Demystifying the Geography of Urbanization in Indonesia (Case Study: Southeast Sulawesi Province)

Abstract

What is a city? How can we define the boundaries of a city? Despite being the popular subject of research in rapid urbanization, the discussion of the spatial dimension of urbanization is still few in Indonesia. This paper aims to make the geography of urbanization in Indonesia clearer and easier to understand by applying the degree of urbanization (DEGURBA/DoU) methodology. The case study location proposed is Southeast Sulawesi province (SSP)which has distinctive urbanization trajectories with rapid urbanization trends and a relatively stable rural population, but a lack of research regarding its urbanization patterns. This research used Global Human Settlement Layer (GHSL) data as the primary source to conduct spatial analysis to produce the degree of urbanization with complementary national statistical and spatial data to compare with the current conventional classification methodology. The findings show that based on DoU, 39.87% of the SSP population live in urban centers, 22.58% in urban cluster/periurban, and 37.56%. SSP has a total of 49 urban units with 6 urban centers/cities, and 43 urban clusters/towns. By testing the rank-size distribution of SSP’s urban units, it shows that SSP’s urban systems pattern satisfy Zipf’s law with R2 = .9885, and the slope of the fitted line is -0.9554. Based on this result, currently, SSP has 4 medium cities, 2 small cities, 21 large towns, and 20 small towns. In addition, the DoU also has an opportunity to understand how should urban areas be governed and managed, and by whom. Despite the Greater Kendari Urban Area has not yet fulfilled the national’s Metropolitan population threshold (1 million pop), the urban management and development should be conducted more collaboratively with neighboring regencies such as Konawe and South Konawe. The advantages are shown by these findings highlight the importance of scaling up this research to a national scale and using finer and nationally available data.