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With the recent exponential growth in inter-regional movements of population and information, there is an urgent need for accurately measuring the connectivity and centrality of cities. This study aims to investigate the differences in centrality between different scales of a dataset and to propose a calibration method to minimize the gap between the measures from the two scales. Although urban and regional centrality is examined by analyzing regional commuting datasets, this study proposes that it should be measured using nationwide data to validate the centrality results. To demonstrate this, the differences in regional centrality between different spatial scales of commuting trips for two data groups are shown: Seoul regional data and nationwide data. In this structure, the centrality levels of the 25 districts of Seoul were calculated for both groups. The results clearly show the differences in the centrality levels of districts in both groups: Seongbuk district ranked 10th in the local dataset but fell to 18th in the nationwide dataset; Geumcheon district ranked 22nd in the former but rose to 9th in the latter. The ratio of inner commuting in Seoul is thus relatively low, and each district has dynamic connections with other provinces. Furthermore, the results of a linear regression analysis, which was conducted on a local dataset to obtain similar results as those obtained using a national dataset, demonstrate the significance of a wide-ranging commuting dataset for regional centrality analysis of a specific region.

Accuracy of Regional Centrality Using Social Network Analysis: Evidence from Commuter Flow in South Korea