Conceptual analysis demonstrates benefits of hyper-local data in community health programs, indicating improved resource allocation.
Key Points
To introduce precision public health strategies that use granular, hyper-local data to guide community needs assessments and optimize Extension interventions.
Conceptual framework outlining the integration of data science metrics at zip code, census tract, and neighborhood levels.
Evaluation of targeted assessment models for county-based Extension professionals working with high-need populations.
Hyper-local geographic data allows practitioners to identify disparities at neighborhood scales rather than relying on broad regional estimates.
Precision targeting reduces program operational costs, increases the likelihood of measurable health outcomes, and creates more manageable workloads for field staff.