What Determines Mixed Land Use?

Analysis of Big Data on Commercial Districts in Seoul, South Korea

Each commercial district has a different degree of mixed land use. Some commercial areas are a huge cluster with monotonous retail use, while others consists of a wide variety of land uses including residential or office uses as well. The specialization or diversity strategies are both to maintain the vitality of the region. The  homogeneous commercial clusters collectively choose to pursue localization economies among stores of similar businesses, while the heterogeneous commercial clusters enable the multi-purpose activities in one place for urbanization economies.

Locational factors such as proximity to other amenities such as transit and public services have a great influence on the mix of land use. Chen et al.(2016) argued TOD around the railway station enables efficient transit induce mixed land use in the area. Also, Retail and food establishments demonstrate a certain location pattern in the urban area and It make the neighborhood more lively and vigorous (Sevtsuk, 2014). And according to Blumenberg et al.(2019), some young adult and older adults prefers homes for various reasons and they thrive with urban amenities.

A certain age or sex group of customers may also affect it in a commercial area. This paper aims to find the causes of these epic differences in land use mix in terms of the location and socioeconomic environments of each commercial district and the demographic characteristics of visitors and residents. As Manley et al. (2018) used smart card data to analyze regularity of the individual travel patterns in London, we use location based big data (Seoul Living Population, a De Facto population counting with mobile phone gps data) to analyze the visitor’s demographic pattern of commercial districts.

To this end, we introduce a micro-scale approach to determine the key determinants of land use mix (LUM, an entropy index indicates mixed land use) in commercial districts. The land use mix of each district is defined by the entropy index using data for more than 100,000 individual buildings in commercial districts in Seoul, the capital city of South Korea. We also use real-time big data(Seoul Living Population) on individual visitors to commercial districts. The existing studies on the relationship between land use and human activity usually use data collected at a specific time by a survey or the aggregation of existing data. The fixidity of the time dimension of data makes it difficult to examine the dynamic changes of visitors to commercial areas. The real-time big data on visitors enable us to overcome this limitation. By processing mobile phone GPS data, from January 1, 2018 to December 31, 2019, we analyzed the distribution of visitors in the city of Seoul and their visiting patterns in commercial districts along the time dimension. We use the number of visitors in the peak hour of each commercial district, their age and sex as explanatory variables of land use mix in addition to the locations and socioeconomic factors of the district.

The expected results of the analysis are as follows: First, the mixed use of residential and office use in a commercial district has a positive impact on the total number of visitors. Second, the LUM was relatively high in the commercial areas where the proportion of young adult (2-30s) visitors and residents was significantly higher than those in other age groups. And lastly, in high-density metropolises such as Seoul, more visitors are coming to commercial districts with high accessibility by subway or bus. This suggests that transit, together with mixed land use, is an important factor in enhancing the dynamics of the commercial clusters.

Key words

mixed land use, urban commercial clusters, transit accessibility , big data, applied GIS

Major references/citations

1. Sevtsuk, A., & Kalvo, R. (2018). Patronage of urban commercial clusters: A network-based extension of the Huff model for balancing location and size. Environment and Planning B: Urban Analytics and City Science, 45(3), 508-528.
2. Chen, S. H., & Zegras, C. (2016). Rail Transit Ridership: Station-Area Analysis of Boston’s Massachusetts Bay Transportation Authority. Transportation Research Record, 2544(1), 110-122.
3. Zhong, C., Schläpfer, M., Müller Arisona, S., Batty, M., Ratti, C., & Schmitt, G. (2017). Revealing centrality in the spatial structure of cities from human activity patterns. Urban Studies, 54(2), 437-455.
4. Blumenberg, E., Brown, A., Ralph, K., Taylor, B. D., & Turley Voulgaris, C. (2019). A resurgence in urban living? Trends in residential location patterns of young and older adults since 2000. Urban Geography, 40(9), 1375-1397.