The GAC will support investigators, clinicians, and community partners through four integrated objectives:

A. Parsing complex data critical for understanding disease onset and progression

1. The GAC will integrate multi‑level datasets, including:

   • Electronic health records (EHRs) and cancer registry data

   • Census and American Community Survey (ACS) data

   • Environmental and occupational exposure data

   • Transportation, housing, and neighborhood infrastructure data

   • Public health surveillance and administrative datasets

   • Can someone add Florida-specific examples?

2. Apply spatial linkage methods to connect individual‑level clinical data with place‑based contextual factors while preserving privacy and regulatory compliance.

3. Support hypothesis‑driven research examining spatial drivers of cancer risk, stage at diagnosis, treatment trajectories, toxicity, survivorship, and mortality.

B. Identifying, quantifying, and communicating patterns of cancer care across diverse communities

1. Characterize geographic variation in:

   • Screening uptake and diagnostic delays

   • Treatment access, modality choice, and adherence

   • Clinical trial availability and participation

   • Outcomes across racial, ethnic, rural/urban, and socioeconomic groups

2.  Develop intuitive spatial visualizations (maps, dashboards, and analytic summaries) for investigators, clinicians, and health system leaders.

3. Support community‑engaged research by translating spatial findings into formats usable by community partners, advocacy groups, and policymakers.

C. Transforming spatial data into actionable insights for policy and system‑level interventions

1. Inform strategic planning and resource allocation for:

   • Cancer prevention and early detection programs

   • Mobile screening, telemedicine, AI-facilitated oncology advisement, and outreach initiatives

   • Workforce and facility placement

   • Transportation and navigation services

2. Support evaluation of policy changes, pilot programs, and service innovations through before‑and‑after spatial analyses.

3. Provide data‑driven evidence to support institutional, local, and state‑level cancer control initiatives.

D. Developing novel algorithms and real‑time models to operationalize clinical interventions

1. Design predictive and prescriptive geospatial models that:

   • Identify communities at elevated cancer risk or with suboptimal access to care

   • Trigger targeted outreach, navigation, or care coordination efforts

   • Support real‑time learning health system applications

2. Collaborate with clinical and informatics teams to embed geospatial intelligence into operational workflows where feasible.

3. Advance analytical methods that support equity‑focused, place‑based precision medicine.

The GAC will provide fee‑for‑service and collaborative support across the cancer research and care continuum, including:

• Study design and consultation for grants and protocols incorporating spatial components

• Data integration and spatial linkage across clinical, population, and environmental datasets

• Geospatial modeling and advanced analytics

• Interactive visualizations and dashboards tailored to scientific, clinical, or administrative audiences

• Support for pilot projects and early‑stage investigators

• Methodological innovation in spatial epidemiology and health services research