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Day time, night time, over time: geographic and temporal uncertainty when linking event and contextual data
David C Folch1, Christopher S Fowler2, Levon Mikaelian3
1Department of Geography, Planning and Recreation, Northern Arizona University, PO Box 15015, Flagstaff, AZ, 86011, USA.
Summary
Spatial and temporal uncertainty in geolocated health studies varies significantly per individual. Understanding this uncertainty is crucial for accurate health outcome analysis, especially considering life course and positional factors.
Area of Science:
- Environmental epidemiology
- Geographic information systems (GIS)
- Public health research
Background:
- The increasing availability of geolocated data offers new research avenues in health, particularly linking individuals' environments to health outcomes.
- This research integrates individual-level data with aggregate socioeconomic or environmental data, representing individuals as points and context as polygons.
- Complex spatial representations introduce inherent uncertainties that require careful consideration.
Purpose of the Study:
- To assess the stability of spatial and temporal relationships between point and polygon data in health research.
- To develop and present four sensitivity analysis approaches for quantifying uncertainty in geolocated health studies.
- To understand how different factors like positional accuracy, neighborhood size, life course, and time of day contribute to overall uncertainty.
Main Methods:
- Utilized eight years of longitudinal child cohort data from Pennsylvania and North Carolina.
- Incorporated eight years of gridded air pollution and population data (0.5-mile resolution).
- Applied four sensitivity analysis methods: positional accuracy, neighborhood size, life course, and time of day.
Main Results:
- Spatial and temporal uncertainty exhibited significant variability across individuals in the cohort.
- Uncertainty was found to be greater over the life course and related to neighborhood size compared to positional accuracy.
- Time of day uncertainty was relatively low for the studied child cohort.
Conclusions:
- Spatial and temporal uncertainty must be evaluated individually in geolocated health studies due to considerable variation.
- The assumptions underpinning the source data significantly influence the measured levels of uncertainty.
- Acknowledging and quantifying uncertainty is essential for robust and reliable health outcome analyses.
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