The lack of certain types of geographic data prevents the development of location-aware technologies in a number of important domains. One such type of "unmapped" geographic data is space usage rules (SURs), which are defined as geographically-bound activity restrictions (e.g. "no dogs", "no smoking", "no fishing", "no skateboarding"). Researchers in the area of humancomputer interaction have recently begun to develop techniques for the automated mapping of SURs with the aim of supporting activity planning systems (e.g. one-touch "Can I Smoke Here?" apps, SUR-aware vacation planning tools). In this paper, we present a novel SUR mapping technique - SPtP - that outperforms state-of-the-art approaches by 30% for one of the most important components of the SUR mapping pipeline: associating a point observation of a SUR (e.g. a 'no smoking' sign) with the corresponding polygon in which the SUR applies (e.g. the nearby park or the entire campus on which the sign is located). This paper also contributes a series of new SUR benchmark datasets to help further research in this area.