Effects of street-level physical environment and zoning on walking activity in Seoul, Korea

Sung, Hyungun; Go, Doo-Hwan; Choi, Chang Gyu; Cheon, SangHyun; Park, Sung Jin

doi:10.1016/j.landusepol.2015.07.022

Cited by 29 publications

(29 citation statements)

References 23 publications

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“…The data collected from different years have been used, and the subjects have also varied. By using 2009 PVS data, Kang [17] analyzed the effects of spatial accessibility and centrality on weekday and weekend pedestrian volume, and Sung et al [56] focused on the effect of the zoning type. Sung et al [16] tested Jacobs’s theory [13] on street life by using 2010 PVS data.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of PM10 Levels on Pedestrian Volume: Findings from Streets in Seoul, South Korea

Chung

Kim

2019

IJERPH

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Although many studies have revealed that both air quality and walking activity are dominant contributors to public health, little is known about the relationship between them. Moreover, previous studies on this subject have given little consideration to the day-to-day atmospheric conditions and floating populations of surrounding areas even though most pedestrian count surveys are not conducted on a single day. Against this backdrop, using the 2015 Pedestrian Volume Survey data and quasi-real-time weather, air quality, and transit ridership data in Seoul, this study investigates the relationship between particulate matter (PM)10 and pedestrian street volumes empirically. The regression results suggest that PM10 concentration determines people’s intention to walk and affects the volume of street-level pedestrians. The three regression models, which adopted different spatial aggregation units of air quality, demonstrated that PM10 elasticity of pedestrian volume is the largest in the borough-level (the smallest spatial unit of air quality alert) model. This means that people react to the most accurate information they can access, implying that air quality information should be provided in smaller spatial units for public health. Thus, strengthening air quality warning standards of PM is an effective measure for enhancing public health.

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Section: Literature Reviewmentioning

confidence: 99%

The Impact of PM10 Levels on Pedestrian Volume: Findings from Streets in Seoul, South Korea

Chung

Kim

2019

IJERPH

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“…The land use of Cambé was divided into 5 categories: residential, commercial, entertainment (including restaurants, bar, cafeterias, gym, leisure, country houses), service (including offices, clinics, small industries and factories) and institutional (including all townhall facilities, schools, institutions). Only the street level (ground floor) was considered as this level is associated with walking (SUNG et al, 2015) and urban vitality (JACOBS, 2011). Google Street View was adopted to determine all the land uses.…”

Section: Resultsmentioning

confidence: 99%

Understanding walkable areas: applicability and analysis of a walkability index in a Brazilian city

2018

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Considering the growing of chronic non-communicable diseases worldwide, many studies have pointed out the role of the built environment as a support for walking. These studies are being applied in metropolitan and county regions or in specific areas of larger citiesof developed countries. Nevertheless, there is noevidences of the application of the walkability index in an entire Brazilian city. The main objective of this study is to understand walkable areas in a Brazilian medium sized city such as Cambé - PR, based on the walkability variables defined by Frank et al. (2010). The index systematizes four built environment variables: net residential density, retail floor area ratio, intersection density and land use mix. The results show that higher walkability areas are located in downtown and in three areas in the outskirts of Cambé, which were formerly designed to attend social housing projects. The transformations of land use mix and the urban layout characteristics of these areas improved the walkability level. This paper contributes to enlighten the characteristics of walkable areas and highlights a different outcome when considering a city of a developing country such as Brazil.

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“…The GWR model has also been successfully applied in the field of urban spatial analysis for urban landscapes (Gao & Li, ), land uses (Tu & Xia, ), urban transport (Cardozo, García‐Palomares, & Gutiérrez, ), etc. In addition, some POI data, such as variables that reflect convenience facilities (e.g., education, company, shopping), that are related to residents’ lives show high correlation with human activities (Bakillah et al, ), while the densities of transport facilities and land use perform well in some models (Nadai et al, ; Sung, Go, et al, ; Sung, Lee, et al, ). However, the construction environment has no significant impact on human activities (Ewing & Cervero, ).…”

Section: Materials and Methodologymentioning

confidence: 99%

“…POI data, such as variables that reflect convenience facilities (e.g., education, company, shopping), that are related to residents' lives show high correlation with human activities (Bakillah et al, 2014), while the densities of transport facilities and land use perform well in some models (Nadai et al, 2016;Sung, Go, et al, 2015;Sung, Lee, et al, 2015). However, the construction environment has no significant impact on human activities (Ewing & Cervero, 2010).…”

Section: Gwr Methodsmentioning

confidence: 99%

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Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China

Zuo

Xiao²,

Luo

et al. 2019

Growth and Change

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With the development and popularity of mobile Internet technology, data sources of human activity in urban centers are rapidly updated and play an important role in supporting urban planning and management. Therefore, it is critical to integrate different data sources and detect spatially implicit information in the spatial pattern of relationships between urban human activity and related geographical factors. A new analytical framework is first proposed to integrate multisource location‐based big data and use these data to analyze dynamic real‐time human activity density (HAD). Taking Wuhan, the largest city in central China as an example, using the Baidu’s thermal data, this paper analyzes spatiotemporal characteristics of HAD distributions at different points on weekends and weekdays, and further combines the relevant cities’ points of interest data to analyze the correlations between different spatial elements and HAD distributions. The results show that: (a) Using a new indicator and data processing method can simply achieve effective utilization of Baidu’s thermal data; (b) Combined with standardized grids, spatial density estimation can match the two different data sources in this study; (c) The greater the HAD, the greater is the elasticity of change, and in the active population area, the densities of human activity on weekends and weekdays at different times have significant differences; and (d) Different geographically weighted regression models effectively distinguish the influence of different urban elements on weekdays and weekends. In particular, the impact patterns of the workplace, education, and cityscape reflect the unique spatial patterns of research cases. These findings, as well as visual analytics, help in the understanding of the potential value of Baidu heatmaps in urban study and provide support for more scientific and accurate urban planning and space management for the better consideration of real‐time changes in human activity.

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Effects of street-level physical environment and zoning on walking activity in Seoul, Korea

Cited by 29 publications

References 23 publications

The Impact of PM10 Levels on Pedestrian Volume: Findings from Streets in Seoul, South Korea

The Impact of PM10 Levels on Pedestrian Volume: Findings from Streets in Seoul, South Korea

Understanding walkable areas: applicability and analysis of a walkability index in a Brazilian city

Urban human activity density spatiotemporal variations and the relationship with geographical factors: An exploratory Baidu heatmaps‐based analysis of Wuhan, China

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