The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference is implemented using Markov chain Monte Carlo (MCMC) methods to obtain efficient estimates of spatia...

The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, w...

Assessing nitrogen (N) status is important from economic and environmental standpoints. To date, many spectral indices to estimate cotton chlorophyll or N content have been purely developed using statistical analysis approach where they are often subject to site-specific problems. This study describes and tests a novel method of utilizing physical characteristics of N-fertilized cotton and comb...

The global spread of highly pathogenic avian influenza (H5N1) in wild birds and poultry is considered a significant pandemic threat. Furthermore, human infections resulting from direct contact with infected birds/poultry pose a serious public health threat. From November 2003 to March 2007, a total of 3345 H5N1 outbreaks were reported worldwide. Spatial and temporal patterns can provide clues i...

Domain experts are frequently interested to analyze multiple related spatial datasets. This capability is important for change analysis and contrast mining. In this paper, a novel clustering approach called correspondence clustering is introduced that clusters two or more spatial datasets by maximizing cluster interestingness and correspondence between clusters derived from different datasets. ...

Clustering is an important descriptive model in data mining. It groups the data objects into meaningful classes or clusters such that the objects are similar to one another within the same cluster and are dissimilar to other clusters. Spatial clustering is one of the significant techniques in spatial data mining, to discover patterns from large spatial databases. In recent years, several basic ...

Spatial clustering is a fundamental task in Spatial Data Mining where the goal is to group nearby sites and form clusters of homogeneous regions. Spatial clustering must be driven by the discrete spatial structure of data that expresses the (spatial) relational constraints between separate sites. Only similar sites (transitively) connected in the discrete spatial structure may be clustered toge...

Spectral clustering in spatial data mining plays a very important and innovative role due to its capacity of handling of large size of data ,effective application of linear algebra to solve graphical representation and problems, and application of very low cost of clustering algorithms like k-nearest or є neighbourhood graph. Most of the research in this area is focused on efficient query proce...

In this paper, we introduce a model-based method for clustering multiple curves or functionals under spatial dependence specified up to a set of unknown parameters. The functionals are decomposed using a semi-parametric model where the fixed effects account for the large-scale clustering association and the random effects for the small scale spatialdependence variability. The clustering model a...

In spatial clustering, the scale of spatial data is usually very large. Spatial clustering algorithms need high performance, good scalability, and are able to deal with noise and multidimensional data. In this paper, we propose a rapid spatial clustering algorithm based on hierarchical-partition tree. The proposed algorithm partitions spatial data into subsets by simple arithmetical calculation...