Christopher Torpelund-Bruin

Geographic knowledge discovery from Web Map segmentation through generalized Voronoi diagrams

Highlights? Propose a flexible sequential-scan based algorithm for various generalized Voronoi diagrams. ? The algorithm is computationally efficient in O(F) time where F is the number of pixels. ? The algorithm is flexible and effective to be used with various environmental settings. ? Applications of the algorithm discovering geographic knowledge through Web Map segmentation. Web maps have become an important decision making tool for our daily lives. We propose a flexible Web Map segmentation method in order to better use them for decision makings. We extend the distance transform algorithm to include complex primitives (point, line and area), Minkowski metrics, different weights and obstacles. The algorithms and proof are explained thoroughly and illustrated. Efficiency and error for the novel algorithms are also detailed. Finally, the usefulness of the algorithms is demonstrated through a series of real-life case studies.

Map segmentation for geospatial data mining through generalized higher-order Voronoi diagrams with sequential scan algorithms

Segmentation is one popular method for geospatial data mining. We propose efficient and effective sequential-scan algorithms for higher-order Voronoi diagram districting. We extend the distance transform algorithm to include complex primitives (point, line, and area), Minkowski metrics, different weights and obstacles for higher-order Voronoi diagrams. The algorithm implementation is explained along with efficiencies and error. Finally, a case study based on trade area modeling is described to demonstrate the advantages of our proposed algorithms.

Raster voronoi tessellation and its application to emergency modeling

Due to the advances in Web technologies, various raster maps are available through Web Map Services such as Google maps and Yahoo maps. These online maps are used to visualize diverse types of disasters. Understanding disasters with these online maps has become an important research issue. In this article, we propose a map-based general-purpose emergency management support system based on a computational model of generalized (multiplicatively weighted, order-k, and Minkowski-metric) Voronoi diagrams. The proposed system tessellates Web maps and models disasters (or emergency response units) having different weights in the complete order from 1 to k-1 in the three popular Minkowski metrics (Euclidean, Manhattan, and Maximum distance) provide insightful information for various what-if emergency scenarios. The proposed map-based emergency management support system systematically supports neighboring queries, districting queries, location optimization queries, and routing queries. We provide specific examples to illustrate how our system supports these queries.

When Generalized Voronoi Diagrams Meet GeoWeb for Emergency Management

This article is to investigate a Voronoi-based computational model for Geographic Knowledge Discovery (GKD) from Geo-reference Web 2.0 datasets to provide detailed emergency management analysis of various geospatial settings including various distance metrics; weights modeling different speeds, impacts, sizes, capacities of disasters; point, line and areas of influence representing disasters, obstacles blocking interactions such as political boundaries, rivers, and so on; higher order neighbors in case the first k -nearest neighbors are currently busy or not functioning; any combination of these settings. The framework is analyzed for efficiency and accuracy and tested in a variety of emergency life-cycle phases consisting of real datasets extracted from GeoWeb 2.0 technologies.

Voronoi Image Segmentation and Its Applications to Geoinformatics

As various geospatial images are available for analysis, there is a strong need for an intelligent geospatial image processing method. Segmenting and districting digital images is a core process and is of great importance in many geo-related applications. We propose a flexible image segmentation framework based on generalized Voronoi diagrams through Euclidean distance transforms. We introduce a three-scan algorithm that segments 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 of the proposed method in various geoinformation datasets. Illustrated examples demonstrate the usefulness and robustness of our proposed method.

MARKET AREA ANALYSIS AND INTELLIGENCE THROUGH GEOWEB MAP SEGMENTATION

Segmentation is of particular interest in market area analysis and intelligence. A generalized Voronoi influence model provides a flexible framework for segmenting diverse business cases and market tessellations. In this article, we propose a Voronoi influence model–based computational framework for mining various user-supplied business and market data sets from a GeoWeb model, in particular from Yahoo Local. The market area analysis and intelligence provided by the generalized Voronoi model can be effectively used for various market intelligence and strategy decision support. The advantages of using this computational model from the GeoWeb framework is the access to enormous amounts of participation-driven information on businesses and services available on the Internet. The proposed model supports weighted analysis, order-k analysis (k-nearest neighbor analysis), various metrics modeling different scenarios, complex modeling data types representing complex real-world situations, and obstacles modeling real-world barriers through a series of generalized Voronoi diagrams. We provide a series of cases studies that demonstrate the usefulness and practicability of the proposed model.

What-If Emergency Response Through Higher Order Voronoi Diagrams

As witnessed in many recent disastrous events, what-if emergency response is becoming more and more important to prevent hazards, to plan for actions, to quickly respond to minimize losses, and to recover from damages. In this chapter, we propose a complete set of higher order Voronoi diagram-based emergency response system for what-if analysis which is particularly useful in highly dynamic environments. This system is based on a unified order-k Delaunay triangle data structure which supports various topological and regional queries, and what-if analysis. The proposed system encompasses (1) what-if scenarios when new changes are dynamically updated; (2) what-if scenarios when order-k generators (disasters or professional bodies) or their territorial regions are of interest; (3) what-if scenarios when ordered order-k generators or their territorial regions are of interest; (4) what-if scenarios when k-th nearest generators or their territorial regions are of interest; and (5) what-if scenarios with mixtures of the above.

A Voronoi-based model for emergency planning using sequential-scan algorithms

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 the usefulness and robustness of our proposed computation model.

Multiplicatively-weighted order-k Minkowski-metric Voronoi models for disaster decision support systems

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 supports neighboring queries, districting queries, location optimization queries and routing queries.