Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Spatial tessellations: concepts and applications of Voronoi diagrams
Spatial tessellations: concepts and applications of Voronoi diagrams
Continuous cartogram construction
Proceedings of the conference on Visualization '98
CartoDraw: A Fast Algorithm for Generating Contiguous Cartograms
IEEE Transactions on Visualization and Computer Graphics
Geovisualization for Knowledge Construction and Decision Support
IEEE Computer Graphics and Applications
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Information Space Regionalization Using Adaptive Multiplicatively Weighted Voronoi Diagrams
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Temporal Visualization of Planning Polygons for Efficient Partitioning of Geo-Spatial Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Computational Geometry: Theory and Applications
IEEE Transactions on Visualization and Computer Graphics
Spatio-temporal sensing and visualizing of CO2
SIGGRAPH '09: Posters
Hi-index | 0.00 |
The dreaded effects of climate change have led to a new research focus in many applications. In urban planning, the visualization of carbon footprints has become one of the most sought after aspects. Urban planning data of carbon footprints contains spatial [location] and abstract [statistical indicators] information. Although many techniques for the visualization of such partially spatial data have been successfully applied in the area of geovisualization, the core focus has been on a global depiction of non-spatial information. However, conducting local comparisons, as in the case of comparing neighborhood districts and househoLds, is of particular importance in investigative tasks. Additionally, representing different carbon footprint indicators [multiple nonspatial parameters] and unstructured parameter values [resulting in scaling issues] in a static representation provides an interesting challenge for visualization. This paper describes a novel and generic solution to the above-mentioned issues: a neighborhood relation diagram for the local comparison of non-spatial information in partial spatial data. The technique is based on the geometric computation of Voronoi diagrams according to a weighted neighborhood metric. The shape of spatial regions [e.g. city districts] within this diagram is characterized by a directed and constrained deformation according to the non-spatial [i.e. carbon footprint] relations to neighboring regions. The effectiveness of our method is highlighted in a preliminary study of carbon footprint patterns in downtown Phoenix [Arizona, USA]. In this study, neighborhood relation diagrams enable city planners to detect local effects on carbon emissions and their relation to planning projects.