TY - JOUR
T1 - Uncertainty in geospatial health
T2 - challenges and opportunities ahead
AU - Delmelle, Eric M.
AU - Desjardins, Michael R.
AU - Jung, Paul
AU - Owusu, Claudio
AU - Lan, Yu
AU - Hohl, Alexander
AU - Dony, Coline
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2022/1
Y1 - 2022/1
N2 - Purpose: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. Methods: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. Results: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. Conclusions: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.
AB - Purpose: Uncertainty is not always well captured, understood, or modeled properly, and can bias the robustness of complex relationships, such as the association between the environment and public health through exposure, estimates of geographic accessibility and cluster detection, to name a few. Methods: We review current challenges and future opportunities as geospatial data and analyses are applied to the field of public health. We are particularly interested in the sources of uncertainty in geospatial data and how this uncertainty may propagate in spatial analysis. Results: We present opportunities to reduce the magnitude and impact of uncertainty. Specifically, we focus on (1) the use of multiple reference data sources to reduce geocoding errors, (2) the validity of online geocoders and how confidentiality (e.g., HIPAA) may be breached, (3) use of multiple reference data sources to reduce geocoding errors, (4) the impact of geoimputation techniques on travel estimates, (5) residential mobility and how it affects accessibility metrics and clustering, and (6) modeling errors in the American Community Survey. Our paper discusses how to communicate spatial and spatiotemporal uncertainty, and high-performance computing to conduct large amounts of simulations to ultimately increase statistical robustness for studies in public health. Conclusions: Our paper contributes to recent efforts to fill in knowledge gaps at the intersection of spatial uncertainty and public health.
KW - American community survey
KW - GIS
KW - Geocoding
KW - Geoimputation
KW - High-performance computing
KW - Residential Mobility Simulations
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85119407272&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119407272&partnerID=8YFLogxK
U2 - 10.1016/j.annepidem.2021.10.002
DO - 10.1016/j.annepidem.2021.10.002
M3 - Review article
C2 - 34656750
AN - SCOPUS:85119407272
SN - 1047-2797
VL - 65
SP - 15
EP - 30
JO - Annals of epidemiology
JF - Annals of epidemiology
ER -