Spatial Regression Analysis to See Factors Affecting Food Security at District Level in South Sulawesi Province - Safitri, Irma Yani; Tiro, Muhammad Arif; Ruliana (ARRUS Journal of Mathematics and Applied Science, 2022)
Spatial regression is a development of classical linear regression which is based on the influence of place or location. To determine the location/spatial effect, a spatial dependency test was performed using the Moran Index, and the Lagrange Multiplier (LM) test was used to determine a significant spatial regression model. In this study, spatial regression was applied to the case of food security in each district in South Sulawesi Province. The results of the analysis show that there is a negative spatial autocorrelation, meaning that the spatial effect does not affect the level of food security. The significant spatial regression model is the SEM (Spatial Error Model) model. The equation of the SEM model produces variables that have a significant effect, namely the ratio of normative consumption per capita to net availability, percentage of population living below the poverty line, percentage of households with a proportion of expenditure on food more than 65 percent of total expenditure, percentage of households without access to electricity, percentage of households without access to clean water, life expectancy at birth, ratio of population per health worker to the level of population density, the average length of schooling for women above 15 years, and the percentage of children under five with height below standard (stunting). Thus, the resulting distribution pattern is a uniform data pattern. This means that each adjacent district tends to have different characteristics.