Mosquito management within households remains central to the control of dengue virus transmission. An important factor in these management decisions is the spatial clustering of Aedes aegypti. We measured spatial clustering of Ae. aegypti in the town of Borbón, Ecuador and assessed what characteristics of breeding containers influenced the clustering. We used logistic regression to assess the s...

Polygons can serve an important role in the analysis of georeferenced data as they provide a natural representation for particular types of spatial objects and in that they can be used as models for spatial clusters. This paper claims that polygon analysis is particularly useful for mining related, spatial datasets. A novel methodology for clustering polygons that have been extracted from diffe...

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. Spatial clustering is one of the very important spatial data mining techniques which is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. So far, a lot of spatial clustering algorithms have been proposed in many applic...

Polygons can serve an important role in the analysis of georeferenced data as they provide a natural representation for particular types of spatial objects and in that they can be used as models for spatial clusters. This paper claims that polygon analysis is particularly useful for mining related, spatial datasets. A novel methodology for clustering polygons that have been extracted from diffe...

BACKGROUND In many of the European countries affected by Bovine Spongiform Encephalopathy (BSE), case clustering patterns have been observed. Most of these patterns have been interpreted in terms of heterogeneities in exposure of cattle to the BSE agent. Here we investigate whether spatial clustering is present in the Dutch BSE case data. RESULTS We have found three spatial case clusters in t...

In this paper, we will propose a distributable clustering algorithm, called Distributed-GridMST (D-GridMST), which deals with large distributed spatial databases. D-GridMST employs the notions of multi-dimensional cube to partition the data space involved and uses density criteria to extract representative points from spatial databases, based on which a global MST of representatives is construc...

The unprecedented large size and high dimensionality of existing geographic datasets make the complex patterns that potentially lurk in the data hard to ®nd. Clustering is one of the most important techniques for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods focus on the speci®c characteristics ...

In this paper we present CP, a new clustering algorithm for large spatial databases, which exploits spatial access methods for the determination of closest pairs. Several extensions are presented for scalable clustering in large databases that contain clusters of various shapes and outliers. Due to its characteristics, the proposed algorithm attains the advantages of hierarchical clustering and...

Cluster detection in Spatial Databases is an important task for discovery of knowledge in spatial databases and in this domain density based clustering algorithms are very effective. Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm effectively manages to detect clusters of arbitrary shape with noise, but it fails in detecting local clusters as well as clusters of d...