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Mathematical Problems in Engineering
Volume 2016, Article ID 1612901, 12 pages
http://dx.doi.org/10.1155/2016/1612901
Research Article

Application of the Multitype Strauss Point Model for Characterizing the Spatial Distribution of Landslides

1National Remote Sensing Centre, Department of Space, Government Of India, Hyderabad 500 037, India
2Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, Netherlands

Received 16 July 2015; Revised 30 March 2016; Accepted 20 April 2016

Academic Editor: Filippo Ubertini

Copyright © 2016 Iswar Das and Alfred Stein. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Landslides are common but complex natural hazards. They occur on the Earth’s surface following a mass movement process. This study applies the multitype Strauss point process model to analyze the spatial distributions of small and large landslides along with geoenvironmental covariates. It addresses landslides as a set of irregularly distributed point-type locations within a spatial region. Their intensity and spatial interactions are analyzed by means of the distance correlation functions, model fitting, and simulation. We use as a dataset the landslide occurrences for 28 years from a landslide prone road corridor in the Indian Himalayas. The landslides are investigated for their spatial character, that is, whether they show inhibition or occur as a regular or a clustered point pattern, and for their interaction with landslides in the neighbourhood. Results show that the covariates lithology, land cover, road buffer, drainage density, and terrain units significantly improved model fitting. A comparison of the output made with logistic regression model output showed a superior prediction performance for the multitype Strauss model. We compared results of this model with the multitype/hard core Strauss point process model that further improved the modeling. Results from the study can be used to generate landslide susceptibility scenarios. The paper concludes that a multitype Strauss point process model enriches the set of statistical tools that can comprehensively analyze landslide data.