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Due to the ubiquity of GPS enabled devices and the advances in sensing technologies, trajectory data has become abundant. Regions of interest are important since they describe specific hot-spots within the data that often correlate with domain specific phenomena. Traditional region of interest mining utilises grid based rasters to model space. This suffers from two main problems: hard to determine...
Flickr represents a massive opportunity to mine valuable human movement data from geo-tagged photos. However, existing Flickr trajectory data mining research has not considered mining frequent trajectory patterns whilst also considering the temporal domain. Therefore, a significant opportunity exists to demonstrate the application of a pattern mining algorithm to a large geo-tagged photo dataset....
Millions of geo-tagged photos are becoming available due to the widespread of photo-sharing websites. These social medias capture attractive points-of-interest and contain interesting photo-taking patterns. Massive amount of these user-oriented data produces new challenges and understanding people's photo-taking behavior is of great importance for local tourism-related businesses. This paper analyzes...
The Internet has penetrated to every aspect of our daily life. Users are feeding their georeferenced knowledge, preference, consumer patterns and behaviors, likes and dislikes, and living patterns to the Web. Proper understanding of these user-provided georeferenced Web data is of great importance. This article combines Web 2.0 and Geospatial Web to retrieve and map user-driven data, and mixes clustering...
Clustering has been widely used in many data mining applications. It reports aggregations and concentrations in large databases. There has been a lot of research in clustering, but relatively less attention has been paid to what contributed to those clusters. This paper overviews reasoning techniques for clusters, and reviews qualitative cluster reasoning framework.
Clustering is a core technique in many disciplines that assigns objects into similar groups. It provides answers for where, when and what objects are aggregated. As clustering becomes more important and popular, post-clustering activities that attempt to answer why they (what objects) are there (spatial) and/or then (temporal) need a great attention. This paper suggests a qualitative cluster reasoning...
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...
Intelligent crime analysis allows for a greater understanding of the dynamics of unlawful activities. Discovering crime and spatial features that exhibit strong correlation allows a deeper insight into the complex question of crime analysis. To effectively search heterogeneous data types for cross correlation, a spatial multivariate association measure can be used. We demonstrate a bivariate spatial...
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...
This paper presents a method of creating a hybrid Voronoi approach to a real representation. This representation builds the positive qualities of the vectorized model into the Voronoi diagram to improve upon functionality within a geo information environment. This alternative representation aims to offer notable performance improvements whilst maintaining comparable accuracy to its original counterpart...
Exploratory spatial analysis is increasingly necessary as larger spatial data is managed in electro-magnetic media. We propose an exploratory method that reveals a robust clustering hierarchy from 2-D point data. Our approach uses the Delaunay diagram to incorporate spatial proximity. It does not require prior knowledge about the data set, nor does it require preconditions. Multi-level clusters are...
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