Purpose of This Methodology Module
This module defines the limits of aggregation when interpreting residential data at the city level. Its purpose is to explain why aggregated readings can obscure structural differences, distort visibility signals, and compress heterogeneous residential environments into misleading summaries. The focus is on interpretation discipline rather than analytical outcomes.
Aggregation as an Analytical Convenience
Aggregation is commonly used to simplify complex residential landscapes into a manageable analytical frame. At the city level, aggregation groups together districts and submarkets with distinct urban forms, housing typologies, and listing behaviors. While this convenience enables high-level organization, it removes contextual detail that is essential for accurate interpretation.
Compression of Visibility Dynamics
Listing-based datasets reflect uneven participation and rotation across locations. When aggregated, areas with high publication frequency can dominate visibility, while structurally underrepresented areas recede from view. This compression effect alters the apparent distribution of residential activity without changing the underlying structure that produced the listings.
Loss of Boundary-Specific Meaning
Residential boundaries carry analytical meaning at submarket and district scales. Aggregation dissolves these boundaries, blending areas with different publication mechanics and spatial roles into a single measure. As a result, signals that are meaningful within a defined boundary lose interpretive value once merged into a citywide total.
Interpretation Boundaries for Aggregated Views
Aggregated city-level views should be treated as orientation layers rather than explanatory endpoints. They can indicate where listings circulate at a broad level but cannot support conclusions about residential structure, scale, or composition. This module establishes a clear boundary against extrapolating aggregated observations beyond their organizational function.