools to conduct spatial analysis, focusing on the study areas of Antwerpen, Belgium, and Berlin, Germany.
  • Employed GeoPandas to manipulate spatial data, reproject layers, and calculate minimum distances between green urban areas and buildings.
  • Set up the environment using Anaconda-Navigator and integrated essential packages like earthpy and GeoPandas.
  • Analyzed and visualized land use, street lines, and building data for both study areas, effectively reprojecting coordinate systems and calculating distances between buildings and green areas.
  • Interpreted results, highlighting the significance of dataset selection and the impact of local conditions on spatial analysis outcomes.