The defining characteristic of wireless and mobile networking is user mobility, and related to it is the ability for the network to capture information on where users are located and how users change location over time. Information about location is becoming critical, and therefore valuable, for an increasingly larger number of location-based or location-aware services. One key open question, however, is how valuable exactly this information is. Our goal in this paper is to develop an analytic framework, namely models and the techniques to solve them, to help quantify the economics of location information. Our aim is to derive models which can be used as decision making tools for entities interested in or involved in the location data economics chain, such as mobile operators or providers of location aware services (mobile advertising, etc). We consider in particular the fundamental problem of quantifying the value of different granularities of location information, for example how much more valuable is it to know the GPS location of a mobile user compared to only knowing the access point, or the cell tower, that the user is associated with. We illustrate our approach by considering what is arguably the quintessential location-based service, namely proximity-based advertising. We make three main contributions. First, we develop several novel models, based on stochastic geometry, which capture the location-based economic activity of mobile users with diverse sets of preferences or interests. Second, we derive closed-form analytic solutions for the economic value generated by those users. Third, we augment the models to consider uncertainty about the users' location, and derive expressions for the economic value generated with different granularities of location information. To our knowledge, this paper is the first one to present and analyze this class of economic models.