Place holds human thoughts and experiences. Space is defined with geometric measurement and coordinate systems. Social media data can analyze the connection between place and space. This research uses two case studies to illustrate the building of dynamic ontological models for places. The first case collected Twitter data with nine key place names in San Diego. The second case study collected Sina Weibo data with eight key place names in Chinese in Beijing. Only geotagged social media data from the two social media platforms were collected through their APIs. This research utilized three spatial analysis methods in order to analyze the dynamic ontological representations of place names: 1). Kernel Density Estimation (KDE); 2) Density-based spatial clustering of applications with noise (DBSCAN); and 3) hierarchal clustering. The normalized KDE approach is the best algorithm to generate appropriate boundaries or footprints for place names. DBSCAN and hierarchal clustering cannot deliver desirable ontological boundary outcomes. This research classified polyline and non-polyline type of place name ontologies by comparing their default search radius of KDE of geotagged points. With the dynamic characteristics of social media, this research provided dynamic spatiotemporal analysis. By viewing the seasonal changes of highly dynamic nonadministrative places, it reflects the seasonal change of human activities. Besides spatial analysis, this research also investigates the semantic meaning associated with each place name. By looking at Pointwise Mutual Information (PMI) scores and wordclouds, PMI can be used to measure the most significant keywords from the social media message associated with place names. There are several constraints and challenges in this research. For example, GIS Software may not handle large numbers of social media data points. Also, the data may include some noise or have uneven amount of volume. The major contribution of this research is to link and analyze the inter-relationships between place, space, and their attributes in the field of geography. Researchers can use the “bottom-up” approach and crowd-sourced data (social media) to study the ontology of places v rather than relying on traditional gazetteers. The dynamic ontology has many actual applications in industry as well such as urban planning and re-zoning.