The purpose of this research is to develop a methodology for calculating the index of the spatial equity for the public transportation services for the city of Seoul using the Lorenz curve and Gini coefficient based on accessibility to the services. The index was estimated for 100 m × 100 m cells first and then aggregated for 424 traffic analysis zones (TAZs) in the city. The indices for Seoul’s subway and bus were represented with and without the weighting factor. On the basis of this methodology, this study assessed the effect of an additional subway line on the spatial equity before and after its operation. Furthermore, an index of relative difference indicating the change in population and employment in the station areas was applied to investigate the changed effects of spatial equity between population and employment from the new subway line. The core findings from this research are summarized as follows. First, the subway network of Seoul provides more unequal public transportation services for the TAZs than for the bus network. Second, an improvement in the operation frequency of both transit modes would deepen the spatial inequity of Seoul. Third, a subway extension plan for Seoul could noticeably reduce the spatial inequity. The plan would improve the spatial equity for the population more than that for employment. The most important meaning of this study is to be found in combining spatial equity on the supply side with transit-oriented development (TOD) on the demand side. So, by establishing a TOD policy for the additional subway station areas, an appropriate land-use pattern can be established for maximizing potential ridership in consideration of the combination.

This study aimed to assess the robustness and resilience of retail areas in Seoul, based on the changes in sales before and after the COVID-19 outbreak. The spatial range and temporal scope of the study were set as district- and community-level retail areas in Seoul, from January 2019 to August 2020, to consider the effect of the COVID-19 outbreak. The data used in this study comprised sales information from the retail sector, namely Shinhan Card sales data for domestic and foreigners by business type in Seoul, provided by Seoul Big Data Campus. We classified the retail areas based on the change in sales before and after the COVID-19 outbreak, using time series clustering. The results of this study showed that time series clustering based on the change in sales can be used to classify retail areas. The similarities and differences were confirmed by comparing the functional and structural characteristics of the district- and community-level retail areas by cluster and by retail area type. Furthermore, we derived knowledge on the decline and recovery of retail areas before and after a national crisis such as the emergence of a COVID-19 wave, which can provide significant information for sustainable retail area management and regional economic development.