Identification of Spatial Patterns of Food Insecurity Regions using Moran's I (Case Study: Boyolali Regency)

Abstract

The aim of this research is to identify spatial pattern of food insecurity based on neighbors analysis concept by using Moran's I Method. Besides it, this research is to know whether neighbors analysis concept can be used to be the correlation indicator spatially for food insecurity in an area compared to another area. From FSVA, 2009, the research used seven indicators and the research areas are 19 districts in Boyolali Regency. Based on Neighbors analysis concept by using Moran's I method, there are some district in Boyolali regency, 2008, which is categorized as potentially food insecure area, there are Wonosegoro, Kemusu, Ampel dan Cepogo districts. While on 2009-2010 there are 5 districts, Wonosegoro, Kemusu, Ampel, Cepogo dan Sambi. From this research, the index of Moran's 9 indicators the index is about +1, it means that the indicators has a high correlation. Based on Moran's Index, the indicators which has correlation of the food insecurity in Boyolali Regency from 2008-2010 are Habitant percentage which live below the poor level and limited electricity access in the areas. .

Reconstruction and normalization of LISA for spatial analysis

“…In spatial autocorrelation analysis, the spatial contiguity matrix based on local action is mainly applicable to relationships between regions. The spatial contiguity relationship of regions bears three ways of definitions, that is, Rook’s contiguity, Bishop’s contiguity, and Queen’s contiguity [ 38 ]. Rook’s contiguity plus Bishop’s contiguity yields Queen’s contiguity.…”

“…Summing Eq (38) yields global Geary's coefficient, that is, Eq (24). According to Eq (37), the relation between Anselin's first set of Geary's coefficient and the local Geary's coefficient formula improved in this paper is…”

“…Further, the relationship between local Moran's indexes and local Geary's coefficient can be derived. From Eq (38) it follows…”

Improved IDW Interpolation Application Using 3D Search Neighborhoods: Borehole Data-Based Seismic Liquefaction Hazard Assessment and Mapping

“…The estimation of an unknown value in interpolation is determined by the spatial relationship, which can be defined differently by the neighborhood search method. In the case of a squarebased grid, the neighborhoods are typically Rook, Bishop, and Queen that determine four or eight neighboring cells, and to minimize the edge effect of the square grid, a hexagon grid can be used (Figure 1) [6,7]. However, such traditional raster-based neighborhood analysis is performed based on the assumption of a 2D vector space and is limitedly applied to observation data distributed in 3D space (i.e., fine dust assessment by altitude using a drone [8], borehole-based geology modeling [9], marine environment assessment [10]).…”