Woo, J. (2023, June), Board 29: Compiling Census Data and Atmospheric Repository Data to Infer Socio-Environmental Trends  Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42786

\bibitem{asee_peer_42786}
  Woo, J. (2023, June), \emph{Board 29: Compiling Census Data and Atmospheric Repository Data to Infer Socio-Environmental Trends}
  Paper presented at 2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland.
  10.18260/1-2--42786

Joe Woo.  "Board 29: Compiling Census Data and Atmospheric Repository Data to Infer Socio-Environmental Trends".  2023 ASEE Annual Conference & Exposition, Baltimore , Maryland, 2023, June.  ASEE Conferences, 2023.  https://peer.asee.org/42786 Internet.  03 May, 2024

\bibitem{asee_peer_42786}
  Joe Woo.
  "Board 29: Compiling Census Data and Atmospheric Repository Data to Infer Socio-Environmental Trends".
  \emph{2023 ASEE Annual Conference \& Exposition, Baltimore , Maryland, 2023, June}.
  ASEE Conferences, 2023.
  https://peer.asee.org/42786 Internet.  03 May, 2024

@INPROCEEDINGS{asee_peer_42786
author = "Joe Woo"
title = "Board 29: Compiling Census Data and Atmospheric Repository Data to Infer Socio-Environmental Trends"
booktitle = "2023 ASEE Annual Conference \& Exposition"
year = "2023"
month = "June"
address = "Baltimore , Maryland"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/42786}
number = {10.18260/1-2--42786}
}

TY  - CPAPER
AB  - In engineering coursework, it can be difficult to find real-world datasets that convey meaningful, correlative relationships between measurable phenomena and relevant social issues. With the recently-completed US Census, a comprehensive and publicly-available set of geospatially distributed information about a variety of factors (e.g., race, gender, income, etc.) can be compared against the increasingly numerous pollutant sensors that are measuring air quality around the United States. Students were tasked with selecting census data for a minimum of five zip codes near their homes. After hypothesizing what social factors would affect ambient air quality, students extracted relevant census data and compiled their findings against one year of historical NO, NO2, and ozone concentration measurements from EPA Air Quality monitors in the same zip code. As they find trends in their results, both expected and unexpected, students also develop a deeper understanding of the physical drivers behind air quality and the computational skills necessary to align, clean, and process their data. The open-ended nature of this project, combined with the direct connection between the students’ home neighborhoods and the data being collected, fosters student investment and curiosity in their analysis. 
AU  - Joe Woo
CY  - Baltimore , Maryland
DA  - 2023/06/25
PB  - ASEE Conferences
TI  - Board 29: Compiling Census Data and Atmospheric Repository Data to Infer Socio-Environmental Trends
UR  - https://peer.asee.org/42786
DO  - 10.18260/1-2--42786
ER  -