Natural disasters pose serious threats to human survival. With global warming, disaster chains related to extreme weather are becoming more common, making it increasingly urgent to understand the relationships between different types of natural disasters. However, there remains a lack of research on the frequent spatial-temporal intervals between different disaster events. In this study, we utilize textual records of natural disaster events to mine frequent spatial-temporal patterns of disasters in China. We first transform the discrete spatial-temporal disaster events into a graph structure. Due to the limit of computing power, we reduce the number of edges in the graph based on domain expertise. We then apply the GraMi frequent subgraph mining algorithm to the spatial-temporal disaster event graph, and the results reveal frequent spatial-temporal intervals between disasters and reflect the spatial-temporal changing pattern of disaster interactions. For example, the pattern of sandstorms happening after gales is mainly concentrated within 50 km and rarely happens at farther spatial distances, and the most common temporal interval is 1 day. The statistical results of this study provide data support for further understanding disaster association patterns and offer decision-making references for disaster prevention efforts.

Historical figures are crucial for understanding historical processes and social changes. However, existing databases of historical figures primarily focused on ancient Chinese individuals and are limited by the simplistic organization of textual information, lacking structured processing. Therefore, this study proposes an automatic method for constructing a spatio-temporal database of modern Chinese figures. The character state transition matrix reveals the spatio-temporal evolution of historical figures, while the random walk algorithm identifies their primary migration patterns. Using historical figures from Fujian Province (1840–2009) as a case study, the results demonstrate that this method effectively constructs the spatio-temporal chain of figures, encompassing time, space, and events. The character state transition matrix indicates a fluctuating trend of state change from 1840 to 2009, initially increasing and then decreasing. By applying keyword extraction and the random walk method, this study finds that the state transitions and their causes align with the historical trends. The four-dimensional analytical framework of “character-time-space-event” established in this study holds significant value for the field of digital humanities.