Library Usage & Community Demographics Analysis

DSA 210 Spring 2025 Term Project

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Motivation

Public libraries serve as critical community resources, offering free access to education, technology, and cultural programs. However, library engagement—such as book checkouts or event attendance—varies widely across neighborhoods. These disparities often reflect underlying socioeconomic factors like income levels, education, and population density. By analyzing library usage alongside demographic data, we can uncover patterns that guide fair resource distribution and advocate for data-driven policy decisions.

This project focuses on answering two key questions:

1. How do community demographics correlate with library engagement metrics (e.g., checkouts, program attendance)?
2. Can we predict library usage patterns using socioeconomic indicators?

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Data Sources

1. Library Usage Data

• Source: IMLS Public Library Survey (2022)
• Key Features:
  • TOTCIR: Total circulation of materials
  • VISITS: Total annual library visits
  • REGBOR: Number of registered users
  • TOTSTAFF: Total paid FTE employees
  • GPTERMS: Internet computers used by the general public
  • HRS_OPEN: Total annual public service hours for all service outlets
  • TOTPRO: Total number of synchronous program sessions
  • TOTATTEN: Total attendance at synchronous programs
  • TOTINCM: Total operating revenue
  • TOTOPEXP: Total operating expenditures
  • POPU_UND: Unduplicated population of the legal service area for the library
  • ZIP_CODE: Administrative ZIP code of the library system

2. Community Demographics Data

• Source: US Census ACS 5-Year Estimates (2022)
• Key Features:
  • MEDIAN_INCOME: Median household income by ZIP Code Tabulation Area (ZCTA)
  • BACHELORS_PERCENT: Percentage of population with a bachelor’s degree or higher
  • UNEMPLOYMENT_RATE: Unemployment rate
  • ZIP_CODE: ZIP code

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Data Collection

1. IMLS Data:

   • Downloaded the PLS_FY22_AE_pud22i.csv file from the IMLS website.
   • Filtered columns to retain ZIP_CODE, TOTCIR, VISITS, REGBOR, TOTSTAFF, TOTATTEN, TOTPRO, GPTERMS, TOTINCM, TOTOPEXP, HRS_OPEN, POPU_UND.

2. Census Data:

   • API Integration: Fetched data using the US Census Bureau API (ACS 5-Year Estimates 2022).
     • Tools: Python census library with a registered Census API key.
     • Features: Extracted B19013_001E (median income), B15003_022E (% bachelor’s degree), and B23025_005E (unemployment rate).
     • 📎 fetch_census_data.py

• Merged datasets on ZIP_CODE: 📎 merge-datasets.py

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Presentation: 📎 DSA210TermProject.pdf or DSA210TermProject.pptx