City Of Boston: Uncovering Insights Through Data-Driven Approaches
Project Overview
In collaboration with the City of Boston and our client, Hope R., our team embarked on a comprehensive analysis of Boston's housing landscape. The primary focus of this project was to investigate the impact of renovations on bedroom and living area changes, as well as to examine the demographic composition of affected neighborhoods. By leveraging data-driven approaches and advanced analytical techniques, we aimed to uncover valuable insights that could inform policymakers and stakeholders in developing equitable and sustainable housing solutions.
Slides We Presented
Team and Leadership
I had the privilege of leading a talented and dedicated team consisting of Ashley Harlow, Maha Alali, Wilbert Limson, and Anderson Lawrence. As the team lead, my responsibilities included:
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Organizing and managing our team's Trello board for efficient task allocation and tracking
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Coordinating with the project manager and client to provide regular updates on our progress
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Facilitating effective communication and collaboration among team members
My Contributions pt.1
My primary contributions to the project encompassed the following:
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Investigating the impact of renovations on bedrooms and living areas (pages 17-31):
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Analyzing trends in the average number of bedrooms and living area per property over time
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Exploring the correlation between changes in living area and bedroom count due to renovations
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Conducting linear regression analysis to identify potential predictors of bedroom and living area changes
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My Contributions pt.2
Examining the demographic composition of neighborhoods affected by housing changes (pages 39-45) in collaboration with Maha Alali:
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Mapping the modal age group, race, and household type per neighborhood
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Analyzing the demographic breakdowns of significantly impacted neighborhoods like Brighton and Dorchester
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Providing insights into the populations potentially affected by housing market shifts
My Contributions pt.3
Developing and contributing to key Jupyter notebooks:
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Variables_in_Property_Assesment-Yearly_Analysis.ipynb
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Renovations&Bedrooms_Analysis.ipynb
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Living Room Correlation.ipynb
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Linear Regression on Bedrooms.ipynb
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Updated_Heatmap notebooks
Organizing and compiling both the GitHub and Report.
Key Findings
Through our analysis, we arrived at several key takeaways:
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Renovations, on average, do not result in a loss of bedrooms or living area
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Changes in living area and bedroom count during renovations are largely independent
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Neighborhoods such as Brighton (with a majority young, White population) and Dorchester (with a majority middle-aged, Black/African-American population) experienced significant housing market changes, though we can't conclude which specific demographics have been affected by these changes.