Spatial statistical analysis of soil nutrient data

Spatial data, also known as geospatial data or geographic information it is the data or
information that identifies the geographic location of features and boundaries on
Earth. At the present situation the spatial data is getting more importance in all
around the world, so the analysis of the data is also getting more attention.
Spatial analysis is a formal technique which study entities of real world by using their
topographic, geometric and geographic properties. Spatial statistics includes a
variety of techniques for spatial data analysis; many of these techniques are still
under study.
The primary principal of spatial statistics is based on the assumption that near things
are more related. The precursors of current spatial statistical researchers include
those who sought to describe areal distributions, the nature of spatial interactions,
and the complexities of spatial correlation. The spatial statistical methods in current
use, and upon which research is continuing, include: spatial association, pattern
analysis, scale and zoning, geostatistics, classification, spatial sampling, and spatial
econometrics. In a time-space setting, scale, spatial weights, and spatial boundaries
are especially difficult problem areas for further research.
This study is conducted for analyzing the soil nutrient properties of Nedumudi
panchayath and adjoining panchayats in Alappuzha District of Kerala. Various
techniques like Principal Component Analysis, K-Means Clustering, Getis Ord G
istatistic, Getis Ord G
i* statistic and Moran’s I statistic are used for the analysis.

MSC GIS 2017-2019 INT T Radhakrishnan