Abstract
Statistical analysis of rainfall-triggered landslides inventory is a key for landslide hazard
and risk prediction analysis of susceptible areas, also, it acts as a vital input into
current/future development plans of decision makers. The main objective of the study is to
test if the inventory locations has spatial auto-correlation; that could either be clustering
(spatial attraction), dispersed or random distribution (spatial independency). Two
categories of spatial distance functions were applied, first using, first-order distance
analysis using Quadrat Counts function and kernel density analysis. 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 in 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 illustrations and confirm the landslides
distribution pattern in most landslide prone area of Trabzon city, northern turkey. The
current study aims to facilitate the integration between spatial data and the coding in R
environment through using an extensive research libraries and tools.