Semester of Graduation

Fall 2025

Degree

Master of Science in Computer Science (MSCS)

Department

Computer Science and Engineering

Document Type

Thesis

Abstract

Data visualization is an essential part of analyzing environmental geospatial data. Despite having the availability of large environmental datasets, there remains a lack of easily accessible, user-friendly, and interactive visualization tools in this field. Therefore, this study aims to develop a user-friendly and easily available web-based visualization tool. Before developing the tool, we conducted a survey of researchers at the LSU Coastal Studies Institute to collect their opinions on currently available visualization tools. In the survey, 55% of participants responded between somewhat satisfied to dissatisfied with their current visualization tools. Most of the participants mentioned two major limitations of existing visualization tools. These limitations are overly complex interfaces of the existing visualization tools and the need for basic programming knowledge to use these tools. To address these issues, we have developed EnVis, an easily accessible, interactive, and dynamic web-based tool that provides effective data analysis and visualizations for large environmental datasets. EnVis combines a React-based frontend and a Flask backend, which delivers user-friendly and responsive map-based data visualizations. The key feature of this web tool is that users can upload their own environmental datasets for quick visualization. On the Leaflet map, users can hover over and click on each datapoint to examine individual grid-cell values in detail without writing a single line of code. Researchers can also assess their large datasets and make informed decisions whether they want to invest their time in the datasets for further analysis. Additionally, EnVis provides daily visualization for two widely used environmental variables, such as sea surface temperature (SST) and atmospheric dust. These daily visualizations help researchers to explore seasonal patterns and trends in SST and atmospheric dust more easily and effectively. After developing EnVis, we conducted a follow-up survey to evaluate its accessibility and feature usefulness of this tool. In the survey, 77% of participants rated the EnVis interface as excellent, 92% of participants rated the CSV upload feature of EnVis as very clear and easy, and 92% of participants mentioned that they will use EnVis and recommend it to other environmental researchers. Overall, these results indicate that EnVis is a promising web-based visualization tool for environmental researchers to analyze and visualize large-scale dataset.

Date

11-12-2025

Committee Chair

Mahmood Jasim

Additional Files
Shila - newthesis_approval_sheet_fillable (6) (1).pdf (439 kB)
Student Approval Forms
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Shila - newthesis_approval_sheet_fillable (6) (1).pdf (439 kB)

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