## 4 Trend Analysis Based on Bert_LSA
### The Result of Trend Analysis  
According to the topic number selection method in Section 4.1, the cluster numbers of USA, China, India, Germany, UK, Russian Federation, Italy, and Others were finally set to 6, 5, 6, 5, 7,6, 7, and 4, respectively. Table 1 shows the results of topic modeling using Bert_LSA.  
\begin{table}
\begin{tabular}{l l l l} \hline
**USA** & & **China** & \\ \hline Topic & Ratio (\%) & Topic & Ratio (\%) \\ \hline Water/Polarhub/Enviroatlas & 21\% & Landscape/Livability/Government & 29.4\% \\ Building/Air/BIM & 19.4\% & Extraction/Metadata/Information & 23.5\% \\ Fire/Risk/Precipitation & 17.7\% & Soybean/Crop/Area/Policy & 17.6\% \\ GEE/ Framework / Model & 17.7\% & Geohazards/Landslide/ Anomaly & 14.8\% \\ Stream/Land/Temperature & 12.9\% & Multisource/Search/Metadata & 14.7\% \\ Greenery /Heat & 11.3\% & & \\ \hline
**India** & & **Germany** & \\ \hline Topic & Ratio (\%) & Topic & Ratio (\%) \\ \hline Cloud/computing/Hadoop/Share & 26.3\% & Navigation/Prediction/Street & 25\% \\ Flood/Distribution/Coastline & 21.1\% & Visualization/Database/Datasets & 25\% \\ SDI/WPS/Framework & 21.1\% & Change/Land/Observation & 18.8\% \\ Stormwater/ Groundwater/Conserve & 10.5\% & Stress/Life/Measurement & 18.8\% \\ Land/Investor/Vicinity & 10.5\% & Demand/Heat/Supply & 12.4\% \\ School/Platform/location & 10.5\% & & \\ \hline
**UK** & & **Russian Federation** & \\ \hline Topic & Ratio (\%) & Topic & Ratio (\%) \\ \hline Geohazards/Household/Landslide & 16.7\% & Risk/Environment/Management & 27.3\% \\ Point cloud/Framework & 16.7\% & Network/Generation/Transport & 18.2\% \\ Feature/Attribute/Database & 16.7\% & Customer/Bank/Transaction & 18.2\% \\ Mangrove/Fishing/Intensity & 16.7\% & Monitoring/Change/Climate/Season & 18.2\% \\ BIM/Project/Evaluation & 16.7\% & Client/Cloud/computing/Device & 9.1\% \\ Weather/MCSA (Multi-Channel Sequences & 8.3\% & Image/Anomaly/Validation & 9\% \\ Analysis/Condition & 8.2\% & & \\ \hline
**Italy** & & **Others** & \\ \hline Topic & Ratio (\%) & Topic & Ratio (\%) \\ \hline Geo/Disaster/Cluster & 30\% & Land/Housing/City/Water & 41.2\% \\ Challenge/Spiral/OpenGIS & 20\% & Village/Free/model/System & 23.5\% \\ Crop/Precision/Classification & 10\% & Datasets/Soil/Accuracy & 23.5\% \\ Landslide/Hazard/Flood & 10\% & SDI/Web/Collection & 11.8\% \\ Map/Territory/Accessment & 10\% & & \\ Location/Behavior/Category & 10\% & & \\ GNSS/Radar/Remote/sensing & 10\% & & \\ \hline \end{tabular}
\end{table}
Table 1: The topic analysis results based on Bert_LSA model, and the percentage indicates the proportion of the country in all documents.  
The results of the trend analysis show that the focus of each country's concern is different. USA, for example, focuses on the environment, buildings, and fires. However, China pays attention to livability, information extraction, and crops, while words like government and policy also appear. India focuses on information technology, such as cloud computing, SDI and WPS, etc., as well as focuses on disaster events such as floods. Italy and others also pay attention to disaster-related content. Germany, UK, and the Russian Federation all focus on content related to climate change. In general, countries pay more attention to disaster events (e.g., fires and floods), and related information technologies, such as cloud computing, Hadoop and GEE, etc., have also received higher attention. This is also a good indication that our proposed method can successfully identify the current technologies and application trends related to the use of geospatial data, which can quickly provide research hotspots for relevant researchers, especially those who are not specialized in GIS. In this way, it considerably saves the time needed to read a large amount of literature, which is of practical significance.