Formulation of huge lattice spatial adjacency matrices with non-rectangular shape of socio-economic grid-cell data for the analysis of sustainable economy with high computational efficiency

Gigih Fitrianto, Shojiro Tanaka, Ryuei Nishii

Research output: Contribution to journalArticle


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 re gions. 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.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalResearch in World Economy
Issue number2
Publication statusPublished - Jan 1 2018


All Science Journal Classification (ASJC) codes

  • Industrial relations
  • Sociology and Political Science
  • Economics, Econometrics and Finance (miscellaneous)
  • Political Science and International Relations

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