Journal of Applied and Natural Science 7 (1): 501–513 (2015)
Spatial autocorrelation analysis in plant population: An overview.
18E/564 Chopasni Housing Board, Jodhpur (Rajasthan), INDIA
Present Address: Plant Ecology Laboratory, Central Arid Zone Research Institute, Jodhpur (Rajasthan), INDIA
*Corresponding author. E-mail: email@example.com
Abstract : Analysis of spatial distribution in ecology is often influenced by spatial autocorrelation. In
present paper various techniques related with quantification of spatial autocorrelation were categorized. Three broad
categories namely global, local and variogram were identified and mathematically explained. Local measurers captures the
many local spatial variation and spatial dependency while global measurements provide only one set of values that represent
the extent of spatial autocorrelation across the entire study area. Global spatial autocorrelation measures the overall
clustering of data and it included six well defines methods, namely, Global index of spatial autocorrelation, Joint count
statistics, Moran’s I, Geary’s C ration, General G-statistics and Getis and Ord’s G. The study revealed that out of the six
methods Moran’s I index was most frequently utilized in plant population study. Based on their similarity degree, local indicator
of spatial association (LISA) can differentiate the neighbors in to hot and cold spots. Correlogram and variogram approaches are
Keywords :Correlogram, Global and Local Autocorrelation, Moran’s I Spatial Autocorrelation, Variogram approaches.