This research is motivated by the need for 3D GIS data models that allow for 3D spatial query, analysis and visualization of the subunits and internal network structure of ‘micro‐spatial environments’ (the 3D spatial structure within buildings). It explores a new way of representing the topological relationships among 3D geographical features such as buildings and their internal partitions or subunits. The 3D topological data model is called the combinatorial data model (CDM). It is a logical data model that simplifies and abstracts the complex topological relationships among 3D features through a hierarchical network structure called the node‐relation structure (NRS). This logical network structure is abstracted by using the property of Poincaré duality. It is modelled and presented in the paper using graph‐theoretic formalisms. The model was implemented with real data for evaluating its effectiveness for performing 3D spatial queries and visualization.

Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.