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1 2017

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Binary segmentation methods for identifying boundaries of spatial domains

Raveendran Nishanthi, Sofronov Georgy

Annals of Computer Science and Information Systems

2017

Vol. 13

95--102

Spatial clustering is an important component of spatial data analysis which aims in identifying the boundaries of domains and their number. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields. In this paper, we focus on identifying homogeneous sub-regions in binary data, which indicate the presence or absence of a certain plant species which are observed over a two-dimensional lattice. The problem of finding regional homogeneous domains is known as segmentation, partitioning or clustering. To solve this clustering problem we propose to use the change-point methodology. We develop new methods based on a binary segmentation algorithm, which is a well-known multiple change-point detection method. The proposed algorithms are applied to artificially generated data to illustrate their usefulness. Our results show that the proposed methodologies are effective in identifying multiple domains and their boundaries in two dimensional spatial data.