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Making Sense of Sensor Data: How Local Environmental Conditions Add Value to Social Science Research.

Ned English1, Chang Zhao1, Kevin L Brown1

  • 1NORC at the University of Chicago.

Social Science Computer Review
|April 11, 2022
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Summary
This summary is machine-generated.

New environmental sensors provide local air quality data, linking pollution exposure to respiratory and dementia diseases in elderly Chicago residents. This technology enhances neighborhood health research.

Keywords:
Air QualityEnvironmental ExposureGISHealthNeighborhoodPollutionSensors

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Area of Science:

  • Environmental Science
  • Public Health
  • Computer Science

Background:

  • Advances in computing enable low-cost, compact weather and air quality monitors.
  • The Array of Things (AoT) project deployed over 140 sensor nodes in Chicago.
  • Traditional neighborhood research often lacks detailed environmental data.

Purpose of the Study:

  • To explore the feasibility of using AoT sensor data to improve neighborhood research.
  • To investigate the link between local air pollution and health outcomes in elderly populations.
  • To pilot the use of novel sensor data in social science research.

Main Methods:

  • Combined one year of AoT sensor data with household data from 450 elderly Chicagoans.
  • Analyzed environmental conditions and health data to identify correlations.
  • Utilized data from the Array of Things (AoT) sensor network.

Main Results:

  • Found support for recent findings linking air pollution exposure to adverse health outcomes.
  • Demonstrated a connection between environmental pollutants and respiratory diseases.
  • Indicated a correlation between pollution exposure and dementia-related diseases.

Conclusions:

  • Local environmental sensor data can enhance traditional neighborhood research.
  • AoT data provides valuable insights into the health impacts of air quality.
  • Future improvements in sensing technology will strengthen these findings and enable broader applications.