Proximity to metro stations and commercial gentrification

Transport Policy 77 (2019) 79–89

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Transport Policy journal homepage: www.elsevier.com/locate/tranpol

Proximity to metro stations and commercial gentrification a,∗

T

b

Jen-Jia Lin , Shu-Han Yang a b

Professor, Department of Geography, National Taiwan University, Taiwan Research Assistant, Department of Geography, National Taiwan University, Taiwan

A R T I C LE I N FO

A B S T R A C T

Keywords: Commercial gentrification Metro system Logit model Google maps street view

This study explores the relationship between newly launched metro stations and commercial gentrification. It focuses on gentrifiable areas near five metro stations in Neihu District, Taipei City, Taiwan. Changes in retailers and restaurants located in the study areas from 2009 to 2015 were observed using logit models to analyze the image records of Google Maps Street View. Empirical evidence revealed that the probability of commercial gentrification increases as the travel distance from metro stations decreases. In addition, the influence ranges of commercial gentrification are approximately 240 m for short-term changes (2009–2012) and 300 m for longterm changes (2009–2015). The influence ranges also vary with the land uses of the station areas. Accordingly, this research suggests that local administrations should take commercial gentrification into account when developing metro systems.

1. Introduction Commercial gentrification occurs when local stores, which are commonly small in size and locally owned, are displaced by upgraded businesses, such as boutique shops, chain stores, or high-priced retailers. Such upgraded businesses usually vitalize the living environment but can also have negative influences on the lives of local residents. The negative influences include depriving local store owners of economic opportunities when they are displaced (Lim et al., 2013), increasing the living cost of economically disadvantaged residents (Wang, 2011), and altering the local sense of a place (Zukin, 2009). Thus, the major driving forces behind commercial gentrification must be understood to handle such effects. Limited studies have explored commercial gentrification. Those published before 2000 presumed the occurrence of commercial gentrification, explored its influences on communities, and developed strategies in response to the phenomenon. Cohen (1983) explored how land-use zoning ordinances can prevent gentrification in neighborhood commercial streets in San Francisco. McDonald (1986) examined 14 gentrified neighborhoods in the USA and clarified whether residential or commercial gentrification affects crime rates. Kloosterman and van der Leun (1999) discussed the meanings and policy responses of the immigrant-driven processes of commercial gentrification in Amsterdam and Rotterdam in the Netherlands. Since 2001, a few researchers have explored whether and why commercial gentrification occurs by investigating the concrete displacement evidence of commercial

∗

activities. Thrash (2001) investigated how commercial gentrification manifested in two neighborhoods in San Francisco and Cambridge. Zukin (2009) examined the relationship between commercial upgrading and residential redevelopment in two gentrifying areas in New York City. Zukin (2009) also argued that living environments change as commercial gentrification occurs in a neighborhood, thereby causing a sense of disruption in the neighborhood. Wang (2011) explored commercial gentrification based on the adaptive reuse of historic dwellings for upscale shopping, dining, and culture in Shanghai, China. Lim et al. (2013) identified the effects of the Cheonggye Stream Restoration Project on the commercial gentrification and displacement of small business clusters in Seoul, South Korea. The aforementioned gentrification studies contributed to an improved understanding of commercial gentrification. However, they have yet to confirm whether other significant driving forces behind commercial gentrification exist. In previous research, urban redevelopment schemes, including revitalization initiatives (Thrash, 2001), residential redevelopments (Zukin, 2009), the reuse of historic dwellings (Wang, 2011), and the restoration of open spaces (Lim et al., 2013), were cited as the major reasons for commercial gentrification. Transportation accessibility has long been identified as one of the major determinants of retail location (Brown, 1993). However, the influences of transportation infrastructures on commercial gentrification have not been explored. To clarify whether newly launched metro stations induce commercial gentrification in nearby areas, a disaggregate study was conducted

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

https://doi.org/10.1016/j.tranpol.2019.03.003 Received 24 June 2018; Received in revised form 6 March 2019; Accepted 6 March 2019 Available online 11 March 2019 0967-070X/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Research process.

for this research by observing each property unit on the ground floor. A total of 701 observations (or property units) were collected near five metro stations that were launched in 2009 in a gentrifiable district in Taipei City, Taiwan. Commercial business displacements in the study areas during 2009–2015 were identified using the image records of Google Maps Street View. Logit models were applied to examine the relationship between the proximity to metro stations and the probability of commercial gentrification. The current research contributes three novel arguments to the literature. First, a metro system also induces commercial gentrification near its stations. Second, the influence range of metro-induced gentrification geographically extends with the increase in the number of years that a metro station has been in operation. Third, the influence ranges of metro-induced commercial gentrification vary with the land use of each station area. The results add further evidence for metro-induced gentrification in terms of commercial activities, complementing Lin and Chung's (2017) survey in Taipei, which focused on residential gentrification.

that core transit users, such as renters and low-income households, are priced out of transit-rich neighborhoods by high-income, car-owning residents who are less likely to use public transit for commuting. Furthermore, Lin (2002) concluded that transit access spurs gentrification, which, in Chicago, has spread like a “wave”, moving from Lake Michigan and downtown Chicago. Plevak (2010) clarified the impacts of light rail transit projects on land use, property values, and demographics in Austin and Dallas, Texas and related these impacts to gentrification. He further proposed a policy of mixed-income transit-oriented developments (TODs) to mitigate gentrification. Feinstein and Allen (2011) examined the effects of the earlier Red Line extension on neighborhood demographics and housing costs in Boston and concluded that gentrification occurred after the extension. Aside from the USA, empirical evidence of metro-induced gentrification has been reported worldwide. Pagliara and Papa (2011) suggested that transit access spurred gentrification based on comparisons of property prices and the number of residents between station catchment areas and control areas in Naples, Italy. Zheng and Kahn (2013) documented evidence that place-based public investments in Beijing, China (including the Olympic Village and two newly launched subway routes), triggered gentrification in nearby areas. Moore (2015) concluded that newly built gentrification and displacement occurred in Bangkok, Thailand, due to mass transit. Grube-Cavers and Patterson (2015) analyzed the data of Montreal, Toronto, and Vancouver in Canada and found that proximity to rail transit and other gentrifying census tracts had a statistically significant effect on gentrification in the first two cities. Lin and Chung (2017) argued that proximity to metro stations in Taipei induced gentrification in inner and outer city areas with different attributes. The empirical findings mentioned above support the positive association between proximity to metro stations and the gentrification of residents. However, these findings neglect the displacement of other metro-related activities. According to previous research, launching metro services improves transportation efficiency (Cervero, 1994), reduces air pollution and energy consumption (Poudenx, 2008), elevates

2. Literature review The literature recognizes the development of metro systems as one of the major driving forces behind gentrification. LeRoy and Sonstelie (1983) theorized a basis for the influences of transit presence on the gentrification of transit-served neighborhoods. They empirically supported their argument according to US data between 1850 and 1977. Since LeRoy and Sonstelie's research, growing empirical evidence for the association between metro systems and gentrification has been reported in the USA. Two studies provide multicity evidence in the country. Kahn (2007) used a 14-city census tract-level panel dataset to document the effects of rail transit expansion. He also suggested that communities receiving increased access to new “walk and ride” stations experience greater gentrification than communities close to new “park and ride” stations. Pollack et al. (2011) studied 42 neighborhoods in 12 metropolitan areas first served by fixed-guideway transit. They found 80

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accessibility (Lewis-Workman and Brod, 1997) and land value (Lin and Hwang, 2004), and relocates populations and industries (Cervero and Landies, 1997) along metro corridors. These changes also lead to high living expenses, especially when the livability and commercialization of a metro corridor increase. These changes further encourage businesses that can afford high land costs to move to the corridors and displace pre-existing local businesses. This class-upward process along metro corridors can be considered metro-induced commercial gentrification. However, existing knowledge on metro-induced gentrification in the literature is limited to residential gentrification. 3. Methods Fig. 1 illustrates the research process of this study. First, the study defined the measure of commercial gentrification. Second, the factors associated with commercial gentrification were identified, and research hypotheses were proposed. To examine the hypotheses, we sampled observations, investigated variable data, and estimated binary logit models. Finally, the hypotheses and implications were discussed in accordance with the estimation results. 3.1. Recognizing commercial gentrification Previous research has provided similar but slightly inconsistent descriptions for commercial gentrification. For example, Thrash (2001) describes commercial gentrification as the process by which locally owned stores are displaced by chain stores. Thrash (2001) also defines commercial gentrification as changing store pricing and catering to high-income customers. Zukin (2009) mentions that “boutiquing”, the phenomenon of boutique shops replacing local shops, is a sign of commercial gentrification. Wang's (2011) study on commercial gentrification is based on the adoptive reuse of historic dwellings for upscale shopping, dining, and culture. In addition, Lim et al. (2013) recognize commercial gentrification as lot-based land-use changes from small industrial firms to offices or commercial uses. On the basis of the aforementioned studies, the present research identified high-priced retailers, restaurants, boutique shops, and chain stores as gentrifying businesses. In this research, we used property units as the subject of observation and classified the uses of observations into three categories. Category A denoted gentrifying businesses, Category B denoted retailers and restaurants that are not in Category A, and Category X denoted users other than retailers and restaurants. If a property unit changed its use from Category B or X in time t to Category A in time t’, then the property unit was recognized as a commercial gentrification observation, and its outcome variable value was coded as 1. Otherwise, the property unit was not recognized as a commercial gentrification observation, and its outcome variable was coded as 0. Given that the outcome was a binary variable, a binary logit model was applied to analyze the study observations. Fig. 2 illustrates an example of outcome coding. The observed property unit in the figure was a local restaurant with a per customer transaction of less than 100 Taiwan dollars (TWD) in 2009 and 2012. The restaurant was displaced in 2015 by a high-priced chain restaurant with a per customer transaction of more than 300 TWD. Hence, commercial gentrification did not occur in short-term changes (maintaining Category B during 2009–2012; outcome = 0) but rather in long-term changes (Category B to Category A during 2009–2015; outcome = 1). All of the sample metro stations opened in July 2009. Thus, this research used the images obtained from Google Maps Street View in February 2009 as the bases for identifying property uses at time t (before the opening of a metro station) and those in March 2012 and March 2015 (short- and long-term changes after the opening of the metro station, respectively) as the bases for recognizing property uses at time t’.

Fig. 2. A study observation and changes in property uses. Source: Google Maps Street View

3.2. Factors associated with commercial gentrification The explanatory variables and their hypothetical associations with commercial gentrification are listed in Table 1. These hypothetical associations were based on previous studies and field interviews. Field interviews with six business owners were conducted in November 2016 near the metro stations being studied. Since the metro stations opened, interviewees [NR1] and [NR2] have operated restaurants that belong to Category B, and interviewee [OS1] has operated a retail store that belongs to Category A. Meanwhile, interviewee [OR1] has operated a restaurant that belongs to Category B, and [OS2] and [OS3] have operated retail stores that belong to Category A since before the metro station opened.

3.2.1. Distance from metro station The explanatory variables consisted of two groups, namely, the metro variable and controls. The metro variable was measured by the shortest travel distance from a property unit to the nearest metro station. This variable is hypothesized to be negatively related to the possibility of commercial gentrification. Brown (1993) found that transport accessibility has long been identified as one of the major determinants of retail location. Retailers and restaurant owners mostly find locations with good accessibility to raise their exposure to potential customers. Newly launched metro stations significantly elevate the accessibility of surrounding areas (Du and Mulley, 2006; Lewis-Workman and Brod, 1997; Martinez and Viegas, 2009), thus inducing nonlocal entrepreneurs to find opportunities for setting up businesses within metro station areas. According to Alonso's (1964) bid rent theory, property owners tend to rent out their properties to bidders who are willing to pay the highest rent. Gentrifying businesses mostly have higher rent 81

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Table 1 Definitions of the explanatory variables and their hypothetical associations. Variables Metro MetroDisti Comi Offi Controls LandValueSi LandValueLi IncomeSi IncomeLi EducaSi EducaLi DenstSi DenstLi FloorSi FloorLi RoadWidthi InterDisti BusDisti PbsDisti

Definitions

Units

Hypothetical association with the outcome

The shortest travel distance from property unit i to the nearest metro station after 2009 Property unit i is (=1) or is not (=0) located in commercial areas that were developed earliera Property unit i is (=1) or is not (=0) located at office areasb

m na na

– na na

Short-term changec of the land value of property unit i Long-term changed of the land value property unit i Average household income in the areae near property unit i in 2012 Average household income in the area near property unit i in 2015 Short-term change of college graduate ratio in the area near property unit i Long-term change of college graduate ratio in the area near property unit i Short-term change of population density in the area near property unit i Long-term change of population density in the area near property unit i Short-term change of floor area in the area near property unit i Long-term change of floor area in the area near property unit i Road width in front of property unit i The shortest travel distance from property unit i to the nearest intersection in 2015 The shortest travel distance from property unit i to the nearest bus stop in 2015 The shortest travel distance from property unit i to the nearest public bike rental station in 2015

NT$/m2 NT$/m2 103 NT$ 103 NT$ na na Person/km2 Person/km2 m2 m2 m m m m

+ + + + + + + + + + + – – –

a

The area near Neihu and Donghu metro stations. Neihu Technology Park. c Short-term change means the change from 2009 to 2012. d Long-term change means the change from 2009 to 2015. e The area near property unit i means the area within a 400-m buffer-ring using travel distance on practical road network and center of the property unit i travel distance on practical road network and center of the property unit i. b

gentrification geographically extends with the increase in the number of years that a metro station has been in operation. To examine H2, two outcome variables (short- and long-term changes) were applied in the model estimations. Finally, the influence range of metro-induced commercial gentrification may vary with local land use. For example, in a commercial area that was developed earlier, the influence range of metro-induced commercial gentrification was shorter than that of other land uses. Such a finding was attributed to the already high property rent and gentrifying businesses that existed before the metro station opened. As stated by a boutique shop owner near the BR19 metro station, which is located in a major commercial district,

affordability than local nongentrifying businesses. Hence, the displacement of the latter by the former can occur in areas near newly launched metro stations. Given that accessibility declines as the distance from a metro station increases, the possibility of commercial displacement can likewise decrease as the distance from a metro station increases. The following field interviews confirmed the above inference. Transport accessibility is a very important concern for location evaluation. Any newly launched transport system that significantly improves accessibility raises exposure to potential customers and is beneficial to businesses nearby. [OS3] The rents of commercial properties next to metro stations are over a hundred thousand dollars per Pin. Wow! It is not affordable for all businesses. Only business owners who are confident of their profits dare to rent the properties near metro station. [NR2] (Note: The Pin is a Taiwanese area unit, which is approximately equal to 3.3 m2.)

My store has been here for two decades. I selected the location because this is the earliest and liveliest commercial center in Neihu District. The opening of a metro station resulted in a slight change here. Transport accessibility and property rents have been superior to other areas in Neihu for many years. [OS3]

Commercial property rents near metro stations are too expensive for general renters. Only big enterprises and chain stores can afford rents there. [OS2]

Meanwhile, areas with office buildings are major areas for gentrifying retailers and restaurants, as middle-class and wealthy customers gather there on weekdays. The number of customers is an important criterion in the selection of a retail location (Arnold et al., 1983; Berry, 1988; Kuo et al., 2002). In addition to the proximity of metro stations, gentrifying businesses have other location options where office buildings are nearby, thus ensuring a steady stream of customers. Therefore, metro-induced commercial gentrification should be less important in office areas than in areas with other land uses. The field interviews support the inference presented above.

Accordingly, the present study proposes the following hypothesis: H1. Proximity to a newly launched metro station induces commercial gentrification. To examine H1, the explanatory variable of MetroDisti defined in Table 1 was applied in the model estimations. Given that the possibility of commercial gentrification decreases as the distance from a metro station increases, the influence of the metro station with commercial gentrification should be limited within a specific range. However, the influence can geographically extend over time. The geographical extension of the influence range may be attributed to two factors. First, the displacement of the renters may take a few years to be completed because of lease contract restrictions. Second, pioneer gentrifying businesses have occupied available property units near metro stations. Thus, other gentrifying businesses are forced to look for locations that are farther from metro stations. Jones and Lucas (2012) regard gentrification as a long-term social impact of transport decision-making. Therefore, the second hypothesis is as follows: H2. The influence range of metro-induced commercial

The major reason for me to select this location is the approaching office workers. This location is not close to the metro station; however, you can see a lot of office buildings nearby. [NR1] Well, office buildings are distant from here, and their workers seldom have their lunch breaks here. [OR1] Accordingly, the present research proposes the third hypothesis: H3. The influence ranges of metro-induced commercial gentrification vary with the land uses of station areas. To examine H3, the dummy variables of Comi and Offi defined in 82

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capita (CPC) are below the city average. In 1996, the average CPC in Taipei was 1.217 million TWD. Eight districts had a CPC below the city average and were thus recognized as commercially gentrifiable areas. In comparison, Neihu District's CPC was 0.274 million TWD in 1996, which was far below the city average. Fig. 3 shows the seven metro stations coded from BR16 to BR22 in Neihu. All seven metro stations opened in July 2009. The areas near BR18 and BR20 were excluded from the survey areas because they were mostly occupied by parks and green spaces. Thus, the survey areas consisted of those within a walking distance of 400 m from the exits of BR16, BR17, BR19, BR21, and BR22. This study selected observations from the image records of Google Maps Street View in February 2009 by following a systematic random sampling approach. An observation was randomly selected among the first three property units at the beginning of a street. One observation was selected for every three property units along the street. The selected observations that had image records in February 2009, March 2012, and March 2015 were considered effective observations. A total of 701 effective observations were successfully collected via the survey. The geographical distributions are illustrated in Fig. 4. Among the observations, 65 observations (9%) in short-term changes and 111 observations (16%) in long-term changes were considered commercial gentrification cases. Fig. 4 reveals that more commercial gentrification cases in long-term changes exist than those in short-term changes for every survey station area. Moreover, some observed commercial gentrification cases are partially close to the metro stations, and others are partially distant from the metro stations. Such distributions imply that metro-induced gentrification can be limited within a geographical range and that a quadratic relationship can exist between the distance from the metro station and the probability of commercial gentrification. Thus, this research simultaneously used the original and quadratic terms of MetroDisti as explanatory variables in the model estimations. The variable data came from multiple existing databases. We measured the shortest travel distances to the nearest metro station, intersection, bus stop, and public bike rental station using the Roadway Network Digital Map System from the Ministry of Transportation and Communications and ArcGIS 10.1 software package. The announced land value data were obtained from the Department of Land Administration, Taipei. Household income data were extracted from the Individual Income Tax Database of the Taxation Administration, Ministry of Finance. Education and population data came from the Social and Economic Database of the National Geographic Information System. Floor area data were obtained from the House Tax Database of the Taipei City Revenue Service. Finally, road width data were sourced from the Land-use Zoning Database of the Department of Urban Development, Taipei. Table 2 summarizes the descriptive statistics of the explanatory variables. All variables have reasonable distribution intervals, and most variables reveal slightly right-skewed distributions. The variation coefficients for most variables are adequate for the regression analysis, whereas EducaSi and EducaLi reveal small variations. Approximately one-third of the effective observations were located in commercial areas that were developed earlier, and only 8% were located in office areas.

Table 1 were used as interaction terms with MetroDisti in the model estimations. 3.2.2. Controls In addition to the metro variable, this study selected three variable groups as controls to explain commercial gentrification. The first group contains variables of land value changes to represent property rents. No reliable rent record for each property unit in Taiwan is available. Thus, the announced land value for each property unit from the government was used as the representative variable of rent. The announced land value is annually renewed for taxation and is positively associated with property rent. Increasing property rent was recognized as a major driving force for commercial gentrification in the studies by Thrash (2001) and Zukin (2009) and as indicated in the responses of interviewees [OS1], [NR1], and [NR2]. The second variable group consists of demographic attributes near an observed property unit. Household income and being a college graduate are two socioeconomic attributes that are positively associated with gentrifiers in previous research (e.g., Kahn, 2007; Zukin, 1987). An increase in gentrifiers in an area can attract gentrifying businesses to move to the area. Therefore, the present research hypothesizes that the two socioeconomic attributes and commercial gentrification are positively related. Population density and floor area are also related to potential customers, including residents and visitors. Zukin (2009) concluded that more boutiques and chain stores open when population density is great and available stores are large. As a result, rents are increased above the level that many local businesses can afford. Accordingly, the current research hypothesizes that changes in population density and floor area are positively related to commercial gentrification. The third variable group is associated with the transportation attributes of property units. As mentioned in the previous subsection, transport accessibility has long been identified as a major determinant of retail location (Brown, 1993). In addition to the metro system, the present research further considers road width and distances to the nearest intersections, bus stops, and public bike rental stations. Increasing road width in front of a property unit and decreasing distances from a property unit to transport facilities implies that the property unit has good accessibility and, therefore, a high possibility of commercial gentrification. 4. Data As an emerging city in East Asia after World War II, Taipei has been developing its metro system for more than 20 years. The Taipei Metro System was launched in 1996 with one line and 12 stations and was continuously expanded into five lines and 108 stations by the end of 2017. Fig. 3 illustrates the map of Taipei and its current metro system network, which has a length of 131.1 km. Taipei City, the center of the Taipei Metropolitan Area, consists of 12 districts and accommodates more than 2.7 million residents. Lin and Chung (2017) confirmed the relationship between metro stations and residential gentrification in Taipei. The present research aims to further explore the process of commercial gentrification. Taipei's experiences are meaningful to other emerging East Asian cities that have been developing their metro systems throughout the past few decades. The current research selected the Neihu District as the study area (Fig. 3). This area became commercially gentrifiable in 1996, the same year the metro system was launched. Freeman (2005), Hammel and Wyly (1996), and Walks and Maaranen (2008) argued that an area must be considered gentrifiable at the beginning of the analysis period for it to become susceptible to gentrification. From the viewpoint of residential gentrification, a gentrifiable area reveals a below-average social status that can be measured through income, education, or professional occupation. As such, the present research recognized commercially gentrifiable areas as those in which commercial products per

5. Results The present research applied binary logit models and the NLOGIT 6 software package to estimate the relationships between the explanatory variables and binary outcomes. Explanatory variables with a coefficient significance below the confidence level of 1-α = 80% in all models were excluded. Confidence levels of 99%, 95%, or 90% were indicated for each variable in the estimation results to represent different significant levels. Four models were estimated for each outcome: the base model considered only the controls; the expanded model 1 considered the controls and MetroDisti variables for testing H1 and H2, respectively; 83

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Fig. 3. Study areas and metro stations in Taipei City.

values are between 0.0359 and 0.1108) are consistent with the expectations shown in Table 2. In contrast, the negative coefficients of household income, which are significant at α = 0.01 or 0.05, are contrary to the expectation. This inconsistency may be attributed to the use of this research on income levels in 2015 rather than the use of income changes from 2009 to 2015, as household income data for minimum statistical areas in 2009 are unavailable in Taipei. The negative coefficients of MetroDisti (p-values are between 0.0041 and 0.0325) and the positive coefficients of MetroDisti2 (p-values are 0.1115 and 0.1275) in the expanding models imply that a convex curve relationship also exists between the distance from the metro station and the probability of commercial gentrification in the long-term changes. Furthermore, the significant coefficients of MetroDisti × Comi, MetroDisti2 × Comi, and MetroDisti × Offi2 (p-values are between 0.0202 and 0.0408) in the expanded models 2 and 3 imply that the relationships between the distance from the metro station and probability of commercial gentrification in commercial areas that were developed earlier and office areas are significantly different from those in areas with other land uses in terms of long-term changes. The values of the variance inflation factor (VIF) listed in Tables 3 and 4 suggest that collinearity exists only between MetroDisti and MetroDisti2. These high correlations should be fine in this research because they are not related to the coefficient estimations of other explanatory variables (Yoo et al., 2014). Moreover, the coefficients of MetroDisti and MetroDisti2 are always jointly explained in this research. The estimation results indicate that H1 is supported by the sample data; that is, proximity to a newly launched metro station induces commercial gentrification in Taipei. Such gentrification occurs within a certain influence range from metro stations. To clarify how the influence ranges of metro-induced commercial gentrification differ between short- and long-term changes and among different land uses, the present research charted Fig. 5 using the estimated models and the means of explanatory variables among the observations. Fig. 5(a) and (b) reveal that the influence ranges extend from approximately 240 m in short-term changes to 300 m in long-term changes. Fig. 5(c)–(f) also present longer influence ranges in short-term

the expanded model 2 considered the controls and the interaction terms of MetroDisti and Comi; and the expanded model 3 considered the controls and the interaction terms of MetroDisti and Offi. The last two expanded models were estimated for testing H3. We applied likelihood ratio tests to examine whether the expanded models have superior goodness-of-fit relative to the base model. Table 3 lists the binary logit models for commercial gentrification in short-term changes. The χ2 test results of the models are all significant at α = 0.05 or 0.1, but the ρ2 values are low. The likelihood ratio tests of the three expanded models are all significant at α = 0.05 or 0.1. Thus, using travel distances from the metro station to explain commercial gentrification is meaningful in terms of improving goodness-offit. All significant coefficients of controls, which are significant at α = 0.1, are positive and consistent with the expectations shown in Table 2. The negative coefficients of MetroDisti (p-values are between 0.0154 and 0.0343) and the positive coefficients of MetroDisti2 (p-values are between 0.0331 and 0.0756) in the expanding models imply that a convex curve relationship exists between the distance from the metro station and the probability of commercial gentrification. The probability of commercial gentrification first decreases as the distance from the metro station increases and then increases at a certain threshold distance. This threshold distance can be viewed as the influence range of metro-induced gentrification. The positive coefficients of MetroDisti2 × Comi (p-value = 0.0338) in the expanded model 2 and MetroDisti × Offi (p-value = 0.0114) in the expanded model 3 likewise imply that the relationship between the distances from the metro station and the probability of commercial gentrification in commercial areas that were developed earlier and office areas are significantly different from those in areas with other land uses. Table 4 lists the binary logit models for commercial gentrification in long-term changes. The χ2 test results and ρ2 values of the models are worse than those of the short-term changes presented in Table 3. However, the likelihood ratio tests of the three expanded models are all significant at α = 0.05. Therefore, using the travel distance from the metro station to explain commercial gentrification is also meaningful in terms of long-term changes. The positive coefficients of RoadWidthi (p84

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Fig. 4. Geographical distributions of study observations. (a) Short-term changes (2009–2012); (b) Long-term changes (2009–2015).

uses. Fig. 5(e) and (f) reveal that the influence range in office areas is significantly less than that in areas with other land uses. Hence, H3 is also supported by the sample data, namely, that the influence ranges of metro-induced commercial gentrification vary with the land uses of station areas in Taipei.

changes than those in long-term changes. Therefore, H2 is supported by the sample data; in other words, the influence range of metro-induced gentrification geographically extends with the increase in the number of years a metro station in Taipei has been in operation. Fig. 5(c) and (d) also reveal that the influence range in commercial areas that were developed earlier is significantly less than that in areas with other land 85

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results in different job opportunities and a different sense of place, which can affect the economic ability and community identity of current local residents (Wang, 2011; Zukin, 2009). To prevent such an observation from negatively affecting local business owners and residents, the local administration should consider implementing interventions in metro development schemes, such as providing affordable commercial spaces or rent subsidies to locally owned small businesses. The second implication is related to the influence range of metroinduced commercial gentrification. Fig. 5 illustrates that the influence ranges are between 200 and 300 m from the metro stations. The ranges are smaller than the survey areas of this research (400 m from stations), most transfer distances of metro passengers in Taipei, and the influence distances in previous studies on the impacts of metro systems. The study sample of Lin et al. (2017) in Taipei reveals that approximately 73% of the transfer distances of metro passengers to final destinations are over 500 m, and the mean and median transfer distances are 982 and 814 m, respectively. Moreover, the influence of the distances of rail stations on property values are, for example, 1/4 mile in Debrezion et al. (2007), 500 m in Pagliara and Papa (2011), 800 m in Cervero and Duncan (2002) and Weinberger (2001), and 1.44 km in Ko and Cao (2013). Such short-term influence ranges of commercial gentrification must be considered in developing local administration-led intervention schemes. For example, if the local administration plans to provide rent subsidies to locally owned small businesses near newly launched metro stations, then the influence ranges would be a meaningful reference for determining a geographic boundary for rent subsidization. Lin and Cheng (2016) indicated that most rent subsidy programs, such as those implemented in the USA (Galster and Zobel, 1998), Europe (Priemus and Boelhouwer, 1999), and Asia (Moriizumi, 1993; Cheng, 2011), determine rent subsidies based only on the socioeconomic conditions of the applicants. Lin and Cheng (2016) argue that the geographical attributes of a subsidy object are worth considering in designing an effective rent subsidy program. The third implication explains the right (increasing) parts of the convex curves in Fig. 5. These increasing parts should not be affected by metro stations but are related to land use. Fig. 5(c)–5(f) reveal that the increasing curves after the turning points (changing rates = 0) of the observations in commercial areas and office areas that were developed earlier are significantly higher than those of the observations in areas with other land uses. The turning points of the observations in

Table 2 Descriptive statistics of the explanatory variables. Variables

Metro MetroDisti Comi Offi Controls LandValueSi LandValueLi IncomeSi IncomeLi EducaSi EducaLi DenstSi DenstLi FloorSi FloorLi RoadWidthi InterDisti BusDisti PbsDisti

Minimum

Maximum

Mean

Variation coefficient

3.44 400.00 275.97 Comi = 1: 32.95%; Comi = 0: 67.05% Offi = 1: 8.00%; Offi = 0: 92.00%

259.73

0.37

16,800.00 40,400 918.90 947.83 0.03 0.07 −4035.99 −4031.82 2498.32 7423.56 4.00 0.00 0.30 1.63

44,342.05 123,959.70 1258.08 1320.81 0.05 0.08 1187.72 1727.27 28,813.87 48,791.25 14.94 25.20 130.27 381.36

0.35 0.36 0.20 0.21 0.09 0.07 1.61 1.23 0.88 0.69 0.57 0.68 0.66 0.64

124,000.00 305,000 2243.28 2460.58 0.07 0.10 6743.78 7310.89 84,616.05 164,849.60 30.00 139.94 422.64 1130.64

Median

39,000 106,028.00 1172.29 1250.37 0.05 0.08 820.20 1394.85 22,023.94 35,003.81 12.00 20.88 121.86 301.76

6. Discussion Corresponding to the existing literature, the empirical evidence in this research is associated with three implications. First, the results mirror the positive relationship between property rent and commercial gentrification raised in the study of Zukin (2009) in New York. The confirmed hypotheses, H1 and H2, denote the spatiotemporal attribute of commercial gentrification. Specifically, the probability of commercial gentrification decreases with the increase in the distance from a metro station and increases with the increase in the number of years since a metro station opened. The commercial property rents in the study areas are consistent with this attribute. According to the Real Estate Transaction Database (Ministry of Interior in Taiwan, 2017), the contract rent of a commercial property near metro station BR18 increased from 1903 TWD/Pin in 2012 to 2036 and 2042 TWD/Pin in 2013 and 2016. In addition, the rental rates of two commercial properties, which were 138 and 282 m from the metro station BR19, were 2339 and 932 TWD/Pin, respectively. Such market-led gentrification

Table 3 Binary logit models (outcome: commercial gentrification in short-term = 1). Explanatory variables

Base model

Expanded model 1

Coefficient

p-value

Constant EducaSi DenstSi FloorSi RoadWidthi MetroDisti MetroDisti2 MetroDisti2 × Comi MetroDisti × Offi

−5.8880a 56.2291b 0.0002b 8.1984d 0.0287c – – – –

0.0010 0.0488 0.0302 0.1626 0.0517 – – – –

LL(β) ρ2 χ2

−211.4039 0.0234 10.1254c

Likelihood ratio test

VIF

1.283 1.113 1.298 1.010

Coefficient

p-value

−4.5986a 56.9991c 0.0002b 7.5638 0.0230d −12.1683b 24.8437b – –

0.0043 0.0506 0.0275 0.2012 0.133 0.0228 0.0331 – –

Expanded model 2 VIF

1.293 1.114 1.305 1.063 21.534 21.309

Expanded model 3

Coefficient

p-value

VIF

Coefficient

p-value

−3.7638b 31.0916 0.0002a 12.9992b 0.0239d −11.4612b 21.2707c 8.6459b –

0.0243 0.3300 0.0041 0.0439 0.1195 0.0343 0.0756 0.0338 –

1.435 1.405 1.546 1.064 21.534 21.440 1.964

−4.2934a 57.8374b 0.0002b 1.9030 0.0189 −13.0658b 24.7948b – 3.6212b

0.008 0.0482 0.0287 0.7736 0.2235 0.0154 0.0352 – 0.0114

−208.9716 0.0346 14.9900b

−206.7169 0.0450 19.4994b

−206.0309 0.0482 20.8714a

4.8646c (χ2 2,0.1 = 4.605)

9.3740b (χ2 3,0.05 = 7.815)

10.7460b (χ2 3,0.05 = 7.815)

Note: VIF, variance inflation factor (a measure of multicollinearity). a significant at α = 0.01. b significant at α = 0.05. c significant at α = 0.1. d significant at α = 0.2. 86

VIF

1.293 1.114 1.465 1.077 21.601 21.309 1.202

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Table 4 Binary logit models (outcome: commercial gentrification in long-term = 1). Explanatory variable

Base model Coefficient

IncomeLi RoadWidthi MetroDisti MetroDisti2 MetroDisti × Comi MetroDisti2 × Comi MetroDisti × Offi2

−0.0015 0.0238b – – – – –

LL(β) ρ2 χ2

−307.8761 0.0000 0.0000

Likelihood ratio test

a

Expanded model 1 p-value 0.0000 0.0359 – – – – –

VIF

Coefficient

1.017 1.017

−0.0008 0.0216c −7.6979b 13.1743d – – – b

Expanded model 2

p-value 0.0130 0.0612 0.0325 0.1115 – – –

VIF

Coefficient

1.021 1.068 21.513 21.315

−0.0009 0.0210c −3.0193a – −7.0460b 22.4107b – a

p-value 0.0005 0.0682 0.0041 – 0.0408 0.0329 –

Expanded model 3 VIF

Coefficient

p-value

VIF

1.039 1.065 1.394

−0.0007 0.0186d −8.1475b 12.745d – – 7.5458b

0.0303 0.1108 0.0246 0.1275 – – 0.0202

1.045 1.077 21.519 21.333

20.230 21.408

b

−304.0116 0.0074 4.5326

−302.9589 0.0108 6.6381d

−301.5436 0.0155 9.4685c

7.7289b (χ2 2,0.05 = 5.991)

9.8344b (χ2 3,0.05 = 7.815)

12.6648b (χ2 3,0.05 = 7.815)

1.069

Note: VIF, variance inflation factor (a measure of multicollinearity). a significant at α = 0.01. b significant at α = 0.05. c significant at α = 0.1. d significant at α = 0.2.

commercial areas that were developed earlier and office areas are also significantly closer to the metro stations than those of the observations in areas with other land uses. These results indicate that the influence ranges of metro-induced commercial gentrification in commercial areas that were developed earlier and office areas are more limited than those in areas with other land uses. The increasing curves in Fig. 5(a) and (b) are caused by the types of land use rather than by metro stations. The observations of commercial areas that were developed earlier come from the station areas of BR19 and BR22, which are located at the western and southern parts of the existing commercial centers, respectively, at a distance of approximately 500 m. The observations were conducted in the office areas of Neihu Technology Park, which is located south of BR16 and BR17. Proximity to customers is also one of the major determinants in selecting a retail location (Brown, 1993). Hence, the existing commercial and employment centers should account for the right parts of the convex curves in Fig. 5. Given that land uses affect the influence ranges of metro-induced commercial gentrification, local administration-led interventions should vary depending on different land uses. Two practical approaches of local administration intervention recorded in the literature mitigate the negative effects of metro-induced commercial gentrification. In the first approach, the government directly provides affordable spaces to locally owned small businesses. Module 7 of the e-learning course titled Transit-Oriented Development at a Corridor Scale (World Resources Institute and World Bank, 2017) presents an inclusive TOD approach by providing affordable housing and developing local economies near metro corridors. To prevent the negative effect of new developments on local businesses, the Singapore government established hawker centers near metro stations where locally owned small businesses can legally operate in affordable spaces. This strategy helps existing workers adjust to new job demands by developing suitable job opportunities and training while simultaneously responding to the needs of TOD communities. Another approach is developing corresponding measures through a negotiation process between local communities and metro developers. Boston's community benefits agreement (CBA) for the extension of the metro Green Line is a typical example of this approach, and the project's details can be found in the study of Feinstein and Allen (2011). A CBA is a contract negotiated between community groups and a prospective developer in which the developer agrees to provide particular community benefits related to the project in exchange for the community's support. In this context, property rental subsidies, affordable business spaces,

Fig. 5. Relationships between the distance to the metro station and the probability of gentrification. 87

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finding, therefore, broadens our understanding of how transportation infrastructures are related to gentrification. The empirical results also remind local administrations to implement intervention measures to prevent negative effects of metro-induced commercial gentrification on local business owners and workers. Further studies on the relationships between metro systems and commercial gentrification are needed to examine three issues that are reflected in the limitations of this research. First, the empirical findings of the present research should be applicable to emerging cities worldwide, which may also be developing their own metro systems and have similar socioeconomic contexts with Taipei. As the first investigation on metro-induced commercial gentrification in the literature, this research reports preliminary knowledge. Further explorations in different cities with various contexts in terms of economy, culture, history, and regime are necessary. Second, in addition to metro services and accessibility improvement, areas near metro stations can accompany TOD schemes. TOD schemes develop station areas into dense, mixed-use, and pedestrian- and bike-friendly environments, thus causing neighborhood revitalization and property value increase (Transportation Research Board, 2004). As argued by Jones and Ley (2016), Kahn (2007), and Rayle (2015), the changes presented above further induce residential gentrification. However, TOD-related commercial gentrification has not been examined before and must be further explored in the future. Finally, the current study selected control variables in accordance with the literature review, field interviews, and available sample data. The estimated binary logit models in Tables 3 and 4 contain few significant controls, reveal low goodness of fit, and denote that the models could miss important explanatory variables. Thus, future studies should apply further comprehensive controls to prevent biases resulting from missing important explanatory variables. Acknowledgements This research is financially supported by Ministry of Science and Technology in Taiwan (MOST104-2410-H-002-229-MY3) and NTU Research Center for Future Earth (NTU-107L901004). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.tranpol.2019.03.003. References Alonso, W., 1964. Location and Land Use: toward a General Theory of Land Rent. Harvard University Press, Cambridge. Arnold, D.R., Capella, L.M., Smith, G.D., 1983. Strategic Retail Management. AddisonWesley, Boston. Berry, B.J., 1988. Market Centers and Retail Location: Theory and Applications. Prentice Hall, New Jersey. Brown, S., 1993. Retail location theory: evolution and evaluation. Int. Rev. Retail Distrib. Consum. Res. 3 (2), 185–229. Cervero, R., 1994. Transit-based housing in California: evidence on ridership impacts. Transport Pol. 1 (3), 174–183. Cervero, R., Duncan, M., 2002. Transit's value-added effects: light and commuter rail services and commercial land values, Transportation Research Record. Journal of the Transportation Research Board 1805, 8–15. Cervero, R., Landies, J., 1997. Twenty years of the bay area rapid transit system: land use and development impacts. Transport. Res. Part A 31 (4), 309–333. Chen, I.L., 2011. New prospects for social rental housing in Taiwan: the role of housing affordability crises and the housing movement. International Journal of Housing Policy 11 (3), 305–318. Cohen, M., 1983. San Francisco's neighborhood commercial special use district ordinance: an innovative approach to commercial gentrification. Gold. Gate Univ. Law Rev. 13, 367–398. Debrezion, G., Pels, E., Rietveld, P., 2007. The impact of railway stations on residential and commercial property value: a meta-analysis. J. Real Estate Finance Econ. 35 (2), 161–180. Du, H., Mulley, C., 2006. Relationship between transport accessibility and land value: local model approach with geographically weighted regression. Transport. Res. Rec.: Journal of the Transportation Research Board 1977, 197–205. Feinstein, B.D., Allen, A., 2011. Community benefits agreements with transit agencies:

Fig. 5. (continued)

living-wage jobs, and local hiring programs can be beneficial to existing local business owners and workers. 7. Conclusion This research argues and empirically confirms the following hypotheses: proximity to a newly launched metro station induces commercial gentrification, the influence range of metro-induced commercial gentrification geographically extends as the number of years a metro station has been in operation increases, and the influence ranges of metro-induced commercial gentrification vary with the land uses of different station areas. Specifically, the influence ranges of metro-induced commercial gentrification in commercial or office areas are significantly shorter than those in areas with other land uses. To the best of our knowledge, such evidence does not exist in the extant research. This 88

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