Applicability of R statistics in analyzing landslides spatial patterns in Northern Turkey

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dc.contributor.authorAlthuwaynee, Omar F.
dc.contributor.authorMusakwa, Walter
dc.contributor.authorGumbo, Trynos
dc.contributor.authorReis, Selçuk
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T08:21:38Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T08:21:38Z
dc.date.issued2017
dc.departmentMühendislik Fakültesi
dc.description2nd International Conference on Knowledge Engineering and Applications, ICKEA 2017 -- 21 October 2017 through 23 October 2017 -- -- 133656
dc.description.abstractStatistical analysis of rainfall-triggered landslides inventory patterns is a key for landslide hazard and risk prediction analysis of susceptible areas. The main objective of the study is to test if the landslides locations are spatially auto correlated, that could either be clustered (spatial attraction), dispersed or randomly distributed (spatially independent). Two categories of spatial distance functions were applied, first using, first-order distance analysis using Quadrat Counts function and kernel density analysis. The second category used second order distance analysis includes Diggle's empty space F-function and nearest neighbor distance G-function, and also, more sophisticated Ripley's K-function, which evaluates the distribution of all neighbor distances within the space taking into consideration the edge correction effect. Based on the generated curves by the G, F and K functions, we observed that landslides locations clearly tend to be clustered in certain areas rather than randomly distributed. Eventually, Moran's I autocorrelation function used to find where the highest amount of landslides are clustered using four conditioning factors (Elevation, Slope, Land-cover, and Geology). This study tests the landslides distribution pattern in landslide prone area of Trabzon city, northern turkey. The current study aims to facilitate the integration of spatial data and the coding in R environment through using the R extensive research tools and libraries. © 2017 IEEE.
dc.identifier.doi10.1109/ICKEA.2017.8169933
dc.identifier.endpage225en_US
dc.identifier.isbn9781538621509
dc.identifier.scopusqualityN/A
dc.identifier.startpage221en_US
dc.identifier.urihttps://dx.doi.org/10.1109/ICKEA.2017.8169933
dc.identifier.urihttps://hdl.handle.net/20.500.12451/2273
dc.identifier.volume2017-Januaryen_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2017 2nd International Conference on Knowledge Engineering and Applications, ICKEA 2017
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectG-f Functions
dc.subjectLandslides
dc.subjectMoran's I
dc.subjectRipley'S K-function
dc.subjectSpatial Pattern
dc.subjectTurkey
dc.titleApplicability of R statistics in analyzing landslides spatial patterns in Northern Turkey
dc.typeConference Object

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