Quantifying Retail Agglomeration using Diverse Spatial Data.

Area of Science:

• Urban Economics
• Spatial Modeling
• Retail Geography

Background:

• Existing consumer choice models operate at an aggregate level.
• New granular data on retail spatial distribution requires advanced theoretical frameworks for analysis.
• Lack of micro-level models hinders understanding of individual retailer dynamics.

Purpose of the Study:

• To develop a theoretical framework for modeling retail location choice at the individual retailer level.
• To quantify the influence of floor space and retail agglomeration on store attractiveness.
• To address limitations in current urban simulation models using fine-grained spatial data.

Main Methods:

• Development of a new model based on random utility theory.
• Quantification of retailer attractiveness using floor space as a key variable.
• Approximation of agglomeration effects by measuring total retail floor space within a 300m radius.

Main Results:

• Empirical testing on Greater London's inner area reveals a super-linear relationship between floor space and retailer attractiveness.
• Significant agglomeration effects were observed, driven by the density of retail floor space.
• The study validates the model's ability to integrate and analyze spatial data at different scales.

Conclusions:

• The proposed model provides a robust framework for micro-level retail location analysis.
• Floor space and local retail density are critical determinants of individual store success.
• Findings inform the development and validation of sophisticated urban simulation tools.