# Machine Learning Based Representative Spatio-Temporal Event Documents Classification
## 2 Related Work  
### Spatio-Temporal Event Detection  
A spatio-temporal event is defined as an event that occurs at a specific time and location of interest to stakeholders [6; 7]. Natural disasters, crimes, and business events that occurred at a specific time and location are examples of spatio-temporal events. In particular, since events such as natural disasters or environmental pollution consume a lot of social costs, it is important to detect them in real time in order to respond to them [8]. For this reason, studies are being conducted to detect events in text news that is uploaded online every moment. Spatio-temporal events are generally detected from text data. Text data can contain information about time and place, and this information can be used to detect spatio-temporal events. For example, news articles or social media posts generally contain information about time and place. To detect events from such data, the time and place are extracted and used to detect events. To detect spatio-temporal events, natural language processing techniques are used to process text data. Sentence classification, named entity recognition, and semantic analysis can be utilized for this purpose. Moreover, there is research on developing techniques to detect spatio-temporal events from image or video data. For instance, in autonomous driving, cameras and radar are used to detect road situations, and techniques to detect spatio-temporal events based on these data are under study. However, generally, spatio-temporal events are detected from text data, and natural language processing techniques are used to process text data. [PERSON] et al. [9] presented a new document expression method based on word embedding that improves efficiency and accuracy by reducing dimensions and mitigating sparse semantics compared to TF-IDF to efficiently monitor events presented in online news. [PERSON] et al. [10] trained a classification model to identify event-related text in social media, and proposed a clustering-based approach to detect and track events. The authors jointly learned the similarity metric and low-dimensional representation of events using an attention-based neural network, saved and updated the event representation, and demonstrated its effectiveness in large data sets. [PERSON] et al. [11] proposed a model to extract and track events from social data streams in real time. To solve the problem that social media datasets are difficult to accurately extract events from due to noise and short text length, [PERSON] et al. [12] proposed Spatio-Temporal Event Detection (STED), which can detect events using other information such as news articles that use a more accurate and realistic vocabulary. The model is used to track events, related topics, time of occurrence, and geospatial distribution across various data sources such as news and Twitter. By modeling news and Twitter together, this model helps filter out noise from Twitter data, and the geographic coordinates and timestamps used in tweets are useful for understanding the spatial and temporal distribution of events. [PERSON] et al. [13] proposed a novel approach to event localization and ranking that can be applied to Twitter data streams. The proposed approach models the language usage of Twitter by hourly city to create a model that detects the magnitude of unexpected change in language usage. This approach uses a space-time grid structure and methods that traverse time, day, state, city, region, and country to detect anomalies in the language spoken in millions of tweets. Recently, studies to detect spatio-temporal events based on deep learning are also being conducted. [PERSON] et al. [14] proposes a hybrid learning model called Deep-Eware for spatio-temporal social event detection.