Spatial Disparity of Visitors Changes during Particulate Matter Warning Using Big Data Focused on Seoul, Korea

Sang-Hyeok Lee and Jung Eun Kang
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Sang-Hyeok Lee: Department of Urban Planning and Engineering, Pusan National University, Busan 46241, Korea
Jung Eun Kang: Department of Urban Planning and Engineering, Pusan National University, Busan 46241, Korea

IJERPH, 2022, vol. 19, issue 11, 1-16

Abstract: This study examined the changes in the number of visitors to regions during periods of high particulate matter (PM) concentrations in Seoul and analyzed the regional differences of these changes. Further, it examined the spatial characteristics that affect these regional differences. This study mapped the regional differences by conducting a spatial cluster analysis using GIS and examined factors affecting the regional differences using logistic regression analysis. The visiting population data used in this study were obtained from the Big Data on the de facto population measured every hour at mobile base stations, and all analyses were conducted in terms of weekdays and weekends. The results indicated that the visiting population decreases significantly on weekdays when there are high PM concentrations; however, visits increase on weekends, even during periods of high PM concentrations. Moreover, there was a huge regional gap in visiting population changes. Regions with more commercial use, higher bus accessibility, and better pedestrian environment (pedestrian paths, Walk Score) were more likely to be hotspots, whereas regions with high residential and industrial use were more likely to be cold spots. These results can be used as the basic data for PM policies based on regional characteristics.

Keywords: particulate matter; visiting population; spatial disparity; hotspot; Seoul (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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