Using High-Resolution Population Data to Identify Neighborhoods and Establish Their Boundaries

Area of Science:

• Urban Sociology
• Spatial Statistics
• Historical Geography

Background:

• Defining neighborhoods is crucial for understanding urban social dynamics.
• Traditional neighborhood definitions often rely on fixed boundaries or single attributes.
• A more nuanced approach is needed to capture the complex spatial and social composition of neighborhoods.

Purpose of the Study:

• To develop and apply a novel method for inferring neighborhood boundaries.
• To define neighborhoods based on both spatial contiguity and social composition.
• To analyze the spatial structure of late 19th-century American cities.

Main Methods:

• Application of local spatial statistics to geocoded census data.
• Inference of neighborhood boundaries from individual-level residential data.
• Analysis of complete-count census data from Albany, Buffalo, Cincinnati, and Newark (late 19th century).

Main Results:

• Identified distinct neighborhoods characterized by unique social attributes and spatial configurations.
• Revealed striking regularities in the spatial structure across the studied cities.
• Observed some notable anomalies in neighborhood formation and spatial organization.

Conclusions:

• The proposed method effectively infers neighborhoods without pre-defined social characteristics or spatial scales.
• Demonstrates the 'spatialization' of the neighborhood concept in historical urban settings.
• Highlights the utility of spatial statistics for social scientific inquiry into urban form.