Users increasingly rely on their mobile devices to search, locate and discover places and activities around them while on the go. Their decision process is driven by the information displayed on their devices and their current context (e.g. traffic, driving or walking etc.). Even though recent research efforts have already examined and demonstrated how different context parameters such as weather, time and personal preferences affect the way mobile users click on local businesses, little has been done to study how the location of the user affects the click behavior. In this paper we follow a data-driven methodology where we analyze approximately 2 million local search queries submitted by users across the US, to visualize and quantify how differently mobile users click across locations. Based on the data analysis, we propose new location-aware features for improving local search click prediction and quantify their performance on real user query traces. Motivated by the results, we implement and evaluate a data-driven technique where local search models at different levels of location granularity (e.g. city, state, and country levels) are combined together at run-time to further improve click prediction accuracy. By applying the location-aware features and the multiple models at different levels of location granularity on real user query streams from a major, commercially available search engine, we achieve anywhere from 5% to 47% higher Precision than a single click prediction model across the US can achieve.