The importance of place-based or platial-based research in the geosciences has been highlighted in recent publications by researchers Goodchild, Elwood, and Sui. They suggest that deep and direct location information is more frequently associated with place and placenames. The traditional spatial context in geosciences is geometry-based, But the human brain links the orientation and association of real-world objects or features by place names rather than by coordinates. Explaining a location of an incident in textual format typically requires prepositions to emphasize the proximity of relevant features. Additionally, global mapping systems do not convey the real naming of real-world features on a local level. This raises the importance of creating local gazetteers containing various names of each feature entry including buildings, landmarks, and road names. In addition, a place might contain smaller places within its boundaries. In the platial perspective, gazetteers and hierarchies of places can be compared to GIS databases and layers in the spatial approach, respectively. This general approach allows for the connection between human-centered place-based referencing, and metric georeferencing systems used in GIS and mapping. In this research, we are instantiating a reference library of geo-parsed footprints based on place names and prepositions in text-based crowdsourced data. A localized gazetteer of place names in George Mason University - main campus, Fairfax VA, USA was created as the functional center of the system. The geo-parsed footprints are bounded differently based on place name types and whether or they are preceded with a preposition. The prepositions are being classified in accordance to their relative distances. Currently there are more than ten footprint definitions based on the geo-parsing methodology. The library of the footprint definitions is expected to grow significantly and we hope to make it a reference for other scholars for further research improvements. The web/mobile platform system, built for this research, integrates the following open source technologies: MapBox, TURF.js, JQuery, and Bootstrap.