Investigating the Effects of Local Weather Conditions on Activity Spaces among Elderly Chicagoans Using a Spatiotemporal Approach

Keywords: Activity Space, Weather Condition, Environmental Sensors, GPS, Human Mobility, Spatiotemporal Modeling
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 08:00 AM (Eastern Time (US & Canada)) - 3/1/2022 09:20 AM (Eastern Time (US & Canada))
Room: Virtual 41

Abstract

Recent advances in computing technologies have enabled the development of low-cost, compact weather and air quality monitors. The NSF-funded Array of Things (AoT) project based at Argonne National Laboratory has deployed more than 100 such sensors throughout the City of Chicago. This study combines longitudinal AoT sensor data with household survey and location tracking data collected from 450 elderly Chicagoans 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 weather conditions to the activity levels of older adults. We incorporate GPS trajectory data collected in 3 survey waves (April 2018, Dec 2018, and June 2019) to investigate daily and seasonal changes in activity patterns within and across individuals. We also examine associations between daily weather conditions and daily activity space sizes, accounting for individual characteristics and neighborhood effects. In so doing, we provide a methodology for linking CHART survey data with AoT sensor data to examine the linkage between immediate environmental exposure and the observed behaviors of elderly Chicagoans.