Automatic map generalization requires the use of computationally intensive processes often unable to deal with large datasets. Distributing the generalization process is the only way to make them scalable and usable in practice. But map generalization is a highly contextual process, and the surroundings of a generalized map feature needs to be known to generalize the feature, which is a problem as distribution might partition the dataset and parallelize the processing of each part. This paper proposes experiments to evaluate the past propositions to distribute map generalization, and to identify the main remaining issues. The past propositions to distribute map generalization are first discussed, and then the experiment hypotheses and apparatus are described. The experiments confirmed that regular partitioning was the quickest strategy, but less effective when taking context into account. The geographical partitioning, though less effective for now, is quite promising regarding the quality of the results as it better integrates the geographical context.

/pdf/experiments-to-distribute-and-parallelize-map-generalization-4qshl5ljdp.pdf

Experiments to Distribute and Parallelize Map Generalization Processes

Visvalingam's algorithm was designed for caricatural line generalization. A distinction must be made between the algorithm and its operational definition, which includes the metric used to drive it. When the algorithm was first introduced, it was demonstrated using the concept of the effective area of triangles. It was noted that alternative metrics could be used and that the metrics could be weighted, for example to take account of shape.Ordnance Survey (Great Britain) and others are using Visvalingam's algorithm for generalizing coastlines and other natural features, with complex parameter-driven functions to weight the original metric. This paper shows how free software and data were used to scrutinize the implications of one of Matthew Bloch's simple and transparent weighting functions. The results look promising, when compared with manually produced mid and small-scale maps; and encourage further research focussed on weighting functions and related topics, such as self-intersection of lines and model...

Implications of Weighting Metrics for Line Generalization with Visvalingam's Algorithm

This paper reports on a new Area Preserving Segment Collapse (APSC) algorithm for simplifying polygonal boundaries while preserving the polygonal area at simplified target scales and minimizing are...

Simplification of polylines by segment collapse: minimizing areal displacement while preserving area

ABSTRACT The present paper presents techniques for polyline simplification based on an artificial neural network within the constraints of generalization knowledge. The proposed method measures polyline shape characteristics that influence polyline simplification using abstracted descriptors and then introduces these descriptors into the artificial neural network as input properties. In total, 18 descriptors categorized into three types are presented in detail. In a second approach, map simplification principles are abstracted as controllers, imposed after the output layer of the trained artificial neural network to make the polyline simplification comply with these principles. This study worked with three controllers – a basic controller and two knowledge-based controllers. These descriptors and controllers abstracted from generalization knowledge were tested in experiments to determine their efficacy in polyline simplification based on the artificial neural network. The experimental results show that the utilization of abstracted descriptors and controllers can constrain the artificial neural network-based polyline simplification according to polyline shape characteristics and simplification principles.

Polyline simplification based on the artificial neural network with constraints of generalization knowledge

Vector data compression can significantly improve efficiency of geospatial data management, visualization and data transmission over internet. Existing compression methods are either based on information theory for lossless compression mainly or based on map generalization methods for lossy compression. Coordinate values of vector spatial data are mostly represented using floating-point type in which data redundancy is small and compression ratio using lossy algorithms is generally better than that of lossless compression algorithms. The purpose of paper is to implement a new algorithm for efficient compression of vector data. The algorithm, named space division based compression (SDC), employs the basic idea of linear Morton and Geohash encoding to convert floating-point type values to strings of binary chain with flexible accuracy level. Morton encoding performs multiresolution regular spatial division to geographic space. Each level of regular grid splits space horizontally and vertically. Row and column numbers in binary forms are bit interleaved to generate one integer representing the location of each grid cell. The integer values of adjacent grid cells are proximal to each other on one dimension. The algorithm can set the number of divisions according to accuracy requirements. Higher accuracy can be achieved with more levels of divisions. In this way, multiresolution vector data compression can be achieved accordingly. The compression efficiency is further improved by grid filtering and binary offset for linear and point geometries. The vector spatial data compression takes visual lossless distance on screen display as accuracy requirement. Experiments and comparisons with available algorithms show that this algorithm produces a higher data rate saving and is more adaptable to different application scenarios.

https://www.mdpi.com/2220-9964/9/12/721/pdf

J. C. Whelan

Papers

Implications of Weighting Metrics for Line Generalization with Visvalingam's Algorithm