Efficient Topological Data Models for Spatial Queries in 3d Gis

Currently, demands on 3D GIS increase, so spatial data analysis in 3D space also is required. Especially as large-scale and complex indoor space has been developed, it is more important to analyze human behaviors in complicated 3D indoor space to find accident spots or calculate evacuate routes. In order to do that, it is essential to represent topological relationships among the 3D entities in spatial data modeling. So far, topological relationships have been expressed by feature-based data model based on B-rep. However, this data model has some limitations in maintaining topological relationships: complex geometric computations leading inefficiency in maintaining topological consistency, unclear connectivity relationship, and big data volume. Then network-based topological data model based on graph theory was raised to overcome these limitations. In this data model, because topological relationships in complex 3D space is described as a simple network structure using nodes and edges, computational complexity to define adjacency and connectivity relationships is expected to be reduced. For this reason, the network-based topological data model has been believed in more efficient than feature-based data models for 3D spatial analysis with no practical tests yet. In this paper, to verify this general assumption, we perform comparative analysis about efficiencies on spatial queries between two data models through a practical implementation. * Corresponding author.