Graduation Semester and Year
Fall 2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Engineering
Department
Computer Science and Engineering
First Advisor
Leonidas Fegaras
Second Advisor
David Levine
Third Advisor
Bahram Khalili
Fourth Advisor
Ashraf Aboulnaga
Fifth Advisor
Ramez Elmasri
Abstract
Currently, spatial geographic data can be collected for many applications that involve data on the planet earth. These collected data typically have coordinates (x,y), or longitude and latitude in map space, and thus can be located and displayed on maps. Data alone represents facts and has no meaning on its own but becomes meaningful when it is associated with application knowledge, such as elections, crimes, disease, etc. For example, there is no meaning behind those numbers (1, 23, 125, 355, . . .), yet they are data that can gain meaning when correlated with the total number of cases of COVID-19 in Texas per day starting on a certain date. Many devices can provide sequences of object location data over time (GPS in vehicles or mobile devices, etc.). However, no device can visualize or display them on its own without a visualization App. Both numeric and location data are raw data that need to be pre-processed and cleaned to become meaningful.
Currently, collected data is a very valuable source of information which, after collection, can be processed, stored, analyzed, and visualized. In this dissertation, the available techniques for spatial data visualization will be overviewed, and an emphasis is placed on the development of interactive, web-based visualization tools designed for public health applications.
This dissertation presents a suite of interactive, web-based visualization tools developed to facilitate the analysis of spatial and spatio-temporal data, with a specific emphasis on public health applications. Through tools like STPHViz and the COVID-19 and Flu Data Visualizer, it provides practical approaches for visualizing complex data patterns, offering insights and recommendations for effectively leveraging visualization in public health research and decision-making.
A case study of COVID-19 spatio-temporal data visualization, using one of these techniques, will be demonstrated. The COVID-19 data will be spatially visualized when data on a specific date is queried for analysis. On the other hand, spatio-temporal visualization will be displayed when a time series of COVID-19 data is queried for analysis.
This dissertation thus contributes a set of visualization tools and guidelines for public health applications, emphasizing interactive, web-based approaches for analyzing spatial and spatio-temporal data. The tools developed enable users to explore trends and patterns in COVID-19 and influenza data across various U.S. counties, integrating demographic and socioeconomic data like median household income for richer contextual insights. This framework provides a valuable resource for researchers, policymakers, and public health officials in analyzing and responding to public health challenges.
Keywords
Spatio-temporal data, public health visualization, COVID-19 data analysis, geospatial visualization, spatial data integration, disease mapping, interactive dashboards, synthetic data generation, flu trends, health informatics
Disciplines
Computer Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
shaito, mohammad, "Web-based Visualization of Spatial and Spatio-Temporal Data Using Integrated Datasets" (2024). Computer Science and Engineering Dissertations. 398.
https://mavmatrix.uta.edu/cse_dissertations/398