The No.4 tailings pond of the Dexing Copper Mine is the second largest in Asia. The tailing pond is a dangerous source of man-made debris flow with high potential energy. In view of the lack of effective and low-cost global safety monitoring means in this region, in this paper, the time-series InSAR technology is innovatively introduced to monitor the deformation of tailings dam and significant key findings are obtained. First, the surface deformation information of the tailings pond and its surrounding areas was extracted by using SBAS-InSAR technology and Sentinel-1A data. Second, the cause of deformation is explored by analyzing the deformation rate, deformation accumulation, and three typical deformation rate profiles of the representative observation points on the dam body. Finally, the power function model is used to predict the typical deformation observation points. The results of this paper indicated that: (1) the surface deformation of the tailings dam can be categorized into two directions: the upper portion of the dam moving away from the satellite along the Line of Sight (LOS) at a rate of −40 mm/yr, whereas the bottom portion approaching the satellite along the LOS at a rate of 8 mm/yr; (2) the deformation of the dam body is mainly affected by the inventory deposits and the construction materials of the dam body; (3) according to the current trend, deformation of two typical observation points in the LOS direction will reach the cumulative deformation of 80 mm and −360 mm respectively. The research results can provide data support for safety management of No.4 tailings dam in the Dexing Copper Mine, and provide a method reference for monitoring other similar tailings dams.

Traditional geographic information system models for map representation use superposition of layers to model physical reality, neglecting the integrity of the environment and limiting the ability to express interactions between features in complex phenomenon. This results in limitations regarding dynamic simulation and geographic causality reasoning. In this paper, we extend the framework of the geographic scene by formalizing the relationship between geographic processes and events to construct a dynamic data model: the process-event-centred dynamic data model. The key element of this data model is relationships between processes, events, and states of the natural or man-made phenomenon of interest. The identified relationships can be translated into a network of hierarchical, developmental, and causal graphs and realized in the Neo4j graph database. The implementation in the graph database supports spatio-temporal reasoning in geographic scenes and achieves an organizational framework for simulating spatio-temporal dynamics and complex calculations. The example of a 2019 mega-typhoon process is used to demonstrate the introduced process-event-centred model and its implementation in the graph database. A series of queries to the graph database show the capabilities of the data model for spatial reasoning and dynamic modeling.

A novel approach is introduced for the detection of the location and direction of traffic congestion using GPS data from taxis. This approach uses a dynamic model that conceptualizes events, processes, and states. The states are the locations of the taxis, the processes are the motion of taxis, and the events are congestion. The model is implemented as a graph database, which represents the relationships between states, events, processes, and things (such as points of interest and road grid). Algorithms for constructing and updating the relationships and taxi behaviors dynamic retrieval method in Neo4j are presented and are used to demonstrate the capabilities in dynamic expression and reasoning. An implementation of Shanghai in 2015 finally demonstrated the ability of congestion direction detection and the cause searching of traffic congestion.