Geospatial Data Apportionment for Retail Site Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing geospatial analysis systems lack sufficient granularity for accurate decision-making, as raw data is often provided at a low level, such as ZIP code basis, which limits precision in evaluating market strength and retail location siting, especially in urban and rural areas.
Innovation Solution
The development of a high-granularity geospatial database through algorithms that allocate data to smaller geographic areas, enabling 'street-corner' decision-making by creating greenfield and brownfield scores for assessing retail location networks, evaluating potential new locations, and identifying optimal retail partners or merger targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is provided at ZIP code level granularity, then data coverage area is large, but measurement precision of geographic areas is insufficient
Solution Approach 1:
The patent segments ZIP code level data into smaller census tract level geographic areas, enabling more precise measurement of market strength and demographic characteristics. This segmentation allows evaluation at a granularity of less than one square mile while maintaining comprehensive data coverage through systematic division of larger geographic units into smaller analytical units.
2Measurement precision
If greenfield benchmark scoring is performed by sequentially degrading surrounding areas, then optimal location selection is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary degradation adjustments to surrounding geographic areas before final score calculation. By pre-modifying the scores of adjacent areas based on the selected location's impact, the system efficiently captures spatial interdependencies without requiring complex iterative computations during the final evaluation phase.
Solution Approach 2:
The degradation factor serves as an intermediary mechanism that mediates the impact of one location on surrounding areas. This intermediary approach simplifies the computational process by using a standardized adjustment factor rather than complex multi-variable interactions, reducing overall system complexity while maintaining scoring accuracy.
Data Source
AI summary
A method and processing system for assessing of the health of a network of retail locations in a defined geographic area, by a process including the development of a greenfield benchmark score for a network of ideally sited locations, followed by the development of a brownfield score of current locations, for comparison to the greenfield score. The invention is further applicable to decisions to open a new location, relocate an existing location or close an existing location. In these cases, the effect of opening a new location, or relocating or closing an underperforming location is evaluated by recalculating the brownfield score after the addition of each of several potential new locations, and/or after relocating or closing each of several poorly performing locations.