  Place holds human thoughts and experiences. Space is defined with geometric
measurement and coordinate systems. Social media served as the connection
between place and space. In this study, we use social media data (Twitter,
Weibo) to build a dynamic ontological model in two separate areas: Beijing,
China and San Diego, the U.S.A. Three spatial analytics methods are utilized to
generate the place name ontology: 1) Kernel Density Estimation (KDE); 2)
Dynamic Method Density-based spatial clustering of applications with noise
(DBSCAN); 3) hierarchal clustering. We identified feature types of place name
ontologies from geotagged social media data and classified them by comparing
their default search radius of KDE of geo-tagged points. By tracing the
seasonal changes of highly dynamic non-administrative places, seasonal
variation patterns were observed, which illustrates the dynamic changes in
place ontology caused by the change in human activities and conversation over
time and space. We also investigate the semantic meaning of each place name by
examining Pointwise Mutual Information (PMI) scores and word clouds. The major
contribution of this research is to link and analyze the associations between
place, space, and their attributes in the field of geography. Researchers can
use crowd-sourced data to study the ontology of places rather than relying on
traditional gazetteers. The dynamic ontology in this research can provide
bright insight into urban planning and re-zoning and other related industries.
