The Spatial Patterns and Local Economic Determinant of Industrial Agglomeration in Semarang District, Indonesia - R Agung Pangarso; R Suharyadi; R Rijanta (Department of Urban and Regional Planning, Diponegoro University, 2019)
Urbanization creates opportunities for Indonesia, potentially to boost economic growth and create vibrant cities (metropolitan). Urbanization and agglomeration economies should be an important element in Indonesia‘s development as a mid-income country. Manufacturing industry becomes a dominant economic sector in metropolitan area such as Semarang that shows urbanization-industrialization relationship. Industrial agglomeration potentially induces socio-economic changes in the region. To prepare these changes, it is important to understand the spatial dynamics of agglomeration and predict its determinants locally. This paper aims to answer questions related to the spatial patterns and determinants of industrial agglomeration in Semarang Regency, a periphery of Semarang metropolitan. Nearest Neighbor Analysis is used to identify spatial patterns, followed by Ellison and Glaeser Index to measure agglomeration strength, and Specialization Index to measure industrial specialization. Geographically Weighted Regression is used to identify determinants of agglomeration. Analysis uses geographical database of Large and Medium Industries in 2016 and related sub-district based data. Result shows 11 of 21 sub-sectors of industries geographically form clustered (agglomerated) pattern. Six of them are strongly agglomerated (most localized). High specializations in these six sub-sectors occur in 14 sub-districts. Result obtains a significant spatial regression model explains the effect of independent variables simultaneously occurring in three sub-sectors: beverages; wearing apparel; wood and products of wood and cork, except furniture, articles of straw and plaiting materials. Partially, industrial agglomeration by three sub-sector’s specializations in sub-district level is determined by variables: industrial employment; vocational school; Gross Regional Domestic Product; population; arterial road; agricultural land availability; and agricultural households.