|Browse Archives||About the Journal||For Contributors||e-Submission|
You are not permitted to access the full text of articles.
If you have any questions about permissions,
please contact the Society.
회원님은 논문 이용 권한이 없습니다.
권한 관련 문의는 학회로 부탁 드립니다.
|[ Article ]|
|Journal of Korea Planning Association - Vol. 56, No. 2, pp.5-21|
|Abbreviation: J. of Korea Plan. Assoc.|
|ISSN: 1226-7147 (Print) 2383-9171 (Online)|
|Print publication date 30 Apr 2021|
Final publication date 03 Mar 2021
|Received 03 Aug 2020 Revised 25 Jan 2021 Reviewed 28 Jan 2021 Accepted 28 Jan 2021|
|Relationship Between Urban Decline and Walking Trips by Trip Purpose in Seoul Metropolitan City: Focused on Urban Decline Index|
Mo, Ah-ron** ; Yang, Dongwoo*** ; Shin, Woo-Jin****
|**Ph.D. Student, Department of Regional Development, Chonnam National University (firstname.lastname@example.org)|
|***Research Professor, Center for Regional Development, Chonnam National University (email@example.com)|
|****Associate Professor, Department of Economics, Chonnam National University (firstname.lastname@example.org)|
|Correspondence to : ****Associate Professor, Department of Economics, Chonnam National University (Corresponding Author: email@example.com)|
Urban decline is a multifaceted problem, comprising numerous socio-spatial factors, and has a self-enforcing effect that aggravates already-declined areas. With a growing concern about the declining trend, the central government has actively used institutional interventions, including the Urban Regeneration New Deal Project, to revitalize the physical infrastructure and social capital. With regard to strategic approaches, fostering pedestrian-friendly environments is crucial for revitalization. This study aims to investigate the association between walking trips and urban decline. We use three types of measurements for urban decline: indices with individual subjects, comprehensive decline index with subject, and comprehensive index total. Using the 2016 Household Travel Diary Survey data, we obtained information on walking behaviors and categorized walking trips into utilitarian and leisure walking. Employing multilevel logistic regression with random intercept, we estimate the effects of urban decline factors on the likelihood of a person walking for the purpose of a trip. We find that a decline in the demographic and social environment is related to a decrease in the possibility of walking with utilitarian purposes. In addition, the more a neighborhood (a Dong) declined, the less a resident in the neighborhood walks with utilitarian purposes, while the more leisure walking trips tend to increase. This implies that central and local governments must consider demographic and socioeconomic decline trends in urban regeneration plans to promote walkability. Additionally, it is necessary to consider ways to support leisure walking in declined areas.
|Keywords: Walkability, Urban Decline, Utilitarian Walking, Leisure Walking, Random Intercept Logit Model
키워드: 보행활성화, 도시쇠퇴, 실용보행, 여가보행, 임의절편로짓모형
Along with the paradigm shift in urban planning policies towards sustainable development, the importance of walking has been heightened. Walking is an act that connects every means of transportation and serves as a fundamental mode of the trip in recent urban planning and design concepts such as compact cities and transit-oriented development (TOD) for sustainable development (Choi et al., 2013). In addition to its role as a means of transport, walking is crucial in creating a sustainable city in reducing pollution, creating social capital, and revitalizing streets. (Kim et al., 2014; Sa and Lee, 2018).
Pedestrian policies subject only to the past transportation sector have gradually expanded to a rather extensive project associated with urban regeneration and other relevant fields (Lee and Jung, 2019). Indeed, pedestrian policies are considered one of the core strategies to revitalize declined cities in urban regeneration policies, since walking is the most crucial barometer in understanding how vibrant a city is and at the same time has excellent potential in revitalizing a city as a whole. Meanwhile, as the level of decline nationwide by Eup (Town), Myeon (Township), or Dong (Neighborhood), which are the units of the administrative division of the Republic of Korea, has aggravated from 64.5% in 2013 to 69% in 2017 (Korea Research Institute for Human Settlements, 2019), walking-related projects under the framework of urban regeneration policies for declined cities are expected to increase.
“Decline” is a spatial phenomenon occurring throughout different parts of a city, and the issues resulting from urban decline and interactions of various relevant factors could lead to a vicious, downward spiral. In general, the aspects of urban decline appear in social, economic, and physical sectors. According to Korea Urban Renaissance Center (KURC, 2010), it is possible to define urban decline as “A state in which a city is losing or has lost vitality in three areas: demographic society, industrial economy, and physical environment.” In line with the understanding of urban decline as a phenomenon comprising social, economic, and physical aspects, some attempts to approach the urban decline with social and spatial factors. Earlier studies showed that the urban decline is closely related to several factors, including health and traffic accidents. (Jo et al., 2014; Park and Park, 2017).
Decisions regarding walking are made based on the socio-economic characteristics and walking environment of an individual, and the walking environment encompasses not only the physical elements but also social, economic, and demographic factors. Therefore, we need a comprehensive approach to carrying out studies on walking, taking various factors related to walking into account (Choi et al., 2013). Nevertheless, most empirical studies conducted domestically on walking tend to focus on the relations between walking and the physical environment, and those dealing with social, economic, and demographic factors are still insufficient. In particular, few studies have focused on walking and urban decline as a universal phenomenon, even though pedestrian policies have expanded thus far. Decrease in population, physical deterioration, weakened economic foundation, and spatial concentration of poverty are associated with walking. Also, to encourage walking activity and create a practical effect of walking by policy measures, it is necessary to identify and fine-tune the factors affecting walking activities (Sa and Lee, 2018). Along with the extensive walking-related projects being promoted recently by urban regeneration policies to tackle urban decline issues, studies clearly showing the relation between urban decline and walking should be further carried out.
Against this backdrop, this study aims to review the preceding literature on urban decline and walk and perform empirical analyses on the relation between urban decline and walking trips by purpose1) based on 181,330 trips surveyed in 423 Dongs (administrative districts) in Seoul. Since Seoul Metropolitan City is one of the top places where urban regeneration projects and walking policies are being applied and taking place, this study’s results can easily find utility in policymaking. This study has two broad purposes: The first is to analyze correlations between the decline phenomenon and walking trips by purpose. To that end, we attempt to identify the relation between urban decline factors and walking trips by purpose, controlling individuals’ socio-economic characteristics and physical environment factors linked with walking trip choices from some prior studies. The second is to analyze the decline phenomenon’s effect size at the Dong-level in purpose-specific walking trip choices, using the Proportional Reduction in Error (PRE)2) in the Hierarchical Linear Model.
Problems stemming from urban decline tend to be intensively in specific places in a city and are multi-faceted results generated from complex interactions of diverse social and spatial factors (KURC, 2010). Thus, the urban decline reflects not only physical aspects but also social and economic sides as well, both an empirical analysis and comprehensive interpretation are required to understand the phenomenon (Kim, 2010) fully. To identify the spatial characteristics of the urban decline phenomenon in this study, we looked into case studies in two fields of the subject: ones on the spatial pattern of decline and others dealing with the relation between urban decline and social and spatial factors.
First, we reviewed studies on the spatial pattern of urban decline. Kim, K. J.(2010) performed a qualitative study to identify the causes behind urban decline in Korea and its characteristics. According to its analyses on 84 cities nationwide, spatial concentration of poverty was found, including physical obsolescence of infrastructure, neglected properties, depressing sales, high vacancy, poverty and job loss, and the socially disadvantaged class’s residence. Despite the problems due to poverty, those cities’ social environments are relatively healthier than in Western countries. Lee, H. Y. et al. (2010) derived the multiple decline index (MDI), analyzing the conditions of decline and spatial patterns in 2,171 Dongs or Eups nationwide. They sampled the top 30% of the administrative divisions based on the MDI and found that Dongs or Eups in Seoul and other metropolitan cities took up a significantly large portion, signifying that the decline phenomenon is occurring universally regardless of the size of a city. Chae, H. W. (2013) analyzed the spatial patterns of urban decline within Seoul using the MDI and found that the decline pattern had shifted from the center of the city to the northern districts and that every Gu had the declined Dongs in the city. Next, studies examined the relationship between the urban decline and social or spatial factors. In order to find the relationship between the decline and health, Jo et al. (2014) analyzed how the MDIs in demography, economy, and physical environment are related to the health level of a region, with the health care characteristics of 228 Sis (Cities), Guns (Counties), and Gus (Districts) nationwide serving as controlled variables. The results showed that the higher the MDI rose, the lower the health performance was. Park, N. Y., and Park, B. H. (2017) examined the relations between individual indices of social, economic, and physical decline factors and the number of accidents that occurred in 251 Sis, Guns, and Gus nationwide to shed light on how the urban decline is related to traffic accidents. The results showed that the number of accidents per 1,000 persons became higher as the ratio of elderly population, annual average population growth rate, the ratio of aged dwelling units, percentage change in the number of businesses, and the number of employees in retail and wholesale per 1,000 persons grew higher than others.
Since the physical environment and socioeconomic, and other personal factors may affect walking, a relatively comprehensive approach must fully understand the relation (Choi et al., 2013). Preceding studies have defined socioeconomic characteristics by diverse variables, ranging from those at the individual/household level to neighborhood level, to resolve inequalities in walking activities due to socioeconomic characteristics.
Agrawal and Schimek (2007) found that individuals with different socioeconomic characteristics may choose different walking trips for different purposes. They that with higher levels of education, utilitarian and leisure walking increased accordingly. When the income level was high, the proportion of utilitarian walking was low, but leisure walking was high. The study also confirmed that Blacks were less likely to choose utilitarian walking than Whites even with variables such as income, education, density, and car-ownership factors controlled. Cerin et al. (2009) analyzed what impact socioeconomic characteristics have on walking as a means of mobility both directly and indirectly. The results showed that the higher the household income and neighborhood income level were, the longer the physical activity hours and leisure time for physical activity were, which indirectly impacted walking for mobility. However, there is no significant direct impact. Considering both the direct and indirect impact, walking for mobility increased as the household income decreased and increased neighborhood income. It implies that household income and neighborhood income bear different significance in terms of walking. Lee, Y. S. et al. (2013) examined the changes in pedestrian volumes in Seoul’s neighborhood units and their relations with diverse urban variables. The urban characteristics variables included the search Dong’s socioeconomic characteristics were classified primarily into land use, finance, income level, population, employment, and car ownership factors. The results showed that the land-use diversity index and factors related to population and employment influenced pedestrian volume changes, and as the diversity index and the employee density rose, walking increased. Sung, H. G. et al. (2015) investigated the determinants of walking activities for leisure and trip purposes focusing on individual and household level characteristics, and reported that walking activities for leisure and trip purposes were different. In particular, the differences in leisure purposes mainly arose from gaps in personal attributes, while the walking activity time for trip purposes differed not only by individual attributes but also by regional characteristics. The higher the level of education, the less time spent on leisure walking and walking for the trip, and the higher the personal income, the shorter the trip’s walking time.
Since the new urbanism and other planning-oriented approaches emerged in the 1990s, various empirical studies on the relationship between the physical environment and walking have been conducted, led mainly by American and other western researchers (Lee et al., 2014). Initially, researchers regarded walking as general pedestrian activities in analysis of its relationship with the physical environment (Lee and Ahn, 2007; Lee et al., 2014), but later some studies have widened its scope by categorizing the purpose of walking (Lee and Choi, 2018) or by segmenting the spatial types of walking (Lee et al., 2013).
This study examined the preceding literature, focusing on studies where the spatial scope was by Dongs. Lee, K. H., and Ahn, K. H. (2007) examine the association of neighborhood environment with walking times for 40 Dongs in Seoul, using Korea National Health and Nutrition Examination Survey data. They found that the land-use mix, multi-family row dwelling units, intersections per km, street connectivity, land gradient, and accessibility to parks and streams affect the walking time. Kim et al. (2014) examined the relations between the urban form and walking probability in Seoul. They consider more variables, including the location of neighborhood and regional attributes. The study shows that neighborhood and regional attributes affect the probability of walking, specifically population density, building coverage ratio, land use mix, street connectivity, intersection per km, land gradient, accessibility to the bus stop, and road ratio. Lee, K. H. et al. (2014) looked into the correlation between the neighborhood environment and pedestrian volume in 424 Dongs in Seoul using the National Household Travel Survey data and reported that the land use mix, intersections per km, and land gradient, were statistically significant variables. Lee, J. S., and Choi, H. M. (2018) investigated 423 Dongs in Seoul and reported that different physical environments of neighborhoods play a role according to the purpose of walking trips. Studies show that utilitarian walking is affected by road network density, population density, mix of business establishments, parks, accessible public sports facilities area. In contrast, leisure walking is related to accessibility to subway and bus stops, street connectivity, road network density, and parks’ floor area.
Findings from previous works can be summarized as follows:
First, the decline is a spatial phenomenon and results of multi-faceted interactions among social, economic, and physical factors, requiring empirical analysis and comprehensive interpretation to understand the urban decline phenomenon fully. Also, the decline can be measured using an individual index of urban decline or multiple decline indexes.3)
Meanwhile, the decisions on walking are made not only by the physical environment but also by socioeconomic characteristics. The physical and socioeconomic factors related to walking are different depending on the purpose of walking trips. Although some attempts to clarify the relation between walking and socioeconomic characteristics of a neighborhood in a somewhat limited scope, it appears insufficient to fully understand the relation between walking and decline only with such studies.
This study suggests different approaches from the primary research as follows:
First, we examined the relationship between decline and walking by extending the scope of variables to include urban decline factors, adding it to land use, urban form, and street environment, commonly employing physical environment factors in preceding research. Second, to understand the urban decline phenomenon’s complexity, we analyzed the urban decline factors by categorizing them into individual urban decline index, multiple decline index by sector, and comprehensive multiple decline index.
In this study, we construct analytical models using factors such as socio-economic characteristics of individuals (S), the physical environment of the neighborhood (N), and urban decline factors (D), taking note of the relations between the choices for walking by walking trip purpose (Wt,u,l) and decline.
Wt : walking trip generated
Wu : utilitarian walking trip generated
Wl : leisure walking trip generated
S : socioeconomic status at individual level
N : environmental qualities of neighborhood
D : urban decline factors
Socioeconomic characteristics of individuals and the neighborhood’s physical environment are crucial factors closely related to the choices for walking trips, as defined in other preceding studies.
We used the hierarchical linear models for analysis measures, enabling consideration of the individual and group levels simultaneously. It means that all observed values are included in all different levels, and therefore differences in all of those different levels should be controlled in quantitative analysis (Lee et al., 2014). In general, when calculating the hierarchically structured data by Least Squares Method (LSM), the assumptions to be complied by a regression model are disregarded. It will significantly reduce the standard error, making it highly likely that the null hypothesis is rejected (Lee and No, 2013). Besides, when using the hierarchical linear models, it is possible to analyze in detail at which level the explanatory variables show their effect and how significant such effect can be by level, based on variances by group, or “Random Effect”, as shown in the hierarchical data (Baek, 2018). More specifically, this study performed the analyses with the random intercept models4) the base model, and since the dependent variables are binary, we use the random intercept logit model. The random intercept logit model is a two-level model; the first level was the individual level and the second the neighborhood level (by dongs) in which the individual resides. We set the least significant difference level at 10% in analyses.
The model specification consists of total walking trips, utilitarian purpose walking trips, and leisure purpose walking trips, and each consists of six models in total (see Table 1). Model 1 is a basic model, which includes only constant terms. It is to see how the two-level variances change when a different explanatory variable is involved. Model 2 includes individuals’ socioeconomic characteristics (level 1), designed to identify whether the neighborhood’s physical environment has a statistically significant effect on walking trips by purpose, controlling individuals’ socioeconomic characteristics. Model 3 includes the neighborhood’s physical environment among the level 2 variables, aiming to see whether the urban decline factors are statistically significant for walking trips by purpose when the socioeconomic characteristics of individuals and the neighborhood’s physical environment are controlled in subsequent analyses. In Model 4 through Model 6, the individual urban decline index (Model 4), the multiple decline index by sector (Model 5), and the comprehensive multiple decline indexes (Model 6) were added for analyses to identify the relations between decline and walking trips by purpose.
Data used in this study are as follows:
First, data on walking trips by purpose was established by summing up the log of by-mode trips in the 2016 National Household Travel Survey5) data. For the decline phenomenon, the Eup-Myeon-Dong index6) in the measurement of urban decline was developed by KURC (2010). To develop the data sets, we used 2016 Seoul Public Data and 2016 GIS-based Building Integrated Information System data by the Ministry of Land, Infrastructure, and Transport (MOLIT), Republic of Korea. Table 2 shows the measurement of urban decline by KURC (2010). For the establishment of data regarding the physical environment of the neighborhood, we used 2016 Seoul Public Data, 2016 administrative district boundary information for the Statistical Geographic Information Service census, 2016 GIS-based Building Integrated Information System by the MOLIT, 2016 Road Name Addresses DB by the Ministry of the Interior and Safety (MOIS).
The walking trip occurrence by trip purpose as a dependent variable refers to “a walking trip by purpose by a resident within his/her residing dong for a day.” The purposes of walking trips were divided into walking trips for utilitarian purposes and those for leisure purposes, based on the National Household Travel Survey data classification. Due to limited data available, we define the walking trips for leisure purposes in this study as “A walking trip as a transport mode for leisure activities.”7)
The explanatory variables are composed of individuals’ socioeconomic characteristics (Level 1) and the neighborhood’s physical environment and urban decline factors (Level 2). Table 3 presents specific methods of measuring and descriptive statistics for each of the variables. For the socioeconomic characteristics of individuals (Level 1 variables), which are crucial factors in understanding the walking trip choices, we used the household status and the characteristics of the household members provided by the National Household Travel Survey data. To explain, the socioeconomic status of individuals in this study consists of Age, Sex, Income, Driver’s license, Car ownership, Housing type, and Occupation.
For the physical environment of the neighborhood, one of two Level 2 variables, the variables were classified into Land use, Urban Form, and accessibility based on preceding works in which the spatial scope was by dongs (Lee and Ahn, 2007; Kim et al., 2014; Lee et al., 2014; Lee and Choi, 2018). Land use meant the accessibility to a destination and was measured using variables such as population density, land use mix, and commercial FAR. For the population density, we calculated the population’s net density using the 2017 Urbanized Area data. The land use mix, which refers to the various land usage, was categorized into Residential, Commercial, Business, Industrial, and Other buildings and measured using the Entropy index.8) Floor areas of commercial buildings (FAR Commercial) are one of the walkability indices showing accessibility to commercial facilities. The higher the FAR Commercial becomes, the more commercial buildings concentrated in the space (Kang, 2013). Urban forms, referring to the connectivity to destinations and streets environment, were measured using intersections, average land gradient, and road surface ratio. We calculated the number of intersections by dividing the number of crossroads intersections by the surface area of urbanization. Simultaneously, the average land gradient was measured for the entire surface areas of dongs subject to this study, using the Digital Elevation Model (DEM) data. The road surface ratio was measured by comparing the actual-width roads’ surface area to the urbanized surface area. For accessibility, we used the number of parks, public sports facilities, bus stops, and the accessible area of subway stations. For the accessibility to parks, public sports facilities, and bus stops, we calculated by dividing the number of such facilities by the urbanized area. We calibrated the accessibility to subway stations by applying a 500m buffer area around each station to estimate the accessible area of the surface (Jeon and Park, 2020).
The urban decline measures, one of the level 2 variables, were classified into the individual urban decline index, multiple decline index by subject, and comprehensive multiple decline index. First, the individual urban decline index’s demography sector indicated the changes in urban population and demographic structure, socioeconomic growth potential, measured by using the annual average population growth rate, aging index, and ratio of the single elderly household. The annual average population growth rate is the mean value of the population growth rates between 2011 and 2016. The economic activity sector showed the size of the city’s economy and economic base, employment opportunities, and capital accumulation. The employees per 1,000 persons measured it, employees per business establishment, the ratio of employees engaged in manufacturing, employees in retail/wholesale businesses per 1,000 persons, and small-sized dwelling units. The physical environment sector represents the deterioration of the physical environment and residential attractiveness, measured by the ratio of an aged dwelling unit and the ratio of newly-constructed dwelling units. The number of aged dwelling units was obtained by dividing the total number of dwelling units by the number of the units built-in no later than 1985, while the number of newly-constructed dwelling units referred to those built-in 2000 and after that. Next, the individual index of urban decline was comprehensively put together to calculate the multiple decline index by sector (Demography, Economic activity, and Physical environment) and the comprehensive multiple decline index. For the multiple decline index, each index’s metric unit was converted into value Z for normalization, and to reflect the relative importance of the indices considered, each index was granted a weight based on the results of the factor analysis. A weighted linear combination method was applied to present the degree of urban decline by sector or comprehensive multiple decline index (KURC, 2010).
The total number of trips in Seoul used in this study was 181,330, with missing values removed, of which 105,580 (58%) were walking trips. As for the ratio of walking trips by purpose, 76,582 (72%) were walking trips for utilitarian purposes and 28,998 (28%) for leisure purposes. The respondents’ average age to the National Household Travel Survey data was 45.16 years old, with male 49.61%, driver’s license holder 59.75%, vehicle owner 69.18%, and residents in apartment 52.35%. Regarding occupations, 30.69% were professional/ office workers, 23.72% in service/sales, 6.42% manual labor, 13.05% housewives, and 26.12% student/unemployed.
As for the average value of the physical and environmental factors of 423 dongs in Seoul, the population density was 306.58 persons/ha, the land use mix 0.57, and the commercial FAR 172%. The number of intersections was 97.02/km2, the average land gradient 5.78%, and the road surface ratio 22%. The number of urban parks was 3.69/km2, and the number of public sports facilities was 0.56/km2. The number of bus stops was 24.05/km2 and accessibility to subway station 0.45, indicating that about half of the urbanized area appeared to locate in the primary station influence area (SIA). For the average value of the urban decline factors, the average annual population growth rate was -0.01%, the aging index 87.05%, the ratio of single elderly households 7.87%. The number of employees per 1,000 persons was 765.88, the number of employees per business 5.43, the ratio of employees in the manufacturing industry 6.12%, and the number of employees in the retail/wholesale business per 1000 persons were 255.90. The ratio of small-sized dwelling units was 73.57%, the ratio of aged dwelling units 67.75%, and the ratio of newly-built dwelling units 4.98%. Finally, the multiple decline index in the demography sector was 5.94 while that of the economic activity and physical environment sectors was 47.36 and 16.94, respectively. The comprehensive multiple decline index was 70.23.
The results of model 1~model 3 are presented in Table 4. Model 1 shows the results of walking trips, utilitarian walking trips, and leisure walking trips models only with constant terms. All three models show that the level 2 variance bears
statistical significance, indicating a difference in choices for walking trips by purpose among dongs (Model 1-t, Model 1-u, Model 1-l).
The results of Model 2 represent the individual’s socioeconomic characteristics (level 1 variable), added to Model 1. The level 2 variance decreased in all analysis models, indicating that individuals’ socioeconomic characteristics account for the difference in purpose-specific walking trip choice by dong. Also, it showed that the Alsos’ socioeconomic characteristics accounted for 20%, 1%, and 10% of the level 2 variance through PRE by analysis model. In particular, in utilitarian-purpose walking trips, the PRE was lower than that of other-purpose trips (Model 2-t, Model 2-u, Model 2-l).
Model 3 is a model in which the neighborhood’s physical environment, one of the level 2 variables, is added to Model 2. The PRE of total walking trips was 0.0826, and that of utilitarian and leisure walking trips was 0.0498 and 0.0651, respectively, accounting for 8%, 5%, and 7% of the level 2 variance, respectively (Model 3- t, model 3-u, model 3-l).
Models 1 to 3 show that the level 2 variance of Model 1 was statistically significant,9) and the explanatory variables of Models 2 and 3 were found to reduce the level 2 variance, indicating that it is appropriate to use the hierarchical linear model in this study. The multicollinearity tests suggest that the Variance Inflation Factor (VIF) maximum value was 5.65, which did not exceed 10, and the tolerance (1/VIF) was more significant than 0.1, confirming that there was no multicollinearity issue to be considered.
The results of model 4~model 6 are shown in Table 5. We the individual urban decline index, multiple decline indexes by sector, and comprehensive multiple decline index in the total walking trips model (Model 3-t) for analyses. The results showed that the level 2 variance decreased in all models, and the corresponding PRE in Models 1, 2, and 3 was 0.0550, 0.0179, and 0.0076, respectively. Concerning the relations between urban decline factors and choices in total walking trips, we found that more chose walking trips when the number of employees per 1,000 persons was large, and the ratio of employees in manufacturing and the number of wholesale and retail employees per 1,000 persons were small. In other words, when the number of employees per 1,000 persons is large, the employees are highly likely to choose walking trips due to the mobility of workers (Lee et al., 2013). Besides, when the proportion of employees in the manufacturing industry and the number of wholesale and retail sector employees per 1,000 persons were low, more people seemed to opt for walking trips. Since the ratio of employees in manufacturing and wholesale/retail sectors per 1,000 persons can translate into the region’s economic base and scale, it implies that more people tend to choose walking trips where the economic situation is relatively unfavorable. These results correspond to the results of a study showing that residents living in socio-economically disadvantaged areas are more likely to choose walking (Turrell et al., 2013) and are in line with other preceding studies reporting that the lower the household income level, the more walking trips are chosen (Lee et al., 2014; Kim et al., 2014). Next, among the comprehensive decline indices by sector, only the economic activity sector appeared to bear statistical significance, which is consistent with the results of a previous study that income is a significant factor among the socio-economic characteristics associated with walking (Sallis et al., 2009). In more detail, many chose walking trips when the comprehensive decline index in the economic activity sector was high, indicating that walking trips take place frequently where economic decline is evident. Finally, it shows that the comprehensive multiple decline indices were not statistically significant (see Model 4-t, Model 5-t, Model 6-t).
Including the individual urban decline index, multiple decline index by sector, and comprehensive multiple decline index to the utilitarian walking trips model (Model 3-u), it was found that the decline phenomenon was serving as an explanatory variable for the difference in the level of choice for utilitarian walking trips by dong. PRE for individual urban decline index, multiple decline index by sector, and comprehensive multiple decline index was 0.0498, 0.0309, and 0.0251, respectively, while its effect size on the level 2 variances was 5%, 3%, and 3%, respectively. Looking at the relations between the urban decline factors and utilitarian walking trips, only the ratio of small-sized dwelling units among the individual-level index showed statistically significant results. Specifically, we found few choices for utilitarian walking trips made when the ratio of small-sized dwelling units was high. Since the ratio of small-sized dwelling units is an indicator to measure the region’s income level indirectly, a higher ratio of small-scale dwelling units means the region’s level of income should be low. Therefore, the lower the neighborhood’s income level, the smaller the number of walking trips is. Next, we found few choices made for utilitarian walking trips when the multiple decline index in the economic activity sector was high. It means that few people would opt for utilitarian walking trips when the region’s economic activities have declined. This result is contrary to a preceding study that considers utilitarian walking as an inferior good, and hence the higher the income level, the less walking for utilitarian purposes (Agrawal and Schimek 2007; Sung et al., 2015).
On the other hand, Plaut (2005) reported that the higher the income level in densely-populated regions, the more utilitarian walking trips tended to be selected. Cerin et al. (2009) found that when controlling accessibility to destinations, more chose the utilitarian walking trips in areas with high-income levels. In particular, Seoul is a highly dense and mixed city compared to Western cities, and therefore these conflicting results can be understood as attributable to Seoul’s urban context. Lastly, we found that utilitarian-purpose walking trips were low when the comprehensive multiple decline index was high, indicating that the probability of choosing utilitarian-purpose walking trips in a declined city is low (see Model 4-u, Model 5-u, and Model 6-u).
Regarding the relation between walking trips for leisure purposes and the decline phenomenon, the difference in level for each dong in terms of choices for leisure-purpose walking trips turned out to be explained by 14%, 9%, and 4% by the individual urban decline index, the multiple decline index by sector, and the comprehensive multiple decline index, respectively. In particular, we found that the PRE of the individual index and the by-sector multiple decline index is higher than that of the physical environment of the neighborhood, indicating that the individual urban decline index and the multiple decline index by sector were essential factors in walking trip choices for leisure purposes by dong. In the individual urban decline indices, the aging index, the number of employees per 1,000 persons, the ratio of employees in the manufacturing sector, the number of employees in the wholesale and retail business per 1,000 persons, and the ratio of small-sized dwelling units are statistically significant variables. In detail, we found that walking trips for leisure purposes were high when the aging index was high; this is probably because, for the elderly, most leisure activities occur on foot within their neighborhood (Kim, 2012). Meanwhile, more chose leisure-purpose walking trips if the number of employees per 1,000 persons was significant, which indicates that more tend to opt for walking trips for leisure when the mobility of employees within the region is more significant. Also, the higher the ratio of employees in the manufacturing sector and the number of employees in the wholesale and retail business per 1,000 persons and the lower the ratio of small-sized dwelling units, the lower the likelihood of choosing walking trips for leisure purposes. Since the ratio of employees in manufacturing, the number of employees in the wholesale and retail business per 1,000 persons, as well as the ratio of small-sized dwelling units, show the income level of the neighborhood, the lower the income level of the neighborhood is, the higher leisure-purpose walking trips occur. These results are contrary to a study that considered leisure-purpose walking like a normal good, reporting that the higher the income level, the more leisure walking trips were made (Agrawal and Schimek 2007). However, one study that analyzed leisure walking trips in Seoul showed that in the case of “non-producers” such as housewives, the unemployed, and students, leisure activities were done within the range of their living area around their place of residence (Lee and Choi, 2020).
Furthermore, in Korea, the higher the income level, the more physical activity tends to occur in indoor facilities such as fitness centers or yoga training centers rather than in the form of walking (Ministry of Culture, Sports and Tourism, 2014). Therefore, these conflicting results appear to be attributable to the cultural differences of each nation. The higher the multiple decline indexes in the economic activities sector, the more walking trips for leisure purposes occur. It implies that leisure-purpose walking trips are relatively prevalent in a city with poor economic activities. Lastly, we found that leisure-purpose walking trips were also high when the comprehensive multiple decline index was high, indicating that the probability of choosing leisure-purpose walking trips in a declined city is high (see Model 4-I, Model 5-I, and Model 6-I).
Jane Jacobs (1961), taking note of the phenomenon of decline in 1961, defined the innate feature of slums as a “lack of lively urban life” and proposed to create lively streets where people can fully and safely enjoy walking as a basis for tackling slums. It may not be coincidental that after about 60 years have passed, walking is emerging as a solution to our cities’ problem again after going through rapid industrialization and urbanization. If Korea’s urban decline refers to the state of a city with no vitality or is losing it, walking is the most potent means of injecting vitality to the city and evaluating the level of its vitality. Promoting walking activities is regarded as a critical strategy in urban regeneration policies to solve the urban decline issue. In line with this, pedestrian projects have actively promoted revitalizing walking within the city since enacting the Special Act on the Promotion and Support of Urban Regeneration in 2013. However, the urban decline in Korea has been worsening over time, leaving us with room for thought on whether the complex interactions between the social/spatial factors and the decline problem have accelerated a vicious downward cycle in our society. Against this backdrop, this study has analyzed the relations between the purpose-specific walking trips and individual urban decline index, multiple decline index by sector, and comprehensive multiple decline index on 181,330 walking trips surveyed in 423 dongs in Seoul to understand the correlation between the decline phenomenon and people’s choices for walking trips.
The results can be summarized as follows:
First, we found that the decline phenomenon accounted for the difference in the choices of walking trips by purpose among the subject dongs, even after controlling the socio-economic characteristics of individuals and the neighborhood’s physical environment. Such results indicate that urban decline and walking trip choices are correlated. Regarding the relation between decline and walking trip choices, different relations were observed by the declining sector and walking purpose.
Second, it was found in the comprehensive walking trips analysis model that the number of employee per 1,000 persons, the ratio of employees in the manufacturing sector, the number of employees in the wholesale and retail business per 1,000 persons, and the multiple decline index in economic activities sector were relevant factors in walking trip choices. Nevertheless, the ratio of employees in the manufacturing sector, the number of employees in the wholesale and retail business per 1,000 persons, and the multiple decline indexes in the economic activities sector, excluding the number of employees per 1,000 persons, showed conflicting results preceding studies. Therefore, neglecting these aspects in understanding the relationship between decline and walking trips may lead to unreasonable observations.
Third, different purposes for walking showed different results in urban decline factors related to walking trip choices. While few chose utilitarian walking trips in a declined city, more tended to opt for walking trips for leisure purposes. The results corresponded to previous studies (Lee et al. 2013, Lee and Choi, 2020), indicating that employees’ mobility contracted when socio-economic factors became worse and that most of the leisure activities occurred within the range of one’s living area. Nonetheless, we need additional empirical studies on this aspect to understand this aspect clearly. We also found that walking trips for leisure purposes were still brisk in a declining city. Unlike in the West, Korean cities, including Seoul, maintain a somewhat healthy social environment for pedestrian safety, despite the urban decline problem (Kim, 2010).
Fourth, the economic activities sector was most closely related to walking trips among the declining sector. It means the income level serves as a crucial factor in choices for purpose-specific walking trips. It is consistent with the results of a previous study that reported that income is a significant factor among walking’s socio-economic characteristics (Sallis et al., 2009).
Fifth, the effect size of the explanatory variables on the neighborhood level variance was measured using PRE. Regarding the walking trips for utilitarian purposes, the PRE of the individual’s socio-economic characteristic was 0.0131, which was lower than PRE 0.1005 for leisurely walking trips. The walking trips for utilitarian purposes are not a choice but an essential activity, indicating that individuals’ socio-economic characteristics appear to be less affected by the neighborhood. In walking for leisure purposes, the PRE of the individual urban decline index was 0.1435, accounting for the neighborhood level’s variance by 14%. This value is higher than that of the neighborhood’s physical environment, indicating that the individual urban decline indices can explain the variance of neighborhood-level in terms of the choices of walking trips for leisure purposes.
Based on the results of this study, the following implications can be drawn:
It is necessary to reflect the decline in demography and economic activities sectors when establishing urban regeneration plans or improving the pedestrian environment through urban regeneration projects. We need to consider the purposes of walking should as well, and since the leisure activities in a declined city tend to take place within the neighborhood around one’s place of residence, thorough planning and strategies on leisure purpose walking should be established.
This study is meaningful because it has empirically identified the relationship between the decline phenomenon and walking trips for different purposes, which has not been dealt with in the preceding domestic studies. Nevertheless, it still has some limitations, which require proper follow-up studies in the future. First, this study was a cross-sectional study, and no analysis was conducted on a before-and-after comparison. Therefore, we hope that a study in which the changes in urban decline phenomenon and walking trips before and after the urban regeneration projects are measured will follow so that the causality between the two variables can be adequately analyzed. Second, as for interpreting the effect sizes of explanatory variables for walking trip choices for different purposes into PRE values, the relevant empirical studies carried out thus far were still insufficient, making it hard to build a respectable theory out of it. Therefore, more empirical studies using PRE should be conducted as well. Finally, it is difficult to generalize this study’s results, the spatial subject of Seoul and its administrative districts (“dong”), since the declining level may differ, depending on the type of city. Therefore, we hope that more studies will be conducted on diverse types of cities in the future.
This article is created by supplementing and modifying the lead author’s Master’s degree thesis in the year of 2020.
|1.||Agrawal, A.W. and Schimek, P., 2007. “Extent and Correlates of Walking in the USA”, Transportation Research Part D: Transport and Environment, 12(8): 548-563.
|2.||Baek, Y.M., 2018. Hierarchical Linear Model Using R, Seoul: Hannarae Publishing Co.
백영민, 2018. 「R을 이용한 다층모형」, 서울: 한나래출판사.
|3.||Cerin, E., Leslie, E., and Owen, N., 2009. “Explaining Socio-Economic Status Differences in Walking for Transport: An Ecological Analysis of Individual, Social and Environmental Factors”, Social Science and Medicine, 68(6): 1013-1020.
|4.||Chae, H.W., 2013. “Exploring Urban Spatial Patterns in Seoul: Using Synthetic Deprivation Index by AHP”, Journal of the Association of Korean Geographers, 2(2): 147-163.
채희원, 2013. “AHP기법을 활용한 서울시 내부의 종합쇠퇴지수 산출 및 도시쇠퇴 공간패턴 탐색”, 「한국지리학회지」, 2(2): 147-163.
|5.||Choi, C,G., Seong, H.G., Lee, S.G., Kim, T.H., Go, J.H., and Won, B.W., 2013. “Developing Walking Activity for Sustainable City”, Urban Information Service. April Issue: 3-22.
최창규·성현곤·이수기·김태현·고준호·원보환, 2013. “지속가능한 도시를 위한 보행활동 증진방안”, 「도시정보」, 4월호: 3-22.
|6.||Jane, Jacobs, 2010. The Death and Life of Great American City, Translated by Yoo K.E., Seoul: Greenbee.
Jane, Jacobs, 2010. 「미국 대도시의 죽음과 삶」, 유강은 역, 서울: 그린비.
|7.||Jeon, J.H. and Park, J.A., 2020. “Moderating Effect of Pedestrian Environment on neighborhood Satisfaction–Focused on the ‘Seoul Housing Survey’ 2017”, Journal of Korea Planning Association, 55(3): 29-42.
전준형·박진아, 2020. “보행환경이 근린환경 만족도에 미치는 조절효과 분석–서울시 주거실태조사 2017 자료를 중심으로”, 「국토계획」, 55(3): 29-42.
|8.||Jo, J.H., Lee, Y.S., Jung, H.Y., and Kwak, T.W., 2014. “Does a Health of People Living in Declined Regions also Decline?”, Journal of Korea Planning Association, 49(6): 109-125.
조준혁·이영성·정해영·곽태우, 2014. “쇠퇴한 지역에 사는 사람들은 건강도 쇠퇴할까?” 「국토계획」, 49(6): 109-125.
|9.||Kang, C.D., 2013. “Measuring Walkability Index and Its Policy Implications in Seoul, Korea”, Seoul Studies, 14(4): 1-25.
강창덕, 2013. “서울시 보행 편의성 지수 측정과 정책과제”, 「서울도시연구」, 14(4): 1-25.
|10.||Kim, H.C., Ahn, K.H., and Kwon, Y.S., 2014. “The Effects of Residential Environmental Factors on Personal Walking Probability-Focused on Seoul”, Journal of The Urban Design Institute of Korea, 15(3): 5-18.
김희철·안건혁·권영상, 2014. “개인의 보행확률에 영향을 미치는 거주지 환경요인–서울시 행정동을 대상으로”, 「한국도시설계학회지」, 15(3): 5-18.
|11.||Kim, K.J., 2010. “Causes and Consequences of Urban Decline in Korean Cities”, Journal of the Korean Urban Geographical Society, 13(2): 43-58.
김광중, 2010. “한국 도시쇠퇴의 원인과 특성”, 「한국도시지리학회지」, 13(2): 43-58.
|12.||Kim, Y.J., 2012. “Analysis on the Elderly’s Catchment Area of Neighborhood Facilities”, Journal of the Architectural Institute of Korea Planning & Design, 28(5): 215-222.
김용진, 2012. “노인의 주요 근린시설별 이용권 도출에 관한 연구”, 「대한건축학회논문집 계획계」, 28(5): 215-222.
|13.||Ko, E.J., 2014. “Effect of Neighborhood Environments on Social Diversity and Residents’ Social Capital: A Case Study of Seoul”, Ph.D. Dissertation, Seoul National University.
고은정, 2014. “근린환경이 사회적 다양성과 동네 사회자본 형성에 미치는 영향–서울시를 대상으로”, 서울대학교 대학원 박사학위 논문.
|14.||Korea Research Institute for Human Settlements, 2019. “Korea Decline Area”, The Korea Spatial, July Issue: 113.
국토연구원, 2019. “전국 쇠퇴지역 현황”, 「국토」, 7월호: 113.
|15.||Korea Urban Renaissance Center, 2010. Development of Urban Decline and Potential Index, Gyeonggi.
도시재생사업단, 2010. 「도시쇠퇴 및 잠재력 진단지표 개발」, 경기.
|16.||Kwak, H.K., 2003. “A study Influential Factors on Neighborhood-related Social Capital”, Korea Society and Public Administration, 14(4): 259-285.
곽현근, 2003. “동네관련 사회자본의 영향요인에 관한 연구”, 「한국사회와 행정연구」, 14(4): 259-285.
|17.||Lee, H.Y. and No, S.C., 2013. Advanced Statistical Analysis Theory and Practice 2nd Edition, Gyeonggi: Moonwoosa.
이희연·노승철, 2013. 「고급통계분석론 이론과 실습 제2판」, 경기: 문우사.
|18.||Lee, H.Y., Shim, J.H., and No, S.C., 2010. “The Features of the Intra-urban Decline and Its Spatial Pattern in Korean Cities”, Journal of the Korean Urban Geographical Society, 13(2): 13-26.
이희연·심재헌,노승철, 2010. “도시 내부의 쇠퇴실태와 공간패턴”. 「한국도시지리학회지」, 13(2): 13-26.
|19.||Lee, J.S. and Choi, H.M., 2018. “Comparison of Utilitarian and Recreational Walking of 423 Administrative District in Seoul-Rethinking Walkability as a Critical Factor for Urban Regeneration”, Journal of The Korean Urban Management Association, 31(1): 41-62.
이종선·최혜민, 2018. “서울시 근린환경과 목적별 보행 비교연구-도시재생을 위한 보행의 함의에 대한 고찰”, 「도시행정학보」, 31(1): 41-62.
|20.||Lee, K.H. and Ahn, K.H., 2007. “The Correlation between Neighborhood Characteristics and Walking of Residents-A Case study of 40 Areas in Seoul”, Journal of Korea Planning Association, 42(6): 105-118.
이경환·안건혁, 2007. “커뮤니티의 물리적 환경이 지역 주민의 보행 시간에 미치는 영향–서울시 40개 행정동을 대상으로”, 「국토계획」, 42(6): 105-118.
|21.||Lee, K.H., Kim, T.H., Lee, W.M., and Kim, E.J., 2014. “A Study on Effects of Neighborhood’s Environments on Residents’ Walking Trips Using Household Travel Diary Survey Data in Seoul”, Seoul Studies, 15(3): 95-109.
이경환·김태환·이우민·김은정 2014. “가구통행실태조사 자료를 이용한 근린환경과 보행통행의 상관관계 연구”, 「서울도시연구」, 15(3): 95-109.
|22.||Lee, N.H. and Choi, C.G., 2020. “A Study on the Influence Factors of Weekday Leisure Travel”, Journal of Korea Planning Association, 55(2): 91-100.
이남휘·최창규, 2020. “유형별 주중 여가통행의 영향요인에 관한 연구”, 「국토계획」, 55(2): 91-100.
|23.||Lee, S.H. and Jung, S.M., 2019. Effects of “Walking City, Seoul” Policy and Directions for Future Researches, Seoul: Seoul Development Institute.
이신해·정상미, 2019. 「‘걷는 도시, 서울’ 정책효과와 향후 정책방향」, 서울: 서울연구원.
|24.||Lee, Y.S., Choo, S.H., and Kang, J.M., 2013. “Analysing Key Factors to Affect Change of Pedestrian Volumes by Neighborhood Units in Seoul”, Journal of Korea Planning Association, 48(5): 197-208.
이연수·추상호·강준모, 2013. “서울시 생활권별 보행량 변화에 미치는 요인 분석”, 「국토계획」, 48(5): 197-208.
|25.||Ministry of Culture, Sports and Tourism, 2014. Long-Range Plan on Balancing Location of Public Sport Facilities, Seoul.
문화체육관광부, 2014. 「공공체육시설 균형배치 중장기계획」, 서울.
|26.||Park, N.Y. and Park, B.H., 2017. “Regional Traffic Accident Model of Elderly Drivers based on Urban Decline Index”, Journal of the Korean Society of Safety, 32(6): 137-142.
박나영·박병호, 2017. “도시쇠퇴 지표를 적용한 지역별 고령운전자 교통사고 영향 분석”, 「한국안전학회지」, 32(6): 137-142.
|27.||Plaut, P.O., 2005. “Non-motorized Commuting in the US”, Transportation Research Part D: Transport and Environment, 10(5): 347-356.
|28.||Rhim, J.H., 2006. “The Land Use Characteristics of Rail Transit Station Area Influencing Transit Demand: A Case Study of Seoul”, Ph.D. Dissertation, Seoul National University.
임주호, 2006. “도시철도 이용수요에 영향을 미치는 역세권 토지이용특성: 서울시 사례연구”, 서울대학교 대학원 박사학위논문.
|29.||Sa, K.E. and Lee, S.G., 2018. “An Analysis of Neighborhood’s Environmental Factors Affecting Residents’ Daily Walking and Exercise Walking”, Journal of The Urban Design Institute of Korea, 19(3): 71-90.
사경은·이수기, 2018. “주거지 기반 근린환경 특성이 일상생활 보행과 운동목적 보행에 미치는 영향 분석–2013 서울시민 신체활동 조사 자료를 중심으로”, 「한국도시설계학회지」, 19(3): 71-90.
|30.||Sallis, J.F., Saelens, B.E., Frank, L.D., Conway, T.L., Slymen, D.J., Cain, K.L., Chapman, J.L., and Kerr, J., 2009. “Neighborhood Built Environment and Income: Examining Multiple Health Outcomes”, Social Science and Medicine, 68(7): 1285-1293.
|31.||Sung, H.G., Lee, M.H., and Seong, T.Y., 2015. “Difference in the Determinants Factors of Walking Activity as the Purposes of Recreation and Travel”, Journal of Korea Planning Association, 50(5): 73-86.
성현곤·이만형·성태영, 2015. “여가와 통행 목적으로서의 보행활동 결정요인의 차이–개인 및 가구수준 특성을 중심으로”, 「국토계획」, 50(5): 73-86.
|32.||Turrell, G., Haynes, M., Wilson, LA., and Giles-Corti, B., 2013. “Can the Built Environment Reduce Health Inequalities? A Study of Neighbourhood Socioeconomic Disadvantage and Walking for Transport”, Health and Place, 19: 89-98.