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.

翻译：场所承载着人类的思想与体验。空间则通过几何测量与坐标系进行定义。社交媒体充当了连接场所与空间的桥梁。本研究利用社交媒体数据（推特、微博）在中国北京与美国圣地亚哥两个区域构建动态本体模型。采用三种空间分析方法生成场所名称本体：1）核密度估计（KDE）；2）基于密度的动态噪声应用空间聚类算法（DBSCAN）；3）层次聚类。我们从含地理标签的社交媒体数据中识别出场所名称本体的特征类型，并通过比较地理标签点的默认KDE搜索半径对其进行分类。通过追踪高度动态的非行政区域的季节性变化，观察到季节性变异模式，这揭示了人类活动与对话随时空变化引起的场所本体动态演变。本研究还通过计算点互信息（PMI）分数与词云分析每个场所名称的语义内涵。该研究的主要贡献在于连接并分析地理学领域中场所、空间及其属性之间的关联性。研究者可借助众包数据研究场所本体，而非依赖传统地名录。本研究的动态本体可为城市规划、区域重划及相关产业提供重要启示。