Overview | 1. Get a Census API Key | 2. Identify Your ZCTAs | 3. Get Baseline ACS Data | 4. Get ZIP-ZCTA Crosswalk | https://github.com/chris-prener/uds-mapper | 5. Get HUD Crosswalk | 6. Run the Audit | Copy-Paste Ready Code | Data Structure Requirements | Baseline Data | ZIP-ZCTA Crosswalk | HUD Crosswalk | Validation | Next Steps

R Markdown | Why Crosswalks Create Delta | Proportional Allocation Is an Assumption | Direction Matters (ZCTA → ZIP ≠ ZIP → ZCTA) | What geoDeltaAudit Measures

The Shell Game | What the Shell Actually Is | The Critical Shift | The Transformation Chain | Pre-allocation | First Claim | The Two Hidden Decisions | Decision 1: Membership Definition | Decision 2: Crosswalk Selection | The Result | Why This Is Agnostic | What This Package Does

Introduction | The Workflow | Step 1: Prepare the Data | Step 2: Run the Audit | Step 3: View Results | Membership Visualization | Baseline: 74 ZCTAs | The First Hop: ZCTA → ZIP | The Second Hop: ZIP → County | The Perturbation | Geometric vs Relationship Membership | Visualizing the Difference | The Shell Game Revealed | Why This Matters | Next Steps

Geographic Data Transformation Auditing: Why Variable Agnosticism Matters | The Problem in Population Health | For the first hop, we construct a ZCTA–ZIP association table by expanding the ZIP→ZCTA relationship file (i.e., grouping ZIPs by their assigned ZCTA). This produces a one-to-many mapping (ZCTA→{ZIPs}) that reflects common “lookup-style” workflows in applied settings. | Decision Points Framework | Variable Agnosticism Design

2) Libraries (minimal) | Association diagnostics | What this vignette demonstrates

2) Libraries (minimal) | Geographic Data Transformation Audit Workflow | Overview | Setup and Data Preparation | Step A: Association Construction | Association diagnostics | Key assumption: | Interpreting Results Tables | Visualizing Perturbations | What this vignette demonstrates

Summary