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 …

The ubiquitous availability of datasets has spurred the utilization of Artificial Intelligence
methods and models to extract valuable insights, unearth hidden patterns, and predict future …

The widespread use of personal geospatial data raises serious geoprivacy concerns for
sharing these data, which may limit the reproducibility of research findings. One widely used …

Long-term exposure to contaminated water can cause health effects, such as cancer.
Accurate spatial prediction of inorganic compounds (eg arsenic) and pathogens in …

Address and location challenges are global, yet sub‐Saharan Africa (SSA) is the most
poorly referenced geographically due to poor spatial referencing of mobility infrastructure …

Geocoding, the process of converting a street address into a pair of coordinates that facilitate
mapping, is a perennial challenge for researchers. Both technical and practical barriers exist …

Environmental epidemiologic studies using geospatial data often estimate exposure at a
participant's residence upon enrollment, but mobility during the exposure period can lead to …

Abstract Majority African‐American neighborhoods on the edges of North Carolina
municipalities are less likely than white peri‐urban neighborhoods to be served by a …

Objective The ability to apply results from a study to a broader population remains a primary
objective in translational science. Distinct from intrinsic elements of scientific rigor, the …

Chronic exposure to arsenic-contaminated drinking water is detrimental to human health.
We develop an autologistic regression model to evaluate if the geology, pH, and well depth …