Given the growth in geographical data production, and the various mandates to make sharing of data a priority, there is a pressing need to facilitate the appropriate uptake and reuse of geographical data. However, describing the meaning and quality of data and thus finding data to fit a specific need remain as open problems, despite much research on these themes over many years. We have strong metadata standards for describing facts about data, and ontologies to describe semantic relationships among data, but these do not yet provide a viable basis on which to describe and share data reliably. We contend that one reason for this is the highly contextual and situated nature of geographic data, something that current models do not capture well and yet they could. We show in this paper that a reconceptualization of geographical information in terms of Peirce's Pragmatics (specifically firstness, secondness and thirdness) can provide the necessary modeling power for representing situations of data use and data production, and for recognizing that we do not all see and understand in the same way. This in turn provides additional dimensions by which intentions and purpose can be brought into the representation of geographical data. Doing so does not solve all problems related to sharing meaning, but it gives us more to work with. Practically speaking, enlarging the focus from data model descriptions to descriptions of the pragmatics of the data - community, task, and domain semantics - allows us to describe the how, who, and why of data. These pragmatics offer a mechanism to differentiate between the perceived meanings of data as seen by different users, specifically in our examples herein between producers and consumers. Formally, we propose a generative graphical model for geographic data production through pragmatic description spaces and a pragmatic data description relation. As a simple demonstration of viability, we also show how this model can be used to learn knowledge about the community, the tasks undertaken, and even domain categories, from text descriptions of data and use-cases that are currently available. We show that the knowledge we gain can be used to improve our ability to find fit-for-purpose data.