# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 3 Research on Data-Driven Predicting of Social Security Events
### Predictive Factors
#### 3.1.2 Joint Factors of Time and Location  
When conducting the task of predicting social security events, focusing on both the time and location where they occur is essential. Such methods focus on a specific geographical dataset to determine whether social security events will occur within a certain timeframe in that region or to provide the probability of such events occurring. This paper categorizes datasets into three types based on their geographical granularity: a. those focusing on countries; b. those focusing on cities or regions; and c. those focusing on a specific location.  
Research works that focus on countries typically involve selecting a national-level dataset to predict social security events at the country level, determining whether they will occur within a specific time range or providing the probability of their occurrence. In 2015, [PERSON] et al. [15] used logistic regression to predict social security events in three Latin American countries, Brazil, Mexico, and Venezuela, providing the probability of protests occurring in Brazil every day in June. The team, led by Professor [PERSON] from Stevens Institute of Technology, also predicted social security events using a national-level dataset, applying graph neural networks to predict events in datasets from five countries,as mentioned in reference [7], and determining whether social security events would occur within a specific time and country range. Additionally, [PERSON] et al. [20] proposed a new SVM classifier to predict internal conflict events in four countries including Afghanistan, providing the probability of internal conflict events occurring in each country.  
Research works that focus on cities or regions are the most common approach, and these typically utilize city-level or regional-level datasets to predict social security events at the city or regional level, determining whether such events will occur within a specified timeframe or providing the probability of their occurrence. [PERSON] et al. [14] used logistic regression to predict social security events in specific cities, providing the time, city, and probability of the occurrence of strike protests for each country in their dataset. [PERSON] et al. [21] also focused on datasets related to unrest in Latin America, proposing a hierarchical incomplete multi-source feature learning model to predict whether internal conflict events would occur in specific cities on a particular day. Furthermore, [PERSON] et al. [5, 22, 23] predicted disease outbreaks in the United States on a state-by-state basis. [PERSON] et al. [24] predicted protest events in Cairo.  
Research works that focus on a specific location typically target a specific place (such as Victoria Park in Hong Kong) and use acquired data to predict social security events at that designated location. Current known prediction methods have not achieved this level of spatial granularity yet. However, they can also capture relevant information about specific locations through media information extraction methods [17, 19] to predict organized and planned social security events.