Authors: Kristianny Ruelas-Vargas and Audrey Yun
Kristianny Ruelas-Vargas and Audrey Yun both completed ITGH's summer program in "Health, Technology, & Society," in collaboration with the Data-Smart City Solutions Program at the Harvard Kennedy School.
Across the US, city government officials are creating climate-conscious policies to address the environmental health complications that result from poor air quality and extreme heat. Data is a driving force behind these policies, with sources such as fire department calls, hospitalization records, air quality monitoring systems, and weather data informing decision-making. Much of this data is also being spatially mapped and analyzed with geographic information systems (GIS) applications, which can then be used by multiple agencies to create vulnerability index maps. These maps are essential for identifying at-risk communities and guiding policymakers and local officials in implementing environmental and public health programs, policies, or projects.
This article explores how cities can use not just location-based but also time-bound data to address environmental health issues, specifically around extreme heat and poor air quality. This piece focuses on two types of time-bound data, real-time and longitudinal, with examples from across the country that highlight why breaking data down temporally is crucial for developing and refining data-driven policies.
Real-time data: provides immediate insights into current conditions of a location. Examples include the US Air Quality Index AirNow.gov and the OSHA-NIOSH Heat Safety Tool app.
Longitudinal data: refers to data that has been collected over an extended period of time and provides insights into long-term trends, impacts, and changes over time . Examples include the National Longitudinal Study of Adolescent to Adult Health and NASA’s GISS Surface Temperature Analysis.
Of course, most initiatives and policies rely on a combination of these types of data. Real-time data helps cities respond to immediate needs of the community and longitudinal data ensures that long-term environmental and public health trends are being analyzed and addressed. The following examples will highlight how these two types of time-specific data lend themselves to different projects or goals; intermediate
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