Economic impacts of a linear urban park on local businesses: The case of Gyeongui Line Forest Park in Seoul

Landscape and Urban Planning 181 (2019) 139–147

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Landscape and Urban Planning journal homepage: www.elsevier.com/locate/landurbplan

Research Paper

Economic impacts of a linear urban park on local businesses: The case of Gyeongui Line Forest Park in Seoul Juhyeon Park, Jeongseob Kim

T

⁎

School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea

A R T I C LE I N FO

A B S T R A C T

Keywords: Economic impacts Actual sales data Revitalization Urban regeneration Linear parks

This study aims to explore the economic impacts of urban open spaces using the sales data of local small businesses. A new urban park could attract more visitors and lead to neighborhood revitalization, especially in distressed neighborhoods. In order to explore this mechanism, this study analyzes the case of the Gyeongui Line Forest Park, a previously underutilized railroad that was converted to a linear urban park. A difference-indifference approach was applied to evaluate the change in the sales of local businesses before and after the park’s opening using credit card and cash sales data provided by the Seoul Metropolitan Government’s Big Data Campus. The results showed that urban linear parks could have positive effects that lead to the neighborhoods’ economic vitality. However, the economic impacts could vary depending on neighborhood contexts. Specifically, economically distressed neighborhoods could benefit more from the opening of a park. This study directly captured the revitalization impact of the Gyeongui Line Forest Park using actual sales data instead of analyzing indirect outcomes such as property values, thereby providing an alternative approach to measure the economic impacts of parks.

1. Introduction An urban park or open space provides amenities and recreational opportunities, thereby contributing to public health by reducing people’s stress and promoting their physical activities (Alcock, White, Wheeler, Fleming, & Depledge, 2014; Cohen et al., 2007; Konijnendijk, Annerstedt, Nielsen, & Maruthaveeran, 2013; Scottish Government, 2007; Thompson et al., 2012). Urban open spaces also improve the quality of the urban environment by reducing air pollution, preventing excessive run-offs, and having a cooling effect on the surrounding areas (Bolund & Hunhammar, 1999; Bowler, Buyung-Ali, Knight, & Pullin, 2010; Heidt & Neef, 2008; Knight, Price, Bowler, & King, 2016; Zupancic, Westmacott, & Bulthuis, 2015). Understanding the role of urban open spaces is essential for effective landscape and urban planning practice, but little is known about the effects of urban open spaces on neighborhoods, especially in the context of business districts. Many hedonic studies have explored the neighborhood effects of urban open spaces and revealed that their positive or negative effects capitalized into property values (Hackett & Dissanayake, 2014; Latinopoulos, Mallios, & Latinopoulos, 2016; Tyrväinen & Väänänen, 1998; Yang & Choei, 2003). For instance, Levere (2014) analyzed the influence of open spaces on the property values of areas surrounding the High Line in New York and found that the rent premium was higher ⁎

by 10–18% in the apartment complexes located within a one-third mile distance from the park following its opening. Jim and Chen (2006) revealed that the existence of urban parks increased house values by 14.93% and a view of a park contributed an additional 1.95% housing value premium in Hong Kong. These hedonic studies are meaningful in measuring the economic value of urban open spaces. However, they often focus on the parks’ effects on residential rather than commercial aspects. Additionally, the results simply reveal the relationship between urban open spaces and nearby property values instead of directly explaining the mechanism by which the externalities of urban open spaces are capitalized into property values. Some studies explore the role of urban parks in promoting neighborhood revitalization (Ganser, 2017; Klenosky, Snyder, Vogt, & Campbell, 2017; Ernst and Young, 2003). Beautiful urban parks are considered engines that attract visitors from surrounding areas and allow for engaging activities to take place within the parks. For example, Bryant Park in New York City had been a derelict place known for its high crime rate in the 1980s. However, after the renovation of the park with desirable amenities in 1991, it became a popular place, attracting approximately 20,000 daily visitors. Ernst and Young (2003) revealed that the rent for commercial offices near Bryant Park increased by 115–225% while other offices in the control area recorded a 44–73% hike in rent. The High Line in New York is one of the most well-known

Corresponding author. E-mail address: [email protected] (J. Kim).

https://doi.org/10.1016/j.landurbplan.2018.10.001 Received 10 October 2017; Received in revised form 1 August 2018; Accepted 1 October 2018 0169-2046/ © 2018 Elsevier B.V. All rights reserved.

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J. Park, J. Kim

environment of the park attracts more visitors to the neighborhood, the gross sales of local businesses could increase (Konijnendijk et al., 2013; Sherer, 2006). The enhanced business environment then brings about an increase in the openings of new small retail businesses, which in turn converts the area from a depressed to a competitive commercial district. The increase in the number of visitors and subsequent demand for new local businesses are reflected in property values as increased rent. Eventually, increased property values enhance the tax base of the neighborhood and the growth of local small businesses leads to more job creation in the neighborhood revitalization process, all of which was triggered by the opening of the park (Chandralal, 2010; Mika, Zawilinska, & Pawlusinski, 2016; Sutton, 2010). In order to explore the mechanism by which a new urban park revitalizes the neighborhood, this study analyzed the case of the Gyeongui Line Forest Park in Seoul. The current park area was previously an underutilized railroad that depressed the growth of the surrounding neighborhoods. However, the railroad was reconstructed as an underground subway line and the ground was converted to a beautiful, linear urban park in June 2015. The Gyeongui Line Forest Park is located near the Hongik University commercial zone, which is one of the most popular commercial districts in Seoul, having been commercialized since 2000. After the opening of the Gyeongui Line Forest Park in 2015, the surrounding neighborhoods have become popular, attracting visitors from the nearby commercial zone. The park is now named “Yeontral Park” and many young Koreans compare it to Central Park in New York City. This park is thus a perfect case to explore the neighborhood effects of a new park in the context of a business district. This study is different from existing literature in several aspects. As described earlier, this research focuses on the neighborhood effects of opening a new park in the context of commercial rather than residential areas. Additionally, it addresses the intermediate process that makes connections between urban parks’ externalities and property value premiums with an emphasis on local business openings and sales. To account for the economic impacts of the park, this study applies a difference-in-difference (DID) approach using the sales data of local businesses from 2014 to 2016, which allows a comparison of the effects of the Gyeongui Line Forest Park on the neighborhood before and after the its opening, thus providing more robust empirical evidence. Finally, this study uses actual sales information to analyze the economic impacts of parks on the neighborhood.

cases that reflect the neighborhood effects of urban open spaces. Built on an old railroad track, it became an extraordinary attraction, with over 7.6 million visitors in 2015, 67% of whom came from outside New York (Ganser, 2017). According to Levere (2014)’s assessment, New York City gained $100 million from property tax increments in 2010 with the remarkable spread of art, entertainment, and recreation establishments after the opening of the park. A linear urban park may have greater potential for revitalizing neighborhoods because its long and narrow shape can penetrate the urban fabric, providing greater access to green spaces. The linear park could promote the benefits of the green spaces more effectively than a square or rectangular park by improving the neighborhood environment and encouraging more physical and recreational activities (Brown, Schebella, & Weber, 2014; Gusteler, López, & Faggi, 2017; Marcus & Francis, 1997; Molnar, 2015). Therefore, linear green spaces that have been transformed from industrial-era infrastructure have become popular as a tool for the revitalization of deprived areas in the past few decades (Kullmann, 2011). Many scholars have reported that the land and property values of surrounding neighborhoods recorded an increase owing to the presence of linear parks, which were converted into lively green spaces from abandoned infrastructure. Examples of these include the High Line in New York City (Levere, 2014), Atlanta’s BeltLine (Immergluck, 2009; Immergluck & Balan, 2018; Weber, Boley, Palardy, & Gaither, 2017), Boston’s Big Dig project (Tajima, 2003), Chicago’s the 606 (Smith, Duda, Lee, & Thompson, 2016), and Seoul’s Gyeongui Line Forest Park (Jung, Choi, & Yoon, 2016; Kwon, Joo, Han, & Park, 2017). However, these studies are limited in reflecting longitudinal changes before and after the opening of the parks in commercial neighborhood contexts and have not addressed the actual economic activities of the parks’ surrounding neighborhoods. How do urban open spaces increase nearby property values in commercial areas? What is the role of an urban open space in the urban regeneration process? Starting from these questions, this study aims to explore the neighborhood effects of urban open spaces based on the sales data of local small businesses. Conceptually, a new urban park could attract more visitors and lead to neighborhood revitalization, especially in distressed neighborhoods (Konijnendijk et al., 2013; Ramlee, Omar, Yunus, & Samadi, 2015). As shown in Fig. 1, the opening of a new park in a disadvantaged neighborhood involves changing the built environment of the park site from being abandoned, underutilized, or disliked to being desirable and pleasant. The effects of new urban parks could be much larger in the case of linear urban parks because they provide greater accessibility from surrounding neighborhoods (Brown et al., 2014; Gusteler et al., 2017). As the improved

2. Methodology 2.1. Case: The Gyeongui Line Forest park The Gyeongui Line Forest Park is a 6.3 km-long linear park in Seoul. It was originally a railroad built in 1996, which connected Seoul with Sinuiju (the northern border of North Korea). After the division of Korea, it served the purpose of transporting passengers and goods between downtown Seoul and the northern part of the Seoul metropolitan area. The railroad had been underutilized until the 2000s when new plans to convert the railways to urban parks were initiated and implemented. The project removed existing railways and constructed a new underground railway system; the ground-level railroad was then converted to a linear urban park with a width of 10–60 m. The Gyeongui Line Forest Park was completed in 2016 following the completion of the three phases as shown in Fig. 2. The opening of this park attracted many visitors and accelerated the revitalization of nearby neighborhoods. Recent studies show that the park’s opening led to an increase in residential property values and commercial rent (Jung et al., 2016; Kim, 2016). It also resulted in the change of land use from residential to commercial purposes, consequently leading to the growth of the number of retail businesses (Lee, 2017; Won, 2017). Some researchers consider this rapid neighborhood change triggered by the Gyeongui Line Forest Park as gentrification

Fig. 1. The revitalization process from the park’s designation. 140

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Fig. 2. Before (left) and after (right) the completion of the Gyeongui Line Forest Park project. Adapted from Press Release, In Seoul Metropolitan Government (2013). Retrieved July 30, 2018, from http://opengov.seoul.go.kr/press/407154.

(Kwon et al., 2017). Considering the unique characteristics and its neighborhood effects, the Gyeongui Line Forest Park project provides a valuable case for commercial revitalization. Importantly, before the designation of the Gyeongui Line Forest Park, the neighborhood had been separated by the railroad for a long time even though the areas were adjacent to one another. The western neighborhood was commercialized, influenced by the Hongdae commercial area, one of the major commercial districts since the opening of Hongik University’s College of Fine Arts and a subway station in the 1980s, and the area was a center of indie culture. The commercialization intensified and expanded to a nearby neighborhood (the west side of the Gyeongui Line) after the opening of a new subway station in 2010. However, the abandoned railroad had blocked the expansion phenomenon from progressing into the eastern neighborhood, which mostly comprised low-rise residential buildings and was a relatively unknown place, especially among tourists. The proportion of commercial and mixed use of land was also relatively low in the eastern region (Table 1). Owing to these reasons, it was expected that these two neighborhoods would be differently influenced by the Gyeongui Line Forest Park. These interesting backgrounds will be covered in this study.

Table 1 Characteristics of neighborhoods (2015). Characteristics

Old buildingsa Residential useb Commercial useb Mixed useb

Region

Walking distance (in meters)

EAST WEST EAST WEST EAST WEST EAST WEST

< 400

400–800

800–1200

43.2% 31.9% 69.1% 58.8% 17.4% 17.0% 13.5% 24.2%

36.2% 29.4% 79.2% 56.0% 14.4% 26.4% 6.3% 17.6%

34.9% 28.7% 75.8% 68.0% 18.4% 18.6% 5.8% 13.4%

a

The number of buildings over 30 years old are counted. Three types of land use (residential, commercial, and mixed use) are used for calculating the proportion. b

model was described as follows:

ln (SALESit ) orln (SALEPBit ) = α + β1 DISTANCE i + β2 TIMEt + β3,it ∑ TIMEt ∗DISTANCEi + β4, i STRUCTUALi + β5, i LOCATIONi

2.2. Method of Analysis: The DID approach

+ β6, i USESi + β7, i DIVERSITYi The DID approach is widely used to measure the causal effect of various interventions on treatment sites where policy or planning actions are implemented. It is a robust and attractive method of analysis that reveals the pure effect of the treatment after controlling site and time-specific factors. In this study, the conceptual equation of the DID

The unit of analysis was a block provided by the Korean Statistical Information Service (http://kosis.kr/). Seoul City (605 km2) is divided into approximately 66,000 blocks with an average of 100 persons each. Each block includes a different share of retail shops and residential 141

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(1) the adjacent blocks by the park considered the most affected areas following its establishment (DIS0), (2) blocks located within a 400meter distance from the park (DIS400: about five minutes’ walking distance), and (3) blocks located between a 400- and 800-meter distance from the park (DIS800: five-to-ten minutes’ walking distance). To control different locational and neighborhood contexts, this study only analyzed blocks located at distances of 1,200 m from the park and assumed control sites to be blocks located at distances between 800 and 1,200 m from the park, considering the walking time (Larsen, ElGeneidy, & Yasmin, 2010; Smailes, 1997). Moreover, the structural, locational, and land-use characteristics, including the land-use diversity of each block, were used to control the effects of other factors in the DID model based on studies on commercial districts (Jeon & Chung, 2014; Kim, Ahn, & Shim, 2014; Lee, Kim, & Jun, 2015; Lee, Park, Lew, & Kang, 2014; Ramlee et al., 2015; Yoon & Choi, 2013). Structural attributes indicate how the structural characteristics of blocks affect business sales. A smaller block size (BKSIZE) may lead to smaller total retail sales owing to the limited space for retail services. A higher share of irregular-shaped lots (LOTSHAPE) could result in lower total sales by limiting the efficient use of the land. The land price (LANDPRICE) is a measure to predict the stability and development potential of the land. The average size of lots in a block (LOTSIZE), building coverage ratio (CONSTRAREA), and floor area ratio (FLOORRATIO) control the effects of the built environment of each block. Regarding the locational characteristics of blocks, the EAST variable, which indicates that the block is located on the east side of the park, was added to examine the spatially varying effects of the park. Before the Gyeongui Line Forest Park project, the eastern neighborhoods were separated from the western areas by the railroad, implying different urban contexts. Before the opening of the park, changes in land use in the western neighborhoods were underway with the gradual expansion of the Hongik University commercial zone, although this was not the case in the eastern neighborhoods. In this study, the effects of subway zones and the commercial zones of Hongik University and

buildings, depending on its location. This study directly measured the economic impacts of the Gyeongui Line Forest Park using credit card and cash sales records from the Big Data Campus of the Seoul Metropolitan Government (https://bigdata.seoul.go.kr/). We collected total sales information based on gender, age, time, day, and business types. We aggregated the total sales of all business types at the block level for each month. Because the opening of the second phase of the Gyeongui Line Forest Park was in June 2015, and the total sales data was available only from 2013 to 2015, this study compared total sales before (July 2014 to December 2014) and after (July 2015 to December 2015) the opening of the park. Among the 1236 blocks located in the study area, pure residential areas that reported no sales records were removed, and the total sales from 659 blocks over 12 months were finally analyzed. Since this meant that the observations (over 12 months) within a particular block were correlated more than those between blocks, we used the random coefficient model. The dependent variables were log transformations of total sales (SALESit ) or total sales per business (SALEPBit ). The spatial relationship between the Gyeongui Line Forest Park and a block was measured by the distance from the park to the block (DISTANCEi ) using GIS software ArcGIS 10.4.1. It represented the network distance between origins and destinations measured along pedestrian roads, which was acquired from the National Geographic Information Institute. TIMEt was a dummy variable that indicated that the sales occurred after the opening of the second phase of the park between July 2015 and December 2015 (reference: sales before the opening of the second phase of the park from July 2014 to December 2014). The pure treatment effect of the park was measured by the coefficient (β3,it ) of interaction variables between TIME and DISTANCE. The interaction terms indicated the spatially varying effects of the park on treatment sites compared with the non-influence area (control) after the opening of the park (Fig. 3). Considering the waking distance in other related studies (Moore, Roux, Evenson, McGinn, & Brines, 2008; Nicholls, 2001; Wolch, Wilson, & Fehrenbach, 2005), the treatment sites were divided into three groups:

Fig. 3. Study area. 142

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DISTANCE (TDIS) indicate the pure treatment effect after the opening of the park (Table 3). The results show the positive effects of the Gyeongui Line Forest Park on both total sales and total sales per business, as shown in models 1 and 4. According to Model 1, the adjacent blocks to the park initially had about 14% lower total sales compared to the reference case, but they could have 28.2% higher total sales than the reference case after the opening of the park. Although the negative impact of the park existed even with the increase in the distance from the park, its opening increased the gross sales by about 10–12% at a statistically significant level (p < 0.01). This neighborhood revitalization effect corresponds to findings by previous studies that were conducted to determine the impact of other urban linear parks on nearby property values (Immergluck, 2009; Immergluck & Balan, 2018; Levere, 2014; Smith et al., 2016; Tajima, 2003; Weber et al., 2017). The positive effect of the park on total sales per business was relatively smaller than that of total sales. Among the blocks adjacent to the park (DIS0), the estimated effect of the sales per business model was about 10% lower than that of the total sales model. This result can be understood by the effect of competition between small businesses. The opening of the park attracted more visitors, which, subsequently, led to an increase in total sales. More small businesses opened in this area to share the economic benefits of the park, resulting in the reduction of sales per business. However, the growth in sales per business is still statistically significantly positive, indicating that the opening of the park could provide economic benefits for both existing and new business owners near the park. These empirical results suggest that urban linear parks could have a positive effect on nearby businesses, which can lead to the economic vitality of the neighborhoods. Interestingly, the effects of the Gyeongui Line Forest Park varied depending on neighborhood contexts. As shown in Fig. 4a and c, the effect of the opening of the park was estimated to record a 45.1% and 33.2% growth in total sales and total sales per business, respectively, in

Yonsei University were controlled. Jacobs (1961) argued that a mix of primary land use is a key condition for urban vitality. To control the differential effects of land use, we merged the percentages of three land uses that could affect sales. LAND1 refers to the percentage of single-family, multifamily, and condominium units among buildings. LAND2 is calculated as the ratio of commercial and public use, and LAND3 is considered the area of mixed residential/commercial use. Lastly, MIXED 4 is the entropy index ∑n

p ∗ lnp

for five types with the equation (− i = 1lnin i ; i: types of land use, pi : area of i land use, n: the number of i). The values of mixed land use variables range from 0 to 1, with a higher value implying more mixed use. All information about land use and stores was provided by the Seoul Big Data Campus. The descriptive statistics of each variable are summarized in Table 1. The DID model was estimated using a multilevel model in which a block was considered an upper level that included repeated observations for each month. We took log transformations of dependent variables, and no multicollinearity problems were found based on variance inflation factors. The multilevel model estimation was implemented using R package lme4 and MuMIn.

3. Results and findings The results of the regression analysis are summarized in Table 2. The dependent variable of models 1–3 is total sales (SALES) of each block, and the sales divided by the number of stores (SALEBP) are predicted in models 4–6. Each model represents the results of the entire eastern and western regions. The explanatory variables account for 23–50% of the proportion of variance explained by fixed effect, and it increases by 86–95% considering random effect. Overall, the results of the estimated explanatory coefficients are consistent with expectations. As noted earlier, the interaction variables between TIME and Table 2 Descriptive statistics.

N

Mean

SD

Min

Max

Dependent variable SALES STORE SALEBP

Grand total of all sales transactions reported (₩1,000,000) Number of stores SALES/STORE

7459 7459 7459

362.28 10.844 30.29

811.78 12.537 112.53

0.00091 1 0.00091

18910.85 97 3649.31

Time and interaction DIS0 DIS400 DIS800 TIME TDIS0 TDIS400 TDIS800

1 if located close to the park 1 if located within 400 m from the park (not DIS0) 1 if located within 800 m from the park (not DIS0and DIS400) 1 if sold after the opening of the park DIS0 * TIME DIS400 * TIME DIS800 * TIME

659 659 659 659 659 659 659

0.033 0.222 0.352 0.044 0.002 0.012 0.021

0.180 0.416 0.478 0.205 0.039 0.110 0.144

0.000 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000 1.000 1.000

Structural BKSIZE LOTSIZE LOTSHAPE LANDPRICE BUILTYEAR COVERAGE FLOORAREA

Size of block (km2) Weighted average size of lot (km2) Percentage of irregular parcels (%) Average land price (₩100,000) Median built year of buildings in block Total covered areas by buildings/BKSIZE Total floor areas of buildings/BKSIZE

659 659 659 659 659 659 659

0.006 0.003 0.410 0.388 26.353 0.366 1.398

0.006 0.005 0.315 0.805 8.560 0.111 0.753

0.001 0.000 0.000 0.000 0.000 0.000 0.000

0.103 0.084 1.000 12.670 56.000 0.921 6.707

Land use LAND1 LAND2 LAND3 MIX

Percentage of residential land use (%) Percentage of commercial land use (%) Percentage of mixed commercial and residential land use (%) Entropy index of land use

659 659 659 659

0.053 0.916 0.031 0.198

0.096 0.164 0.132 0.127

0.000 0.015 0.000 0.000

0.639 1.000 0.903 0.365

Location EAST LARGEROAD SUBWAY UNIV1 UNIV2

1 1 1 1 1

659 659 659 659 659

0.396 0.320 0.203 0.059 0.138

0.489 0.467 0.403 0.236 0.345

0.000 0.000 0.000 0.000 0.000

1.000 1.000 1.000 1.000 1.000

if if if if if

located located located located located

in the east side of the park close to a large road (> 25 m) within 400 m from a subway within 400 m from a commercial district (Shin-chon) within 400 m from a commercial district (Hongik)

143

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Table 3 Parameter estimation results. Model1,

2

Total sales model

Total sales per business model

Region

Model 1 Whole

Model 2 EAST

Model 3 WEST

Model 4 Whole

Model 5 EAST

Model 6 WEST

DIS0 DIS400 DIS800 TIME TDIS0 TDIS400 TDIS800 BKSIZE LOTSIZE LOTSHAPE LANDPRICE BUILTYEAR COVERAGE FLOORAREA LAND1 LAND2 LAND3 MIX EAST LARGEROAD SUBWAY UNIV1 UNIV2 CONS marginal R2 conditional R2

−0.14 −0.362 −0.494** −0.008 0.282** 0.101** 0.119** 48.382** 11.275 −0.046 0.14* 0.005 0.451 0.151 6.528 8.796 8.31 3.799** −0.231 0.804** 0.267 3.182** 1.794** 7.225 0.445 0.948

−0.942 −1.006** −1.562** 0.016 0.451** 0.246** 0.222** 59.219* 74.573* 0.645* 0.193 −0.008 3.118** 0.06 0.739 2.488 2.695 3.001** – 1.325** 0.805* 1.924** – 12.683* 0.502 0.937

0.159 −0.093 0.088 −0.021 0.109 −0.024 0.071* 44.359** 4.412 −0.184 0.017 0.006 −2.218* 0.169 −1.31 0.614 – 4.046** – 0.353* −0.299 – 1.871** 15.808** 0.464 0.954

−0.654* −0.352* −0.441** −0.027 0.184** 0.018 0.1** 13.673 7.371 −0.12 0.128* −0.003 −0.254 0.064 6.43 7.88* 7.924* 1.3** −0.042 0.359** 0.054 1.171** 0.611** 8.27* 0.237 0.878

−0.859* −0.472* −0.918** 0.009 0.332** 0.074* 0.131** −0.808 34.12 0.109 0.284* −0.007 1.114 0.123 3.121 4.507 4.374 0.992* – 0.704** 0.337 0.576 – 11.081* 0.300 0.867

−0.602 −0.326 −0.135 −0.048* 0.023 −0.032 0.088** 13.786 2.513 −0.121 0.018 −0.002 −2.056** 0.058 −1.156 −0.35 – 1.384** – 0.04 −0.27* – 0.652** 16.884** 0.259 0.888

Note: *p < 0.10, **p < 0.05. 1 Estimated results of monthly time dummies are not presented. 2 Administrative boundary dummies are not presented (reference: Yeonnam-dong).

Fig. 4. Estimated park-induced influences on sales. The x-axis denotes the interaction variable (TDIS), the y-axis denotes the estimated coefficients, and the solid/ diagonal pattern indicates the significance level of the estimates. 144

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after the opening of the park. The results reveal that a region depressed by unwanted infrastructure such as railways could benefit more when that unused or abandoned infrastructure is converted to a desirable one such as a park or a green open space. Considering the neighborhood contexts of the case study area, we suggested that the level of commercialization could be related to the economic impacts of the park. The findings are meaningful because this study captured the revitalization impact of the park by directly using actual sales data instead of using indirect outcomes such as property values. The use of big data such as that collected from credit card and cash sales records provides an alternative means of measuring the economic impact of green spaces. Measuring the economic impacts of the park on nearby businesses is very important in justifying policy decisions regarding public investment on parks and open spaces. Empirical evidence on the economic impacts of parks based on more reliable data and methods could lead to the construction of more parks. During the two initial public hearings for the Gyeongui Line Forest Park project, much of the discussion was devoted to revitalizing the depressed surrounding neighborhoods (Lee, 2001, 2002). However, at that time, there was no evidence as to how much and how significant the impact would be. In order to justify the Gyeongui Line Forest Park, the project had to reveal more direct and indirect socioeconomic benefits than alternative land use. Specifically, the feasibility study calculated the opportunity cost of the park project by multiplying the building floor areas and rent after assuming that new commercial buildings would be constructed on the park site. The Gyeongui Line Forest Park project was fortunate in that the construction proceeded despite uncertain economic impacts. Recently, more data, including big data, has become available to measure the economic impacts of parks, as has been done in this study. This empirical research could therefore provide alternative and more precise methods to measure the economic impacts of parks. Urban parks have been an important component of cities. In particular, an increasing number of linear green spaces in urban areas are proposed to be built on industrial-era infrastructure. Linear urban parks are being planned as regeneration tools for deprived areas near old and abandoned infrastructure of the post-industrial era. People’s greater access to green spaces than traditional urban parks could also expand the benefits of the linear park. This analysis supports the idea that a linear urban park, transformed from industrial-era infrastructure, plays a key role in revitalizing the neighborhoods around it. As many existing elements of infrastructure such as railways, roads, and highways have been abandoned or underutilized, it would be justified to convert such sites into green spaces. Green linear spaces are expected to overcome the disadvantages of traditional urban parks (square or rectangular spaces) designed for passive recreation, even though it has only been a short time since its popular establishment. Our findings also suggest that there are different influences of green spaces on the two regions (“eastern” vs. “western”) that had been separated by the railways. There is a tendency to boost nearby property values when the linear parks are designated across different neighborhoods; a few examples of this are: the Northside and Southside of Atlanta’s BeltLine (Immergluck & Balan, 2018) and the Eastern and Western side of Chicago’s the 606 (Smith et al., 2016). Therefore, when long narrow parks are planned to (re)connect neighborhoods, the differential patterns should be monitored. Furthermore, careful consideration should be given to the fact that a new urban park has accelerated gentrification known as “green gentrification” and its related displacement processes. In the case of Gyeongui Line Forest Park at least, besides increasing the sales, dramatic increases in commercial real estate prices attracted widespread media attention. These rent gaps and the changing consumer environment often displace local retailers as they cannot afford the rent to continue their businesses (Mermet, 2017; Monroe Sullivan & Shaw, 2011; Zukin et al., 2009). In turn, retail undertakings that meet the essential needs of long-term residents (i.e., grocery stores) could be shut

the adjacent eastern blocks, which originally experienced a negative effect on sales because of the railroad. This positive effect continued until the blocks located within 10 min’ walking distance from the park (< 800 m). However, the estimated effects in the west side of the park were not similar to those of the eastern areas. There was no statistically significant effect on total sales and total sales per business because of the proximity to the park, regardless of its opening, except in areas within distances of 400–800 m (Fig. 4b and d). This spatially differentiated effect may be attributed to the different neighborhood contexts. The neighborhoods had been split into east and west by the railway. The western areas of the park fell under the Hongdae commercial area. The growth of the Hongdae commercial area combined with the opening of new subway lines in 2010 resulted in the commercial gentrification of the western areas (Kim, 2013, 2015). However, because the western neighborhoods had already been commercialized prior to the designation of the park, the revitalizing effect of the Gyeongui Line Forest Park for the west side was lower than it was for the east side of the park. The unexpected growth in sales among the blocks within a distance of 400–800 m (DIS800) in the western areas could also be considered an outcome of the commercial gentrification process. Consequently, the conversion of the railroad into the park did not have a positive effect on the western areas. On the other hand, the eastern region had been less commercialized compared to the western side of the park. It comprised typical low-rise residential buildings from a land readjustment project in the 1970s; the buildings were less than two stories on small parcels of land (Jeong & Kim, 2013). Additionally, there were only three sections that could be used to connect the west and the east. This poor environment for pedestrians and the negative externalities of the railways such as noise and dirt curbed the expansion of the Hongdae commercial areas and depressed new development in the eastern areas (Lee, 2001, 2002). However, the intervention to designate the park made this hidden and depressed neighborhood a “worthwhile place to be” by improving pedestrian accessibility and the perception of visitors, thus contributing to an expansion of the commercial district. As a result, the sales of the eastern blocks increased significantly over a short period of time after the opening of the park and led to the revitalization of local economies (Jung et al., 2016; Kwon et al., 2017; Lee, 2017; Won, 2017). Regarding control variables, overall variables exhibited the expected results consistent with the literature. For instance, land use mix measured by the entropy index showed a strong positive effect on the sales of the blocks, which was consistent with the findings of Jane Jacobs’s study (1961). Size of the block (BKSIZE), weighted lot average (LOTSIZE), and floor area ratio (FLOORAREA) had a mostly positive association with the business sales in reflecting the physical characteristics of the business. LANDPRICE, the variable representing the value of the land and proxy of the building rent price, also had a positive effect on the sales. Two main commercial districts close to the university around the study area (UNIV1 and UNIV2) were found to be important locations of business sales. Contrary to our expectations, some structural variables, such as COVRATIO and LOTSHAPE, revealed different effects between the eastern and western models. More irregularly shaped parcels and higher building coverage ratios led to more sales in eastern areas. These results may reflect a recent consumer preference for visiting organic and narrow commercial streets in Korea. We expected that the blocks with good accessibility to the subway (SUBWAY) would correlate positively with sales, but the estimated results of the western side of the park were quite the opposite, probably because of the strong positive effect of the Hongik commercial district overlapping the subway station. 4. Discussion and conclusion This study showed the revitalization effect of the Gyeongui Line Forest Park by analyzing sales data as the primary economic outcome 145

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down and converted to high-end establishments (i.e., cafés and pubs). This dynamic of businesses around a new urban park should be monitored especially because the same phenomenon can extend to the commercial areas. Strong interventions would then be needed along with urban greening policies, such as the Vital’Quartier policies in Paris, to enhance existing local retail businesses and rezoning along the New York’s High Line to protect the neighborhoods’ art gallery district. Residential displacements should be also considered as the green space would be an amenity for residents. In terms of future research, this study could be improved by analyzing the long-term economic impacts of the park. Because of limited data accessibility, we have only focused on the short-term effects of the park’s opening. This study analyzed aggregated actual sales data of local businesses at a block level, but individual actual sales data for each local business could provide more meaningful implications. Beyond the aggregated sales, it is important to investigate which business types are affected by new urban parks. Changes in business types and land use could provide more valuable information about the socioeconomic outcomes of urban parks. Lastly, further studies that investigate gentrification and displacement from the perspective of residential and retail spaces should be conducted.

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