You are working on a project to train a model aimed at predicting housing prices using a provided dataset of real estate properties. The dataset includes various features, one of which is the latitude and longitude of each property. Given that real estate experts have emphasized the significant impact of a property's location on its market price, you have decided to integrate this geographic information into your predictive model. You plan to train a fully connected neural network. Considering the critical nature of location in determining real estate prices, what approach should you take to effectively engineer a feature that captures this geographic dependency?