This study addresses the challenge of selecting optimal locations for new businesses, particularly in the sports industry, using geospatial and behavioral data. Despite the growing importance of location intelligence, practical applications of geospatial analysis in business strategy remain limited, especially when using telecom and urban data. The methodology involves integrating machine learning techniques such as K-means clustering, association rule learning, and SHAP-based feature interpretation to analyze location suitability. By applying these methods to geospatial data, including visitor patterns, infrastructure, and demographics, the study uncovers key factors influencing business success. This approach offers a more data-driven, accurate method for identifying optimal business locations, enhancing decision-making in site selection. The findings highlight the critical role of location in business strategies and provide actionable insights for improving site selection strategies in the sports industry.