The advantage of using grid-cell data for socio-economic analysis should be the feasibility to incorporate satellite data that will enrich the regional analysis and has an important role to observe the relationship between socio-economics and nature. This advancement corresponds to the sustainable development goals that balance the socio-economic quality in harmony. In order to perform the analysis, formulation of a spatial adjacency matrix has an important role to project the spatial relationship within regions. However, no precedent research provided a practical formulation for the spatial adjacency matrix in grid-cell data structure (Fitrianto & Tanaka, 2017).The general process that used shapefiles solely, which store geometry and attribute information for the spatial features (ESRI, 1998) to construct the adjacency matrix is not suitable. The problem arises due to the existence of NA cells that represent non-inhabitant areas such as water bodies, yet the shapefile does not contain this information inside the municipal body. The NA cells create a non-rectangular lattice and it is important to exclude them in the analysis to correctly project the real information.This article provides a method to precisely project the real information by using Kronecker product to construct the adjacency matrix and applying a projection matrix to eliminate the NA cells (Tanaka & Nishii, 2009). It showed eminent efficiency compared with commonly used R package called spdep. Experimental results verified that this method, even for huge dimension with a trillion elements, produces more than 2000 times faster elapsed time than the package.
Indonesia is one of the largest coal producers globally, with coal as the main export commodity compared to other commodities in the mining sector. The more competitive the world coal market is, the Indonesian coal market share faces threats from other coal exporting countries. The increasing commitment of countries to reduce air pollution by cutting the use of coal for power plants at PLTU. This study analyzes the competitiveness and various factors that influence the competitiveness of Indonesian coal in 8 export destination countries. This study seeks to determine how the development of Indonesia’s coal competitiveness to the eight central destination countries and what factors affect Indonesia’s coal exports for the 2009-2020 period to the eight central destination countries using RCA analysis and panel data regression. Based on the analysis, results show that the competitiveness of Indonesia’s coal exports to 8 destination countries is excellent. It can be seen from the RCA value obtained by each country from 2009-to 2020, which is greater than 1. Meanwhile, based on the results of panel data, regression estimates with random models show that GDP per capita, population, and coal prices have a negative and significant impact on coal competitiveness in 8 Indonesian coal importing countries. The study results did not find the effect of exchange rates and CPO prices on coal competitiveness in 8 Indonesian coal importing countries.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.