LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization

Visualization of large vector line data is a core task in geographic and cartographic systems. Vector maps are often displayed at different cartographic generalization levels, traditionally by using several discrete levels-of-detail (LODs). This limits the generalization levels to a fixed and predefined set of LODs, and generally does not support smooth LOD transitions. However, fast GPUs and novel line rendering techniques can be exploited to integrate dynamic vector map LOD management into GPU-based algorithms for locally-adaptive line simplification and real-time rendering. We propose a new technique that interactively visualizes large line vector datasets at variable LODs. It is based on the Douglas-Peucker line simplification principle, generating an exhaustive set of line segments whose specific subsets represent the lines at any variable LOD. At run time, an appropriate and view-dependent error metric supports screen-space adaptive LOD levels and the display of the correct subset of line segments accordingly. Our implementation shows that we can simplify and display large line datasets interactively. We can successfully apply line style patterns, dynamic LOD selection lenses, and anti-aliasing techniques to our line rendering

Methodologic issues and approaches to spatial epidemiology

Spatial epidemiology is increasingly being used to assess health risks associated with environmental hazards. Risk patterns tend to have both a temporal and a spatial component; thus, spatial epidemiology must combine methods from epidemiology, statistics, and geographic information science. Recent statistical advances in spatial epidemiology include the use of smoothing in risk maps to create an interpretable risk surface, the extension of spatial models to incorporate the time dimension, and the combination of individual- and area-level information. Advances in geographic information systems and the growing availability of modeling packages have led to an improvement in exposure assessment. Techniques drawn from geographic information science are being developed to enable the visualization of uncertainty and ensure more meaningful inferences are made from data. When public health concerns related to the environment arise, it is essential to address such anxieties appropriately and in a timely manner. Tools designed to facilitate the investigation process are being developed, although the availability of complete and clean health data, and appropriate exposure data often remain limiting factors

Web service approaches for providing enriched data structures to generalisation operators

Web service technologies can be used to establish an interoperable framework between different generalisation systems. In a previous article three categories of generalisation web services were identified, including support services, operator services and processing services. This paper focuses on the category of support services. In a service-based generalisation system, the purpose of support services is to assist the generalisation process by providing auxiliary measures, procedures and data structures that allow the representation of structural cartographic knowledge. The structural knowledge of the spatial and semantic context and the modelling of structural and spatial relationships is critical for the understanding of the role of cartographic features and thus for automated generalisation. Support services should extract and model this knowledge from the raw data and make it available to other generalisation operators. On the one hand the structural knowledge can be expressed by enriching map features with additional geometries or attributes. On the other hand, there exist various hierarchical and nonhierarchical relationships between map features, many of which can be represented by graph data structures. After a brief introduction to the interoperable web service framework, this paper proposes a taxonomy of generalisation support services and discusses its elements. It is then shown how the complex output of such services can be represented for use with web services and stored in a reusable fashion. Finally, the utilisation of support services is illustrated on four implementation examples of support services that also highlight the interactions with the generalisation operators that use these auxiliary services

“The Road and the River Should Cross at the Bridge ” Problem: Establishing Internal and Relative Topology in an MRDB

Abstract The search for a robust vector data model that can generate a hierarchy of multiple representations, which are valid for analysis at multiple resolutions, from a single highly detailed version, challenges GIS and cartographic research communities. Hierarchic decomposition of raster data is commonly accomplished by pyramid building algorithms. It is not possible in current GIS environments to create pyramids for vector data. The reason is that raster data contain pixels that can be resampled at regular or randomized intervals. Vector data in contrast contain features that nest, that connect, that have intrinsic contextual meanings with respect to each other. This paper presents a working implementation of a pyramid architecture (MRVIN) for vector data that preserves line length, local coordinate density, and valid topology at multiple levels of resolution. The paper describes decomposition and reconstruction routines for simple vectors, stream networks, and compound vectors (registered road and river networks); presents the database schema for the pyramid, and describes MRVIN architecture