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We propose efficient and effective sequential-scan algorithms for intelligent emergency planning, spatial analysis and disaster decision support through the use of Voronoi Tessellations. We propose a modified distance transform algorithm to include complex primitives (point, line and area), Minkowski metrics, different weights, obstacles and higher-order Voronoi diagrams. Illustrated examples demonstrate...
We propose a flexible raster image districting framework based on generalized Voronoi diagrams through Euclidean distance transforms. We introduce a three-scan algorithm that segments raster images in O(N) time when N is the number of pixels. The algorithm is capable of handling generators of complex types (point, line and area), Minkowski metrics and different weights. This paper also provides applications...
In this article, we propose a framework that combines geographic knowledge discovery and geo-referenced Web 2.0 in order to mine ever-increasing user supplied datasets. We examine the capabilities of Web 2.0 technologies and the process of geographic knowledge discovery. The combination of these two emerging fields overcomes the fundamental problems of geographic knowledge discovery. The proposed...
In this article, we propose a Voronoi diagram based computational model for geospatial data mining from Geo Web model in particular from Yahoo Local. The spatial analysis provided by generalized Voronoi diagrams can be effectively used for various market analysis and strategy decision support. The advantages of using this computational model from Geo Web model is the access to enormous amounts of...
In this article, we propose a general-purpose disaster support system based on generalized (multiplicatively-weighted order-k Minkowski-metric) Voronoi diagrams. The proposed system is capable of handling disasters (or emergency units) having different weights in the complete order from 1 to k in the three popular Minkowski metrics (Euclidean, Manhattan and Maximum distance space). The proposed model...
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