Data and Resources for Combining Point of Interest Semantics, Locations, and Road Networks

The advancements in Location Based Services (LBS) and Location Based Social Networks (LBSN) have spurred multiple research efforts in query processing as well as recommendation systems that enable planning trips based on combining location and semantic properties of Points of Interest (POI). However, often times such trips need to involve the reality of existing road networks, for the purpose of obeying constraints such as distance or travel-time. Although there are many publicly available datasets (e.g., Gowalla) that include check-in data at POIs with location, they are often not integrated with existing roads-based data (e.g., Open Street Maps (OSM)) causing researchers to spend extra time and labour to experimentally evaluate their findings. In this paper, we present: (1) methodologies for extracting information regarding POIs from publicly available datasets based on users posting; (2) extracting concise semantic categories for each POI; (3) integrating their location and semantic categories with an existing road network. In addition to the methodologies, we also provide two datasets (based on POIs and road networks in Chicago and New York City) constructed using our methodologies that researchers can readily use for their semantic-aware POIs with location and trip based query processing tasks as well as deep learning tasks.