Open AccessMaking Sense of Sensor Data: How Local Environmental Conditions Add Value to Social Science Research
Author(s) -
Ned English,
Zhao Chang,
Kevin L. Brown,
Charlie Catlett,
Kathleen A. Cagney
Publication year - 2020
Publication title -
social science computer review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.3
H-Index - 73
eISSN - 1552-8286
pISSN - 0894-4393
DOI - 10.1177/0894439320920601
Subject(s) - sense (electronics) , value (mathematics) , sociology , data science , computer science , environmental science , statistics , mathematics , engineering , electrical engineering
Recent advances in computing technologies have enabled the development of low-cost, compact weather and air quality monitors. The U.S. federally funded Array of Things (AoT) project has deployed more than 140 such sensor nodes throughout the City of Chicago. This paper combines a year's worth of AoT sensor data with household data collected from 450 elderly Chicagoans in order to explore the feasibility of using previously unavailable data on local environmental conditions to improve traditional neighborhood research. Specifically, we pilot the use of AoT sensor data to overcome limitations in research linking air pollution to poor physical and mental health and find support for recent findings that exposure to pollutants contributes to both respiratory and dementia-related diseases. We expect that this support will become even stronger as sensing technologies continue to improve and more AoT nodes come online, enabling additional applications to social science research where environmental context matters.