[2O4-OS-21a] OS-21

[2O4-OS-21a-04] Clinic Sales Prediction Using a Data-Driven Approach with Satellite Images and Geographical Information

〇Shuntaro Masuda¹, Fumiya Matsuno², Itsuki Hirai², Koji Muta², Shinnosuke Onai², Toshihiko Yamasaki¹ (1. Graduate School of Information Science and Technology, The University of Tokyo, 2. MD Inc.)

Keywords:Spatial Data Mining, Satellite Image Analysis, Large Multimodal Models (LMMs)

In the medical industry, location selection is crucial for business success, yet many decisions are still based on experience and intuition. For clinics, with limited access to sales data and available samples, implementing data-driven decision-making for small sample sizes remains a significant challenge.
This study proposes a sales prediction method combining geographic information with characteristics extracted from satellite imagery using GPT-4o, such as urbanization levels and building ratios. We adopted Support Vector Regression (SVR) to achieve accurate predictions while preventing overfitting with small samples. Furthermore, we developed a data-driven approach that optimizes prediction model variables through correlation analysis for confounding factor exclusion and feature selection algorithms. Experimental results confirmed that regional characteristics enable accurate sales predictions with limited datasets. We verified that characteristics derived from satellite imagery improved prediction accuracy compared to baseline methods using only geographic information. This methodology shows promise for location selection across various industries beyond the medical field.