Integrating updates from timely sources such as volunteered geographic information (VGI) into the spatial data maintained at official agencies is becoming a more demanding requirement but presents many challenges. This paper proposes an approach to addressing the technical challenge of propagating updates from timely sources (e.g. OpenStreetMap) to spatial data maintained at separate map scales. The main idea is to establish a multiple representation database (MRDB) for datasets at different scales and time to facilitate incremental update, where linkages between corresponding objects at different datasets are made explicit. First, two ways in which the timely sources can be integrated into official data for incremental update are discussed. To derive the linkages between different datasets, a data matching procedure based on computer vision is presented and fine-tuned to match data in different scale ranges. Furthermore, the generalization history used to produce smaller scale data from the larger ones in official data is inferred based on the linkages, and is then used to guide the update propagation. Finally, a framework for incremental generalization in MRDBs is proposed, where crucial issues like strategies for update propagation, cartographic generalization, and the so-called ‘chain reaction’ are addressed. The framework is implemented as a fully automated process where operators like simplification, enlargement, compression, displacement and typification are incorporated into the incremental update process. By testing the framework against real world data sets (i.e. OpenStreetMap and official data at 1:10k, 1:50k and 1:100k), we show that the updates are integrated consistently into existing data in terms of spatial relations and cartographic quality. Our work suggests that making use of timely sources by official mapping agencies and companies in a continuous or event-driven data update is technically feasible, with further improvement and extensions discussed.@en

A vector tessellation method is proposed for grid pattern recognition in street networks. This study regards a street network as an independent subspace embedded in the 2D space, and subdivides street segments into linear elements with equal lengths. The characteristics of grid patterns are extracted, including directional, geometrical and topological features. To map the object space to the feature space and to build a vector field, the linear element is described as a feature vector and the eigenvalues are calculated with the neighboring elements. A grid pattern classification is realized based on a support vector machine (SVM), and the classification result is optimized based on Gestalt principles. The method was applied to the street network of Shenzhen. The experimental results show that the method effectively mines grid pattern in street networks.

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The representation of vector data at variable scales has been widely applied in geographic information systems and map-based services. When the scale changes across a wide range, a complex generalization that involves multiple operations is required to transform the data. To present such complex generalization, we proposed a matrix model to combine different generalization operations into an integration. This study was carried on a set of river network data, where two operations, i.e., network pruning accompanied with river simplification, were hierarchically constructed as the rows and columns of a matrix. The correspondence between generalization operations and scale, and the scale linkage of multiple operations were also explicitly defined. In addition, we developed a vario-scale data structure to store the generalized river network data based on the proposed matrix. The matrix model was validated and assessed by a comparison with traditional methods that conduct generalization operations in sequence. It was shown that the matrix model enabled complex generalization with good generalization quality. Taking advantage of the corresponding vario-scale data structure, the river network data could be obtained at any arbitrary scale, and the vario-scale representation was achieved across a wide scale range.

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