The text discusses the use of spatial data science techniques, specifically human mobility data and optimization algorithms, to optimize market coverage for a bank expanding into new locations in southern Spain. The goal is to identify the 10 optimal locations that attract the largest number of visitors while minimizing cannibalization between branches. The methodology developed includes data discovery, support selection and cell enrichment with human mobility sociodemographics and commercial data, catchment area calculation, and optimization using an approximation algorithm based on a greedy approach and linear programming. The results show that the algorithm is able to identify 10 locations that cover a large number of potential customers, improving upon a standard greedy algorithm by approximately 11.75%. The methodology can be applied to other sectors facing similar challenges, such as retail, banking, and telco, to make informed decisions about location expansion.