## 1 Introduction  
Geographical data describes a location and its spatial characteristics attributes. With the rapid development of information technology, geospatial data has become an indispensable data resource for research and application in many fields, such as natural resource management, disaster emergency management, climate change and precision agriculture, etc. [1]. The technologies and applications related to geospatial data are also constantly advancing and upgrading, making new ways of thinking possible, so identifying technologies and applications is helpful to foster and fund further innovation. Through topic analysis, we can identify new research hotspots, acquire knowledge transfer processes [2], and quickly analyze the entire development process of research areas, thus benefiting researchers who are interested in a topic. In addition, it can also provide signals for paradigm shifts in discipline development [3]. For individuals, the results of topic analysis provide an overview of the evolution of the research field and are helpful to us in grasping research trends, keeping up-to-date with the latest research trends in the field, and seeking scientific collaborators [4]. The extensive scientific literature provides researchers with a wealth of information, which is also an important data resource for analyzing the development trends. However, the time and cost for understanding and analyzing the complex dynamics of current technical approaches related to geospatial data are increasing [5]. Therefore, researchers try to save time and reduce costs by seeking automated analysis methods, which allow them to quickly find the most important information, in order to make critical decisions without consulting voluminous literature [6]. At present, there are two main methods for identifying topics in texts. One is a qualitative appraisal method used by the academia, which is known as expert overview. The other is a scientometrics-based approach. Expert overview is a comprehensive and effective method for topic identification, but it is highly dependent on expert opinion, which is time- and energy-consuming. In addition, expert overview is becoming an increasingly inefficient means, due to the explosive growth of the scientific literature. In comparison, the bibliometric approach uses related papers for statistical frequency analysis, and simply captures information such as citation statistics to identify topics. An article with a high citation count is considered as a high-value one [7]. The structural and geospatial developments of industrial symbiosis as subfields of industrial ecology have been explored by using bibliometrics [8]. A statistical approach to bibliometric data from U.S. institutions has also been used to identify institutional hotspots on a map where many high-impact papers are published. The bibliometrics-based approach plays a role in identifying the development of trends, but it lacks consideration of the content of the literature texts themselves. Previous studies are mainly based on traditional methods, which merely review relevant literature or conduct simple frequency analysis without providing insights beyond revealing information about the contents of literature texts [9]. Therefore, it is urgent to conduct comprehensive and in-depth trend analysis of literature texts. Recently, a popular method involves text analysis techniques to identify the main viewpoints and trends of the research [10], for example, by using textual data such as user comments, papers, and patents to analyze keywords or social networks [11; 12; 13; 14; 15; 16]. In particular, topic modeling has recently attracted the attention of trend analysis researchers, since the main purpose of trend analysis based on textual data is to detect the upward and downward trends in the frequency of each topic in the target document [17]. Topic modeling originates from early latent semantic analysis (LSA), which aims to discover meaningful semantic structures in the corpus [18], with a focus on keyword extraction.