sustainability

Article Seeing Impacts of Design Strategies on Local Economy through Big Data: A Case Study of Gyeongui Line Forest Park in

Jisoo Sim

Urban Research Division, Korea Research Institute for Human Settlements, 5 Gukchaegyeonguwon-ro, Sejong-si 30149, Korea; [email protected]

 Received: 13 July 2020; Accepted: 13 August 2020; Published: 19 August 2020 

Abstract: Although big data has emerged as a crucial data source in urban studies, urban park-related studies still rarely use data such as that from card transactions. This study fills the gap between big data and park studies by using card transaction data within 400 m of the Gyeongui Line Forest Park in terms of economic benefits on local business. The authors divided the into five sections according to each section’s design strategy to examine the relationship between the design features and card transaction behaviors. With the data, the authors analyzed the average ages of card users to understand average users’ age in each section. Results show the average ages increased from 2015 to 2017 in Sections 3–5 by years. Sections 1 and 2 describe decreasing of user ages by year, which means young generations visited Sections 1 and 2 For Section 1, amounts of average card transaction also increased from 2015 to 2017 continuously. Compared to other sections, only Section 1, as an open space within a commercialized area, contributed to local business positively. Other sections, such as 2–5, represented the negative impacts on local business from 2016 to 2017.

Keywords: park design strategies; card payment; big data analytics; park user analytics; Gyeongui Line Forest

1. Introduction There are many studies for identifying the impacts of a park on property values of adjacent areas. Some studies coined the term “the growth machine” to describe this trend and accentuate the role of a park, especially an urban park, in economic growth [1]. Researchers have used a hedonic pricing model to estimate the premium values of adjacent areas near an urban park [2–4]. Additionally, researchers have estimated the value of a park by calculating the price of vegetation and elements in an urban park [5]. Though these studies have proven the economic benefits of a park, researchers rarely study the economic benefits of a park on the local business scale because of the difficulties of collecting data. One of the reasons why the economic benefits of were difficult to estimate locally was because of the data scale. Data in the big data era have the potential to make this possible. For example, in the case of card sales data, variables such as the number of times a person consumes, the amount of consumption, and the time of consumption are shown in units of “place”. This has the potential to newly measure the economic benefits of parks. Card transaction data has been used to identify consumer behavior [6] and compare the differences between race, gender, and age [7]. Card transaction data has been cumulated spontaneously and its impacts on economic activity has been analyzed by the diffusion of innovation theory (DOI). Roger introduced the DOI concept in 1961 and it explains an innovative interpersonal networks [8]. Focusing on the differences between races, Medina and Chau explored the differences between Anglos

Sustainability 2020, 12, 6722; doi:10.3390/su12176722 www.mdpi.com/journal/sustainability Sustainability 2020, 12, x FOR PEER REVIEW 2 of 13

interpersonal networks [8]. Focusing on the differences between races, Medina and Chau explored the differences between Anglos and Mexican Americans in their card usage behavior [7]. Among the studies related to card transaction data, there are a few studies which configured the relationship between places and card usage behavior. This study attempts to understand the impact of Gyeongui Line Forest Park on the surrounding economy by using previously available card transaction data. In particular, by

Sustainabilitydividing the2020 linear, 12, 6722 park into five units taking into account the time of opening and design features,2 of 12 and comparing the economic impacts of the same time period, the impact of the park design strategy on the local economy will be examined separately. The purpose of this study is to: (1) know andhow Mexican parks affect Americans the local in theireconomy, card usage (2) know behavior how [ 7park]. Among design the affects studies the related local toeconomy, card transaction and (3) data,know there how are to ause few card studies sales which data. configured This study the is relationship organized between in the following places and order. card usage A theoretical behavior. reviewsThis and study studies attempts on toparks’ understand benefits the include impact economic of Gyeongui benefits. Line ForestThe methodology Park on the surrounding divides the economypark into byfive using sections previously and analyzes available each card transactiondesign. The data. card In sales particular, data will by dividingbe analyzed the linear and parkcompared into five by section. units taking into account the time of opening and design features, and comparing the economic impacts of the same time period, the impact of the park design strategy on the local economy will2. Literature be examined Review separately. The purpose of this study is to: (1) know how parks affect the local economy, (2) know how park design affects the local economy, and (3) know how to use card sales data. This2.1. Benefits study isof organizedUrban Parks in the following order. A theoretical reviews and studies on parks’ benefits include economic benefits. The methodology divides the park into five sections and analyzes each On the topic of the benefits of urban parks, many studies have been conducted to verify their design. The card sales data will be analyzed and compared by section. functions and roles in . Basically, the benefits of urban parks can be divided into several 2.categories. Literature Some Review studies divided the benefits into direct and indirect benefits [9,10]; other studies used on-site and off-site benefits depending on the causes of the benefits [11,12]; and many other 2.1.studies Benefits divided of Urban the benefits Parks into three aspects [13,14]: (1) economic, (2) social, and (3) environmental benefits. Since environmental benefits from urban parks are already verified in various studies, this On the topic of the benefits of urban parks, many studies have been conducted to verify their literature review will be focused on economic and social benefits instead of environmental benefits functions and roles in cities. Basically, the benefits of urban parks can be divided into several and their evaluation models. categories. Some studies divided the benefits into direct and indirect benefits [9,10]; other studies used on-site and off-site benefits depending on the causes of the benefits [11,12]; and many other studies 2.1.1. Economic Benefits divided the benefits into three aspects [13,14]: (1) economic, (2) social, and (3) environmental benefits. SinceThe environmental economic benefits benefits from from urban urban parks parks can are alreadybe considered verified in in two various ways: studies, one is thisthe economic literature reviewvalue of will an urban be focused park itself, on economic and the andother social is the benefits value that instead is generated of environmental by urban parks. benefits For and the their first evaluationone, on-site models. value, researchers have studied its evaluation by using hedonic pricing, contingent models, and travel costs. These models were developed to evaluate the non-market value (Figure 1). 2.1.1.Economists Economic Benefits use travel costs and contingent valuation. As an economic value of the benefits, travelThe cost economic assumes benefitsan equalfrom cost of urban travel parks and measures can be considered traveled distances in two ways: to visit one parks. is the Travel economic cost valueestimates of an prices urban of park urban itself, parks and on the the other basis is of the the value amount that of is money generated necessary by urban to travel parks. to For them. the This first one,method on-site is used value, when researchers market haveprices studied are not its availabl evaluatione. The by important using hedonic feature pricing, of the contingent travel cost models, model andis that travel the costs costs. of These travel models to a site were are developeda metric for to the evaluate monetary the value non-market of the valuesite [11]. (Figure 1).

Figure 1. Framework of non-market valuation. Figure 1. Framework of non-market valuation. Economists use travel costs and contingent valuation. As an economic value of the benefits, travelContingent cost assumes valuation an equal asks cost park of travelusers andhow measures much money traveled they distances are willing to visitto pay parks. (WTP) Travel for costspecified estimates benefits prices from of urbanan urban parks park. on theWillingness basis of the to amountpay is a ofway money to measure necessary theto economic travel to cost them. a Thisuser methodis willing is and used able when to pay market for goods prices or are services not available. [11]. The important feature of the travel cost model is that the costs of travel to a site are a metric for the monetary value of the site [11]. Contingent valuation asks park users how much money they are willing to pay (WTP) for specified benefits from an urban park. Willingness to pay is a way to measure the economic cost a user is willing and able to pay for goods or services [11]. Further, Stevens and Allen explain methods that can be used to evaluate urban parks’ value, and evaluate four parks by using a hedonic pricing model [12]. They divide the benefits of urban Sustainability 2020, 12, 6722 3 of 12 parks into two classes based on the source of the benefits: on-site and off-site benefits, and assert the advantages of a hedonic model. According to their study, a hedonic model has two advantages: (1) it captures the value of external benefits and costs, (2) it is based on actual transaction data [12]. On-site benefits are generated directly by using the park, and off-site benefits accrue to people outside the park. The hedonic pricing model infers the park’s benefits from a non-market resource from the prices of associated goods that are traded in the market. How can we then evaluate the success of the redevelopment of transportation into urban parks? The success of redevelopment projects can be measured by using cost-benefit analysis. Cost-benefit analysis (CBA) estimates the monetary value of the benefits and costs to conclude whether the project is worthwhile [15] and can be a useful way to evaluate non-market goods such as ecosystems [16]. To conduct CBA for evaluation of redevelopment projects, verification of benefits is also important. Benefits can be divided into three categories: economic [17–19], environmental [20,21], and social [22,23]. Many studies have tried to verify economic benefits from redevelopment of transportation infrastructure into urban parks, and most studies have used the hedonic pricing model which was invented to evaluate environmental metrics on property values [12,24]. For example, Tajima estimates the economic benefits of the Big Dig in Boston through hedonic pricing methods [25]. The Big Dig was a mega-scale redevelopment project that turned an elevated highway into an underground expressway and opened a park on the surface. As a result, he finds a negative relationship between parks and property prices and a positive coefficient between the distance from the highway and property prices [25]. Levere, an economist from UC San Diego, who assessed the ’s impact on prices using home sales and property valuations, estimates that, in 2010 alone, the gained $100 million in property tax increases as a result of the High Line [26]. Kang and Cervero investigated changes in land values for properties resulting from the Cheonggyecheon restoration project, which replaced old with an urban park and verified the increase in land values compared to before the [27]. They used property address, land use, assessed land value, and property shape, slope and level of access, and conducted multilevel hedonic models to estimate the impact of the project on the urban environment. Property values increased and the value added depended on the distance from the park [27]. This study shows that an urban park which was redeveloped on a highway can be a trigger to increase property values.

2.1.2. Social Benefits Many studies have researched the role of urban parks as an important space to increase the quality of daily life of urbanized society. Empirical evidence supports that urban parks, green ways, and urban forests in urban contexts improve the quality of life in many ways. Further, in addition to environmental benefits such as reducing pollution, urban parks contribute to improve health, enhance social interactions, and provide peacefulness. Ulrich, for example, compares psychophysiological reactions toward three : vegetation, water, and urban [27–31]. By using metrics which represent physiological and psychological measures, he found that vegetation and water landscapes had more beneficial influences on people’s psychological stages in feeling positive effects [30]. Chiesura states that urban parks provide social and psychological benefits and enrich our lives with meaning and emotions. She verifies the experience of nature is a source of positive feelings by investigating how people’s emotions are involved in urban green spaces [29]. Coley, Sullivan, and Kuo verify that urban parks encourage social interactions among residents and contribute to social integrations [32]. Their findings offer that natural elements increase opportunities for social interactions, using outdoor places to promote communication within neighborhoods [32]. Natural environments can also help people relax, and they also promote social interactions [33]. Kuo et al. show that the presence of urban nature in the inner city generated positive responses from residents. The density of urban nature also increases the sense of safety [33]. Peters, Elands, Sustainability 2020, 12, 6722 4 of 12 and Buijs assert that urban parks can be a trigger to generate social interactions by stimulating social cohesion [34]. They use a survey, observations, and interviews to carry out social interaction research in five urban parks in The Netherlands. According to this study, urban parks can promote mingling of different ethnic groups and promote interactions among visitors. These results reinforce that the social benefits of urban parks include social interactions.

2.2. Big Data Analytics in Urban Studies Based on the attributes of social media data, researchers use it in detecting and land uses, supporting public engagement, and identifying the relationship between people and place. Big data analytics allow us to detect hidden use of urban areas, green spaces, and protected areas. Tu et al. discovered urban functions by using big data [35]. From the data, the authors extracted behaviors of users and identified the time of postings. By doing so, they revealed the use of urban areas by functions such as work and time [35]. Similarly, Chen et al. depicted urban functions by using hot spot analysis with social media data. The authors identified the urban functions and places that emerge as central places [36]. Silva et al. demonstrated using social media data allows us to detect characteristics of each urban area [37]. In urban planning, many studies use social media to find functional areas. Studies in landscape also use social media to detect the ecosystem. Oteros-Rozas et al. examined over a thousand photos from Flickr and Panoramio to identify the relationship between landscape diversity and ecosystem services [38]. Landscape diversity is an important aspect to assess the quality of landscape and cultural ecosystem services refer to benefits of green spaces to people. The authors find a weak and positive relationship between two factors [38]. Social media data provides opportunity to detect soft factors such as the labor market. Batty et al. assert the importance of social media data for understanding the labor and housing market [39].

3. Methods

3.1. Study Site The Gyeongui Line Forest is a park built on the Gyeongui railroad, which was originally built in 1906 to connect Seoul () and (). In 2005, the city rerouted the railroad underground and announced that they would redevelop the vacant area atop into a park. The project has been completed in three phases: Phases 1 and 2 were opened in 2012 and 2015; Phase 3 was opened in 2016. The Gyeongui Line Forest Road is a park that brings the vitality of the city and the vitality of nature to the useless land left by undergrounding the Gyeongui Line (). The Gyeongui Line double track railway passes 10–20 m below the park and the airport railway passes 30–40 m below. This road was a traffic route that carried many people and supplies before and after the Gyeongui Line was laid in 1906, so villages and warehouses naturally flourished around the road. Gyeongui Line Forest Park (Figure2) can be divided into three phases according to the completed years. In this study, the authors divided three phases into five sections reflecting design features of each section. If explained in order, they are as follows. Section1 is the Yeonnam-dong section, which was opened in the second phase of Gyeongui Line Forest Park. The hallmark of this section is the city’s open space where you can enjoy picnics and small shops. Section2 is a book-themed park as part of the Phase 3 plan. There are bookstores in the park that display and sell books. Visitors can walk along the street to view books or buy their own. Section3 is part of Phase 3 and has been created as a walkway for residents. The promenade, which is characteristic of general linear parks, is the main use. The 4th section is the first phase on Gyeongui Line Forest Park. The design characteristics are similar to those of the 3rd section, but the facility has deteriorated after a long time since its opening. Sections3 and4 cross the . Section5 takes the form of a small park as part of Phase 3. Located in an office-dense area, this park is a small resting place for office workers (Figure3). Sustainability 2020, 12, x FOR PEER REVIEW 5 of 13 SustainabilitySustainability2020, 12 2020, 6722, 12, x FOR PEER REVIEW 5 of 135 of 12

Before After Before After Figure 2. Gyeongui Line Forest scene. FigureFigure 2. Gyeongui2. Gyeongui Line Line ForestForest scene.

FigureFigure 3. 3.Five Five sections sections of the study. study. Figure 3. Five sections of the study. 3.2. Data3.2. CollectionData Collection 3.2. Data Collection The authorsThe authors collected collected data data about about Shin Han Han Card Card transaction transaction from from Seoul SeoulBig Data Big Campus. Data Campus. The The SeoulSeoul BigThe Big Dataauthors Data CampusCampus collected provides data about ma manyny Shin kinds kinds Han of Card of big big datatransaction data such such as from ascard card Seoul transaction transaction Big Data data Campus. data from from Shin The Shin Han Card,HanSeoul Card, telecommunicationBig Datatelecommunication Campus provides data data from ma fromny Korea kindsKorea Telecom, ofTelecom, big data and such from from as Shincard Shin Hantransaction Han Card, Card, the data the number from number Shinone one card incardHan Korea, inCard, Korea, provides telecommunication provides the largest the larg amountdataest fromamount of Korea card of card salesTelecom, sales data anddata in Korea.from in Korea. Shin Among HanAmong Card, the the data, the data, number the the card card one user’s card in Korea, provides the largest amount of card sales data in Korea. Among the data, the card age, paymentuser’s age, amount, payment number amount, of number payments, of payments and payment, and payment location location were collectedwere collected from from 2015 2015 to 2017. user’s age, payment amount, number of payments, and payment location were collected from 2015 The authorsto 2017. collected The authors the latestcollected three the years latest from three 2015 years to from 2017. 2015 The to park 2017. opened The park to theopened public to inthe 2012, publicto 2017. in The2012, authors 2015, and collected 2016. When the latest the authors three ye consideredars from 2015the phases to 2017. of Thethe park,park theopened latest to three the 2015, and 2016. When the authors considered the phases of the park, the latest three years were an yearspublic were in 2012, an appropriate2015, and 2016. period When for the the authors study. consideredIn terms of the the phases study of area, the park,this study the latest included three appropriate period for the study. In terms of the study area, this study included office blocks within officeyears blockswere an within appropriate 400 m from period the parkfor the since study. 400 mIn is terms considered of the as study walkable area, in this 5 min study [40]. included 400 moffice from blocks the park within since 400 400 m from m is consideredthe park since as 400 walkable m is considered in 5 min as [40 walkable]. in 5 min [40]. 3.3. Data Analytics 3.3. Data3.3. AnalyticsData Analytics This study statistically verified whether design characteristics were drawn by section and card Thissales study Thisdata studywere statistically comparedstatistically verified to verified show whether significant whether design design differences characteristics characteristics by using R.were were The drawn drawnfactors by used by section section in the and study and card card sales dataaresales shown were data comparedwerein the compared table. to Age show to was show significant divided significant into di 6ff diffscaleserenceserences from by byteenagers using using R. R. Theto The 60-year-olds factors factors used used (score inin the the1: 10 study s, 2: are shownare in shown the table. in the Age table. was Age divided was divided into 6 into scales 6 scales from fr teenagersom teenagers to 60-year-olds to 60-year-olds (score (score 1: 1: 10 10 s, s, 2: 2: 20 s, 3: 30 s, 4: 40 s, 5: 50 s). The average payment amount is the average payment amount per person, and it can be seen how much is spent here. The number of payments is divided by the total number of payments, and you can see how many payments are made per average store. Statistical significance Sustainability 2020, 12, 6722 6 of 12 was judged by statistically comparing age, average payment amount, and number of payments for each year. The author also visualized the result by using ArcGIS.

4. Results

4.1. Design Features of Each Section Because of its long length, there are various places and scenery attached to the Gyeongui Line Forest Park (Table1). Section1 is the busiest part of the Gyeongui Line Forest Road, the Yeonnam-dong section. This section, which has a large floating population and easy access due to the in Hongdae, became the best “hot-spot” in Seoul right after its opening, replacing Yeonnam-dong and Yeonhui-dong. As well as on both sides of the park, restaurants, cafes, and pubs flourished in all the surrounding alleys, creating the nickname “Yeontral Park”. Another characteristic of the Yeonnam-dong section is the waterway. In summer, the waterways and ponds turn into outdoor water that are hard to see in Seoul. The westernmost section of the Gyeongui Line Forest Road, which passes throughSustainabilitySustainability Yeontral2020, 12 ,2020 x FOR, 12 PEER, Park, x FOR REVIEW PEER near REVIEW the bustling Hongdae Station, and leads7 of to 13 Gajwa7 of 13 Station, SustainabilitySustainability 2020, 12 2020, x FOR, 12 ,PEER x FOR REVIEW PEER REVIEW 7 of 13 7 of 13 is full of gingkoSustainability treesSustainability grown 2020, 12 2020, x FOR to, 12 cool,PEER x FOR REVIEW PEER the REVIEW tall . 7 of 13 7 of 13 SustainabilitySustainability 2020, 12 ,2020 x FOR, 12 PEER, x FOR REVIEW PEERTable REVIEW 1.Table Five 1.sections Five sections of the study of the in study detail. in detail. 7 of 13 7 of 13 Table 1.Table Five 1.sections Five sections of the study of the in study detail. in detail. No. No. Photo PhotoTable 1.Table Five 1.sections Five sections of the study of Context the in study detail. Context in detail. In-Out In-Out No. No. TablePhoto 1.PhotoTableFive 1.Table Five sections 1.sections Five sections of the the study of Context studythe in study detail. Context in in detail. detail. In-Out In-Out No. No. Photo Photo Context Context In-Out In-Out No.No. No. Photo Photo Context Context In-Out In-Out In-Out

1 1 opennessopenness 1 1 opennessopenness 1 1 opennessopenness 1 1 1 opennessopenness openness

2 2 Semi opennessSemi openness 2 2 Semi opennessSemi openness 2 2 2 Semi opennessSemi opennessSemi openness 2 2 Semi opennessSemi openness

3 3 enclosedenclosed 3 3 enclosedenclosed 3 3 3 enclosedenclosed enclosed 3 3 enclosedenclosed

4 4 enclosedenclosed 4 4 enclosedenclosed 4 4 4 enclosedenclosed enclosed 4 4 enclosedenclosed

5 5 5 Semi opennessSemi opennessSemi openness 5 5 Semi opennessSemi openness 5 5 Semi opennessSemi openness 5 5 Semi opennessSemi openness

Sustainability 2020, 12, 6722 7 of 12

In Section2, scenery of the village next to the train track, lined with shabby and low , unfolds in the section of Wow that reproduces the crossing scene called Ding Ding Street. In this section with a long “book street”, there are bookstores run by various publishers. There is no place like a small bookstore to thaw frozen feet away from the cold winter wind. A few steps away from Ding Ding, which is also the birthplace of Hongdae’s indie music, design museums, libraries, and small theaters in the Hongdae area invite tired walkers. Section3, Daeheung-dong, the first of the Gyeongui Line Forest , has a rougher design than the rest of the sections, but in spring, it transforms into an “Insta”-worthy place full of colorful cherry blossoms. Unlike other sections, there are separate bicycle lanes, so there are many cyclers pedaling through Yoshino Cherry and Wild Cherry Forests. In the section of Sinsu-dong in front of Sogang University, there is a sculpture that crosses the railroad tracks and recalls memories of playing on the tracks. In this section, there was a -shaped artificial stream, Seontongmulcheon, which was created to prevent the flooding of Mapo during the Japanese colonial period. If visitors walk leisurely in this section, which is rare, it is highly likely that they will gravitate to the restaurant of Mapo’s famous charcoal-grilled ribs. The Section4, Yeomni-dongsection, features the history of an old salt-making village. When visitors leave the Gyeongui Line Forest Road, visitors can see “Sageum-gil” and “Sageum-naru” that make use of the old salt warehouse and market buildings. Office buildings are adjacent to Yeomni-dong, so the park is used as a short but quiet lunch break for office workers. An alternative cultural marketplace called Jeongjang, where performances, exhibitions, and flea markets are frequently held, is immediately attached to this section. Section5, where there was a new warehouse in the Joseon Dynasty, Seonhyecheong, had a steeper slope than other sections. This is the place that corresponds to the dragon’s waist in the name Yongsan (Dragon mountain in English). In this wide field of view, you can see the old terrain of Seoul flowing down the Han River. When visitors go up to Baekbeom Bridge, visitors can look down on the scenery behind Seoul just like any observatory. On the eastern end of Wonhyo-ro, which was completed last, there are many traces of the old railroad tracks, and there is a community facility with a small truck and a “small hutch near the train” still exists. If you walk a little further, you will reach Hyochang Park, where Hyochang and the tombs of Kim Gu, Lee Bong Chang, and Yun Bong Gil are together.

4.2. Age of the Card Users Near the Park The authors examined the average age of card users from Sections1–5 from 2015 to 2017 to provide implications about the generations using each section. The authors divided age generations from 20 to 60 into 6 scales from 1 to 6. For instance, 1 means under 20, 2 means ages 20–29 and 6 means over 60 years of age. Table2 represents the results of age analysis. For Section1, average age scale is 3.74 in 2015, 3.85 in 2016, and 3.80 in 2017. If the same users continue to regularly use the park, the average age scales have to increase a little bit by years. For instance, the average age of Section3 increases from 2015 to 2017 (3.40 in 2015, 3.44 in 2016, and 3.49 in 2017). Sections4 and5 also show increased average ages from 2015 to 2017 (Section4: 3.69 in 2015, 3.72 in 2016, and 3.79 in 2017, Section5: 3.48 in 2015, 3.54 in 2016, and 3.58 in 2017). However, Section1 shows a di fferent result from the other sections. In the case of Section1, the average age scale from 2016 to 2017 shows a decreased age scale from 3.85 to 3.80. It can be considered that the younger generation visited Section1 more frequently in 2017 than 2016 and it can be also deduced that Section1 has greater potential to welcome younger generation than other sections. In Section2, the average age in 2015 was 3.49 and it continuously decreased to 3.46 in 2016. Figure4 visualizes the di fferences between average ages of each section by using statistical analysis. Figures5 and6 show the di fferences between ages by each section by using ArcGIS. Sustainability 2020, 12, x FOR PEER REVIEW 8 of 13

4.2. Age of the Card Users Near the Park The authors examined the average age of card users from Sections 1–5 from 2015 to 2017 to provide implications about the generations using each section. The authors divided age generations from 20 to 60 into 6 scales from 1 to 6. For instance, 1 means under 20, 2 means ages 20–29 and 6 means over 60 years of age. Table 2 represents the results of age analysis. For Section 1, average age scale is 3.74 in 2015, 3.85 in 2016, and 3.80 in 2017. If the same users continue to regularly use the park, the average age scales have to increase a little bit by years. For instance, the average age of Section 3 increases from 2015 to 2017 (3.40 in 2015, 3.44 in 2016, and 3.49 in 2017). Sections 4 and 5 also show increased average ages from 2015 to 2017 (Section 4: 3.69 in 2015, 3.72 in 2016, and 3.79 in 2017, Section 5: 3.48 in 2015, 3.54 in 2016, and 3.58 in 2017). However, Section 1 shows a different result from the other sections. In the case of Section 1, the average age scale from 2016 to 2017 shows a decreased age scale from 3.85 to 3.80. It can be considered that the younger generation visited Section 1 more frequently in 2017 than 2016 and it can be also deduced that Section 1 has greater potential to welcome younger generation than other sections. In Section 2, the average age in 2015 was 3.49 and it continuously decreased to 3.46 in 2016. Figure 4 visualizes the differences between average ages of each section by using statistical analysis. Figures 5 and 6 show the differences between ages by each section by using ArcGIS.

Table 2. Average age scales from 2015 to 2017. Sustainability 2020, 12, 6722 8 of 12 Section 2015 2016 2017 1 3.74 3.85 3.80 Table 2. Average age scales from 2015 to 2017. 2 3.49 3.46 3.47 Section3 20153.40 20163.44 3.49 2017 14 3.743.69 3.853.72 3.79 3.80 25 3.493.48 3.463.54 3.58 3.47 3 3.40 3.44 3.49 4 3.69 3.72 3.79 5 3.48 3.54 3.58

(standardized average ages) (standardized average ages)

Sustainability 2020, 12, x FOR PEER REVIEW (section) (section) 9 of 13 Sustainability 2020, 12, x FOR PEER REVIEW 9 of 13 Figure 4. Average ages using five sections in 2015 (left) and 2017 (right).

Figure 4. Average ages using five sections in 2015 (left) and 2017 (right).

FigureFigure 5. 5.Average Average age age of of five five sections sections in in 2015 2015 (left (left) and) and 2016 2016 (right (right). ). Figure 5. Average age of five sections in 2015 (left) and 2016 (right).

Figure 6. Average age in 2017. FigureFigure 6. Average 6. Average age in age 2017. in 2017. 4.3.4.3. Impacts Impacts of of the the Park Park on on Local Local Economy Economy 4.3. Impacts of the Park on Local Economy ThisThis study study uses uses card card transaction transaction data data to findto find out theout impactthe impact of the of park’s the park’s design design strategy strategy on local on This study uses card transaction data to find out the impact of the park’s design strategy on businesslocal business through through the average the average payment payment amount amount and the and number the number of payments. of payments. As a result As a of result the study, of the local business through the average payment amount and the number of payments. As a result of the instudy, Section in1 ,Section the average 1, the payment average amount payment increased amount yearly increased and the yearly number and of the payments number increased of payments as study, in Section 1, the average payment amount increased yearly and the number of payments well.increased Section as2 well. showed Section that 2 the showed average that payment the average amount payment gradually amount decreased, gradually and decreased, the number and of the increased as well. Section 2 showed that the average payment amount gradually decreased, and the number of payments increased from 2015 to 2016, but decreased from 2016 to 2017. Sections 3 and 4 number of payments increased from 2015 to 2016, but decreased from 2016 to 2017. Sections 3 and 4 show a similar trend. Sections 3 and 4 can be judged to have a positive impact on the economy as the show a similar trend. Sections 3 and 4 can be judged to have a positive impact on the economy as the average payment amount increased from 2015 to 2016 and the number of payments increased. average payment amount increased from 2015 to 2016 and the number of payments increased. However, from 2016 to 2017, the average payment amount decreased and the number of payments However, from 2016 to 2017, the average payment amount decreased and the number of payments decreased. In Section 5, the average payment amount continued to rise. However, the number of decreased. In Section 5, the average payment amount continued to rise. However, the number of payments decreased from 2016 to 2017. payments decreased from 2016 to 2017. Table 3 represents the changes of the average amount of card transactions from 2015 to 2017 by Table 3 represents the changes of the average amount of card transactions from 2015 to 2017 by each section. According to the results, only Sections 1 and 5 increased the average amount of each section. According to the results, only Sections 1 and 5 increased the average amount of transactions since 2015. Sections 2–4 showed that transactions decreased from 2015 to 2017 in the transactions since 2015. Sections 2–4 showed that transactions decreased from 2015 to 2017 in the case of Section 2, or increased from 2015 to 2016 and decreased from 2016 to 2017 in the case of case of Section 2, or increased from 2015 to 2016 and decreased from 2016 to 2017 in the case of Sections 3 and 4. It can be considered that open spaces like Sections 1 and 5 contributed to increase Sections 3 and 4. It can be considered that open spaces like Sections 1 and 5 contributed to increase the amount of transaction money exchanged from 2015 to 2017. Compared to enclosed design the amount of transaction money exchanged from 2015 to 2017. Compared to enclosed design spaces, card users tended to pay more money near the open spaces. Figures 7 and 8 visualized the spaces, card users tended to pay more money near the open spaces. Figures 7 and 8 visualized the transaction changes by using ArcGIS. These figures clearly describe the differences between the transaction changes by using ArcGIS. These figures clearly describe the differences between the sections by years. sections by years.

Table 3. Average amount of transaction from 2015 to 2017 (unit: won). Table 3. Average amount of transaction from 2015 to 2017 (unit: won). Section 2015 2016 2017 Section 2015 2016 2017 1 68,393 87,540 94,470 1 68,393 87,540 94,470 2 42,396 32,761 32,559 2 42,396 32,761 32,559 3 38,586 46,536 42,284 3 38,586 46,536 42,284 4 79,939 91,589 78,183 4 79,939 91,589 78,183 Sustainability 2020, 12, 6722 9 of 12 payments increased from 2015 to 2016, but decreased from 2016 to 2017. Sections3 and4 show a similar trend. Sections3 and4 can be judged to have a positive impact on the economy as the average payment amount increased from 2015 to 2016 and the number of payments increased. However, from 2016 to 2017, the average payment amount decreased and the number of payments decreased. In Section5, the average payment amount continued to rise. However, the number of payments decreased from 2016 to 2017. Table3 represents the changes of the average amount of card transactions from 2015 to 2017 by each section. According to the results, only Sections1 and5 increased the average amount of transactions since 2015. Sections2–4 showed that transactions decreased from 2015 to 2017 in the case of Section2, or increased from 2015 to 2016 and decreased from 2016 to 2017 in the case of Sections3 and4. It can be considered that open spaces like Sections1 and5 contributed to increase the amount of Sustainability 2020, 12, x FOR PEER REVIEW 10 of 13 transactionSustainability 2020 money, 12, x exchanged FOR PEER REVIEW from 2015 to 2017. Compared to enclosed design spaces, card users10 of 13 tended to pay more money near the open5 spaces.49,103 Figures 56,5047 and 861,748 visualized the transaction changes by 5 49,103 56,504 61,748 using ArcGIS. These figures clearly describe the differences between the sections by years. Table 4 represents an interesting implication like Table 3. The authors analyzed the average Table 4 represents an interesting implication like Table 3. The authors analyzed the average number of transactionsTable 3.to Averagetrace how amount frequently of transaction people from use 2015local to businesses 2017 (unit: and won). estimate how many number of transactions to trace how frequently people use local businesses and estimate how many card users contribute to the local economy. Sections 2–5 showed a trend that increased from 2015 to card users contribute to the localSection economy. 2015 Sections 20162–5 showed 2017 a trend that increased from 2015 to 2016 and decreased from 2016 to 2017. However, only Section 1, which has been designed as an 2016 and decreased from 2016 to1 2017. 68,393However, 87,540only Section 94,470 1, which has been designed as an open space, increased from 2015 to 2017 continuously (Figures 9 and 10). It can be considered that open space, increased from 20152 to 2017 42,396continuous 32,761ly (Figures 32,559 9 and 10). It can be considered that the open-space design features may contribute to fostering the local economy compared to enclosed the open-space design features may3 contribute 38,586 to fostering 46,536 the 42,284 local economy compared to enclosed design features such as those in Sections4 2–4. 79,939 91,589 78,183 design features such as those in Sections 2–4. 5 49,103 56,504 61,748

Figure 7. Average transaction amounts of five sections in 2015 (left) and 2016 (right). FigureFigure 7. 7.Average Average transaction transaction amounts amounts of of five five sections sections in in 2015 2015 (left (left) and) and 2016 2016 (right (right).).

FigureFigure 8.8. Average transactiontransaction amountsamounts inin 2017.2017. Figure 8. Average transaction amounts in 2017. Table4 represents an interesting implication like Table3. The authors analyzed the average number of transactions to trace how frequently people use local businesses and estimate how many

Figure 9. Average card transaction of five sections in 2015 (left) and 2016 (right). Figure 9. Average card transaction of five sections in 2015 (left) and 2016 (right). Sustainability 2020, 12, x FOR PEER REVIEW 10 of 13

5 49,103 56,504 61,748

Table 4 represents an interesting implication like Table 3. The authors analyzed the average number of transactions to trace how frequently people use local businesses and estimate how many card users contribute to the local economy. Sections 2–5 showed a trend that increased from 2015 to 2016 and decreased from 2016 to 2017. However, only Section 1, which has been designed as an open space, increased from 2015 to 2017 continuously (Figures 9 and 10). It can be considered that the open-space design features may contribute to fostering the local economy compared to enclosed design features such as those in Sections 2–4.

Sustainability 2020, 12, 6722 10 of 12

Figure 7. Average transaction amounts of five sections in 2015 (left) and 2016 (right). card users contribute to the local economy. Sections2–5 showed a trend that increased from 2015 to 2016 and decreased from 2016 to 2017. However, only Section1, which has been designed as an open space, increased from 2015 to 2017 continuously (Figures9 and 10). It can be considered that the open-space design features may contribute to fostering the local economy compared to enclosed design features such as those in Sections2–4.

Table 4. Average number of transactions from 2015 to 2017.

Section 2015 2016 2017 1 349.0 413.6 424.8 2 605.0 746.3 731.0 3 942.0 1068.7 1038.3 4 621.0 655.2 619.9 Figure 8. Average transaction amounts in 2017. 5 701.0 788.1 739.6

Figure 9. Average card transaction of five sections in 2015 (left) and 2016 (right). Sustainability 2020, Figure12, x FOR 9. PEERAverage REVIEW card transaction of five sections in 2015 (left) and 2016 (right). 11 of 13

Figure 10. Average card transaction amounts in 2017. Figure 10. Average card transaction amounts in 2017. 5. Conclusions Table 4. Average number of transactions from 2015 to 2017. This study examines card transaction data as big data to identify the impacts of a linear park on local business. Many studies have analyzedSection the 2015 economic 2016 benefits 2017 of a park, especially a linear park, in various ways. For example, Kang and1 Cervero349.0 showed 413.6 that424.8 Cheonggyechon impacts on adjacent property values to support the economic2 benefits605.0 of a park746.3 [27].731.0 Curtis measured the economic value of urban green spaces [41]. Compared3 to other942.0 studies, 1068.7 this study1038.3 uses card transaction behavior to trace the changes in local business from4 2015621.0 to 2017. 655.2 The authors619.9 divided the linear park into five sections based on their design features and5 opening701.0 date.788.1 Then, 739.6 they compared each sections’ data by years to identify the relationship between design features and the impacts of the linear park on the adjacent5. Conclusions local economy. TheThis results study strongly examines support card transaction that open spacesdata as design big data contributes to identify to the increase impacts average of a linear transaction park on amountslocal business. and numbers. Many studies Sections have1 and analyzed5 are open the economic spaces like benefits parks. of Other a park, sections especially decreased a linear the park, in various ways. For example, Kang and Cervero showed that Cheonggyechon impacts on adjacent property values to support the economic benefits of a park [27]. Curtis measured the economic value of urban green spaces [41]. Compared to other studies, this study uses card transaction behavior to trace the changes in local business from 2015 to 2017. The authors divided the linear park into five sections based on their design features and opening date. Then, they compared each sections’ data by years to identify the relationship between design features and the impacts of the linear park on the adjacent local economy. The results strongly support that open spaces design contributes to increase average transaction amounts and numbers. Sections 1 and 5 are open spaces like parks. Other sections decreased the average amount and number of transactions from 2015 to 2017; only Sections 1 and 5 showed an increase. It can be considered that open spaces allow card users to contribute to the local business compared to enclosed design spaces like Sections 2–4. This study is helpful to landscape architects and urban designers who design green spaces in urban settings by providing design features which can increase the economic benefits of a park. These results also can be used to monitor economic benefits as a new way of using big data analytics. In terms of limitations, this study used average transaction amounts and numbers instead of tracing individual transaction behavior. A future study may include individual card transaction activity instead of the average data to trim the results precisely.

Funding: This research received no external funding.

Conflicts of Interest: The author declares no conflict of interest.

References

1. Loughran, K. Parks for profit: The high line, growth machines, and the uneven development of urban public spaces. City Community 2014, 13, 49–68. 2. Brander, L.M.; Koetse, M.J. The value of urban open space: Meta-analyses of contingent valuation and hedonic pricing results. J. Environ. Manag. 2011, 92, 2763–2773. Sustainability 2020, 12, 6722 11 of 12 average amount and number of transactions from 2015 to 2017; only Sections1 and5 showed an increase. It can be considered that open spaces allow card users to contribute to the local business compared to enclosed design spaces like Sections2–4. This study is helpful to landscape architects and urban designers who design green spaces in urban settings by providing design features which can increase the economic benefits of a park. These results also can be used to monitor economic benefits as a new way of using big data analytics. In terms of limitations, this study used average transaction amounts and numbers instead of tracing individual transaction behavior. A future study may include individual card transaction activity instead of the average data to trim the results precisely.

Funding: This research received no external funding. Conflicts of Interest: The author declares no conflict of interest.

References

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