Detecting spatial economic clusters using kernel density and global and local Moran's I analysis in Ekurhuleni metropolitan municipality, South Africa

Abstract

Agglomeration economies are credited for providing the needed catalyst for economic growth and development. This paper uses approximately 14,000 firm-level data and employs several spatial data analysis approaches to examine evidence of types of spatial sectoral clusters and their footprints in Ekurhuleni Metropolitan Municipality, a major subregional economy in Gauteng metropolis, South Africa. The results of four selected industrial sectors show evidence of varying global and localized clustering. Localized clustering is statistically significant. This research suggests policies that ensure that regional economy's economic growth and development benefit from agglomeration economies.