Project Overview

This project explores U.S. household income distributions across cities, counties, and states. Using SQL to clean and structure multiple datasets and Power BI to visualize macro-economic trends, I built an analytical solution that highlights income inequality, growth patterns, demographic correlations, and cost-of-living impacts.

Technical Workflow

SQL Data Cleaning & Standardization: Removed duplicates, aligned geographic identifiers, corrected inconsistent naming, and formatted income ranges for analysis.

Data Modeling: Integrated census-level datasets into a relational structure supporting drill-downs from national → state → city.

Power BI Dashboarding: Built dynamic reports with filters, decomposition trees, geographic maps, percentile breakdowns, and trend projections.

KPI Design: Median household income, poverty rates, cost-burden metrics, regional disparities, and demographic segmentation.

Key Insights

High-income clusters correlate strongly with employment sectors such as tech, finance, and biotech.

Rural counties show steeper declines in income growth compared to metropolitan regions.

Cost of living does not always align with income levels — several cities exhibit high income but low disposable income after expenses.

Income inequality widens dramatically in states with large urban–rural divides.

Business Impact

This analysis supports policymakers, housing authorities, and financial analysts by offering a clear, data-driven understanding of economic conditions across the U.S.