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:空间由几何测量和协调系统界定。社交媒体作为地点和空间之间的联系。在本研究中,我们使用社交媒体数据(Twitter,Weibo)在两个不同的领域(北京、中国和圣地亚哥,美国)建立一个动态的本体模型。通过跟踪高度动态的非行政性地点的季节性变化,观察到了季节性变化模式,说明了人类活动变化和时间和空间间对话引起的本体变化。我们还通过审查点对地相互信息分数和字云之间的传统直观性定义,调查每个地点的语义含义。这一研究的主要贡献是分析动态非行政性地点的季节性变化,而不是对地平面的地理学研究。这种研究的主要贡献是分析、对地表和地缘学的研究。</s>