Demographic & Business Dimensions
High-Impact Modeling Variables Built for Data Scientists & Analysts
The AGS Dimensions datasets use statistical modeling to summarize hundreds of attributes into a handful of powerful, uncorrelated factors that describe the character of neighborhoods and commercial areas.
These interpretable variables make modeling faster, cleaner, and easier to explain without sacrificing insight.
The Dimensions datasets include both demographic and business variables, offering a streamlined summary of broad, complex data inputs.
Built to support advanced modeling and signature-based site selection, these tools provide the spatial intelligence behind smarter scoring, clustering, and segmentation strategies.
What’s Included
- 31 Demographic Dimensions describing social, economic, educational, and household dynamics
- 28 Business Dimensions covering industry presence, employee mix, ownership type, and commercial density
- Derived using Principal Components Analysis (PCA) for low redundancy and high interpretability
- Ideal for use in statistical modeling, clustering, and site signature comparisons
Built for Industry
Retail & Restaurant
Rank and score potential sites by comparing them to high-performing locations using shared demographic or commercial traits.
Real Estate
Build more predictive property models using dimensionally reduced neighborhood profiles instead of hundreds of raw variables.
Finance & Insurance
Identify opportunity zones or emerging risks using interpretable market factors tied to household and business conditions.
Healthcare
Understand patient base characteristics and market demand through neighborhood-level segmentation and scoring.
Marketing & Analytics
Improve direct marketing models and lookalike targeting using clean, interpretable inputs for regression and clustering.
Technology & GIS
Power internal scoring systems, ML pipelines, and data enrichment tools using statistically balanced geographic attributes.
Delivery Formats
- CSV, dBase, or flat file for integration into modeling workflows
- SnapSite and Snapshot API available for geospatial applications
- National and local geographies down to the block group level
Methodology
AGS applies Principal Components Analysis (PCA) to create synthetic, continuous variables that summarize key traits across geographies.
PCA reduces multicollinearity, speeds up modeling, and simplifies interpretability—especially valuable for spatial data science and predictive analytics.
The inputs are updated annually and based on the latest AGS base datasets. The results are scaled and standardized for direct use in analytical models or site comparison.
Documentation & Support
Each dataset includes thorough documentation on input variables, PCA loadings, and variable definitions. Our team is available to assist with model integration, interpretation, or cross-referencing against other AGS data.