  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 human-computer
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.
