Old Dominion University
CS620 - Intro to Data Science
Fall 2023
Click here to access a YouTube video created by us that overviews the project.
Welcome to our project, "Walkability and Well-Being - An Urban Analysis," where we explore the relationship between community walkability and various facets of well-being in urban environments, with a focus on New York City.
Many U.S. communities lack walkability and are heavily dependent on cars. Our goal is to investigate the correlation between community walkability and indicators of well-being, such as concentrated poverty rates and student obesity rates, with a focus on discerning potential connections to overall happiness.
To answer our research question, we employed a multi-faceted approach, beginning with the collection of walkability data for New York City neighborhoods from publicly available datasets. Additionally, data pertaining to the percentage of obese public elementary and middle school students and the rate of individuals living in concentrated poverty in New York City neighborhoods was gathered as indicators of community well-being and happiness. Subsequently, the collected datasets underwent thorough preprocessing, involving cleaning and filtering to focus on relevant metrics, such as the most recent year's data, and removal of irrelevant data. Following data preprocessing, the datasets were merged into one DataFrame, enabling a row-by-row comparison of walkability data with the well-being data to identify trends. Furthermore, various plots and charts were generated for data visualization to enhance our understanding. Ultimately, the correlation results were carefully interpreted, which has implications for urban planning, health initiatives, and policy-making.
Walkability&WellBeing_CS620_DataProject.ipynb: The Jupyter Notebook containing the entire project, from the description and methods to data preprocessing, analysis, and visualization.- The
Datasets/folder in this GitHub repository contains all the datasets utilized in the project, both cleaned and uncleaned. It also contains other datasets that could be explored in the future.
To execute Walkability&WellBeing_CS620_DataProject.ipynb, the user needs to compile each Google Colab cell section of code from top to bottom by using the keyboard shortcut Shift + Enter for each cell or by clicking the run button on the left of each cell. They may also use the keyboard shortcut CTRL + F9 to compile every cell, which may be faster.