Variable-scale Geo-information

Abstract

The use of geo-information is changing by the advent of new mobile devices, such as tablet-pc's that harness a lot of computing power. This type of information is more and more applied in mainstream digital consumer products, in a net-centric environment (i.e. dissemination takes place via the Internet) and the advances in mobile hardware also have changed the way people can interact with the geographic information at hand, compared to `old-fashioned' paper maps. However, current state-of-the-art solutions for storing, maintaining and disseminating digital maps still mimic the analogue map-series concept in the sense that for every map scale in the serie (e.g. 1:25K, 1:50K, 1:250K) a different digital copy with independent data is kept and maintained at the producers site. The challenge of this work was to get to a representation of the real world with gradually changing level of detail, instead of representations with discrete levels of detail (organised in multiple, independent layers, each layer representing only one resolution level). Vario-scale data structures try to avoid this redundancy of the geometric description of the map by storing references to composing map elements of the highest level of detail for any other element of a lower level of detail. An example of variable-scale data structures are the tGAP data structures. In addition to the geometry and references, an importance value for every object is stored and based on this importance value different representations (where the level of detail is gradually changing) can be derived on the fly from these structures according to the needed level of detail. The overall aim of this research has been to investigate variable-scale geo-information, by defining theoretical underpinnings of vario-scale geo-information and improving the initial tGAP structures. The objective we had with this research is expressed in the main question, which was formulated as: How can we realise improved vario-scale geo-information having minimal redundancy? The overall outline of the research design draws heavily upon the paradigm of design research. In an iterative fashion we performed theory building, prototype developments and experiments with real world data sets. Over the course of this research, we have made the following main contributions to the design of a vario-scale geo-information environment. We have: - formalised the concept of variable-scale data as a conceptual 3D model (the space-scale cube, SSC), where 2D space and 1D scale is integrated; - shown for the tGAP data structures how minimal data redundancy can be obtained when applying a merge operation, how to perform a parallel simplification of lines, without introducing unwanted topological errors and proposed a split operation, for which it was analysed what the impacts are on the designed data structures; - shown how to derive a 2D map from the structures with a particular number of objects, as well as investigated progressive data streaming; - proposed an improved way of generating data so that even smoother graphic transitions can be derived for visualisation. The main conclusions that can be drawn from these contributions: - With the concept of the proposed space-scale cube (SSC) we have formalised what vario-scale vector data entails. In a sense, the improved design of the tGAP data structures can be seen as a lossless encoding of the data that is captured for a ssc; - To make vario-scale geo-information operational, we need specific generalisation operations. These vario-scale generalisation operations should be designed carefully to be able to give guarantees on the amount of data to be stored and output topologically consistent vario-scale data; - Although the improved tGAP structures are capable of providing a smooth zooming end user experience, we still store and visualise discrete steps -- albeit smaller and more local than is common with current state of the art solutions. Therefore we proposed how smoothness of the vario-scale data can be improved (where the smooth SSC taking a small step in scale leads to a small change in the 2D derived map). A novelty of this approach is that, as it is one integrated space-scale partition, using a non-horizontal slice plane leads to a valid, mixed-scale planar partition: this is useful for use in 3D computer graphics (far away from an observer having less detail than close by). Although this research has generated some knowledge for a vario-scale environment, it also paves the way for future research. The main recommendations for future work are: - Investigate how to deal with very large data sets that do not fit in main memory (during the generalisation process or during visualisation) deserves attention; - The smooth encoding of the SSC has the same building challenge as the classic tGAP with respect to applying the right sequence of generalisation operators (remove or merge, collapse or split, simplify) to obtain maps with sufficient cartographic quality; - Another point for further research is the smooth interactions: it is of importance to know how users perceive these. The same holds for mixed-scale slices (in a 3D world); - Focus of this research has been mostly on obtaining and viewing vario-scale data. Performing analysis with vario-scale data is another interesting aspect that deserves attention, e.g. vario-scale data could be of help in data integration; - Investigate how to make the structures dynamic: currently the tGAP structure (including the new smooth variant) is a static structure and has to be re-built if the source data changes. Being able to perform incremental updates (partially re-generalising data for a new situation) would be beneficial if the data volume increases. Related to this is higher dimensionality of smooth, vario-scale data (e.g. 3D data) leading to integrated 5D data management (integrating dimensions of space (2D or 3D), time (updates, 1D) and scale (level of detail, 1D).