Paper Info
Title
A Cost-Efficient Approach For Measuring Moran'S Index Of Spatial Autocorrelation In Geostationary Satellite Data
Abstract
Spatial autocorrelation (SA), describing correlation of a particular feature/ phenomenon with itself across space, is one of the major properties of any spatial data. Among the various measures of SA proposed till date, the Moran's index (I) is the most common as well as significant one. However, measuring Moran's I, which needs to deal with spatial weight between each pair of spatial data objects, becomes almost unfeasible in case of large-scale raster data, like geostationary satellite data, containing several millions of pixels. This paper proposes a method based on the Hadoop MapReduce framework for computing Moran's I in large-scale raster data. The main contribution of the work lies in the implementation of the Mapper and Reducer processes for a cost effective estimation of Moran's I, considering both rook case and queen case of spatial contiguity. The key feature of these algorithms is an efficient manipulation of the spatial weight matrix, and thereby reducing the overall memory and time requirement. The experimentation shows a promising result in this regard.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7730545
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
Spatial autocorrelation, Moran's I, Raster data, Satellite data, MapReduce
Spatial analysis,Data mining,Raster data,Satellite,Contiguity,Matrix (mathematics),Computer science,Remote sensing,Pixel,Geostationary orbit,Cost efficiency
Conference
ISSN
Citations 
PageRank 
2153-6996
1
0.41
References 
Authors
0
2
Name
Order
Citations
PageRank
Monidipa Das1219.31
Soumya Kanti Ghosh234539.91