The text discusses how Spatial Data Science can be used to predict retail store revenues and help retailers stay ahead of the competition. The authors focus on enriching existing sales data with modern location-based features, training a predictive model that incorporates spatial components, and using techniques such as ensemble modeling, regression kriging, and feature importance analysis to improve the accuracy of revenue predictions. By leveraging these tools and techniques, business leaders can make informed decisions about store operations and expansion, protect their bottom line, and act intelligently in response to changing market conditions. The authors also emphasize the importance of model interpretability and provide examples using SHAP (SHapley Additive exPlanations) to understand how individual features contribute to prediction values.