A Data Analysis Study of Temperature Based on California Cooperative Oceanic Fisheries Investigations (CALCOFI) Data.

Author

• Afsana Mimi, The Graduate Center, City University of New YorkFollow

Date of Degree

6-2026

Document Type

Master's Capstone Project

Degree Name

Master of Science

Program

Data Analysis & Visualization

Advisor

Howard Everson

Subject Categories

Computational Engineering | Environmental Studies | Geological Engineering

Abstract

This research presents an integrated analytical framework for predicting ocean temperatures using long-term observations from the California Cooperative Oceanic Fisheries Investigations (CalCOFI) data files. The study applies advanced data engineering and statistical modeling to assess how a variety of indicators, such as salinity, dissolved oxygen, key nutrients, and ocean depth shape oceanic temperature patterns along the California coastline. Using Microsoft SQL Server software for large-scale data integration and the R for statistical packages (R Core Team, 2025) for statistical computation, I developed and compared multiple regression models, including the ridge regression model, to evaluate the relative influence of physical and chemical factors on ocean temperatures. The final multiple linear regression model achieved an adjusted R² of 0.86, indicating that a combination of depth, salinity, oxygen saturation, and nutrient concentrations explained approximately 86% of the variation in the ocean temperature along the California coastline. These findings demonstrate that oceanic temperature predictions improve significantly when variables are combined into a unified model rather than analyzed independently. By merging data visualization, rigorous statistical methods, and accessible web technologies, this project bridges the gap between technical analysis with environmental understanding—supporting marine scientists, educators, and policymakers in examining the effects of climate change on coastal ecosystems.

The project can be found at: https://afsana25.github.io/DataVizCapstoneProject/ The code repository is available at: https://github.com/afsana25/DataVizCapstoneProject

Comments

Online component: https://afsana25.github.io/DataVizCapstoneProject/

Recommended Citation

Mimi, Afsana, "A Data Analysis Study of Temperature Based on California Cooperative Oceanic Fisheries Investigations (CALCOFI) Data." (2026). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/6727