Neighborhood form and residents' walking and biking distance to food markets: Evidence from Beijing, China

Transport Policy xxx (2017) 1–10

Contents lists available at ScienceDirect

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

Neighborhood form and residents' walking and biking distance to food markets: Evidence from Beijing, China Yulin Chen School of Architecture, Tsinghua University, Room 500, Beijing 100084, China

A R T I C L E I N F O

A B S T R A C T

Keywords: Neighborhood form Food market Walk Bike Distance Catchment

This paper examines residents' walking and biking patterns for food shopping in China and the influence of neighborhood form and market characteristics on effective walk/bike catchment areas of food markets. Based on 1417 resident surveys at 15 food markets in three neighborhoods in the city of Beijing, we found that residents rarely shop at the nearest food stores and prefer larger farmer's markets and supermarkets instead. And they travel in much shorter distance compared with residents in the western cities. An ordinary least squares regression analysis reveals that, after controlling for the effect of personal socioeconomic and trip characteristics, traditional neighborhoods with dense street networks, mixed land uses and ample tree shades present the largest walk/bike catchment area, followed by enclave neighborhoods and superblock neighborhoods. In addition, markets with larger size, ground-level and adjacency to a park are significantly associated with longer walking and biking distances.

1. Introduction Since the late 1970s, cities in China have undergone rapid expansion and motorization thanks to continuing economic growth and urbanization. As car traffic keeps taking up an increasing amount of economic, social and environmental resources, the spread of car-oriented development in cities and neighborhoods has made it a critical challenge to achieve livability and sustainability (Zhao, 2010; Pan et al., 2009; Chen et al., 2008; Jiang et al., 2015). One of the most striking pieces of evidence is that walking and cycling has declined dramatically in most Chinese cities (Darido et al., 2013; Zhao, 2014). Using Beijing as an example, the cycling mode share has dropped from 62.7% in 1986 to 30.3% in 2005, and further down to 11.3% in 2014 (BTRC, 2015). Such an undesirable trend is partially due to the growth of car ownership and longer travel distances; but just as importantly, this has to do with deteriorating neighborhood environments for pedestrians and cyclists, which makes walking or cycling no longer a viable or competitive option, even for short distance trips. “Driving a car to buy soy sauce” has become a popular saying among Chinese people to describe the increasingly excessive usage of cars in their cities. Among various types of non-work trips in cities, going to the food market is basic and necessary for most of China's urban population. Over the last decade western scholars have proved that access to food sources within the built environment can exert a critical influence on people's

diet quality, body weight, and other health related outcomes (Morland et al., 2002, 2006; Laraia et al., 2004; Inagami et al., 2006; Moore et al., 2008; Rundle et al., 2009; Kestens et al., 2010; Laska et al., 2010; Michimi and Wimberly, 2010; Powell et al., 2010; Gibson, 2011). Similar evidence is also found in China (Wang and Shi, 2012). As argued by Clifton (2004), “adequate mobility plays an important role in the economic well-being of families. Although access to non-work destinations has not received much attention from the policy arena, understanding the role of transportation in the acquisition of food resources is vital to crafting more comprehensive antipoverty strategies that go beyond addressing hunger and focus on self-sufficiency.” Understanding the context of food shopping travels is critical prior to conducting research or formulating policy in the field. In the USA, the majority of the population drives to supermarkets to shop for food. According to the 2003–2007 American Time Use Survey, even in lowincome areas with high supermarket access, 65.3% of households drove or were driven to those markets (USDA, 2009). When it comes to China, that context is different. On the demand (consumer) side, currently China is still at a much lower motorization level than developed countries. Walking to food market is sometimes even a type of recreation ways (e.g., exercise, meeting friends) for many residents, especially for retired people. Also importantly, Chinese people place a greater emphasis and desire on the freshness of food, a tradition that can be traced back to the Confucius era more than 2300 years ago (Passmore and Reid, 1982;

E-mail address: [email protected]. https://doi.org/10.1016/j.tranpol.2017.09.015 Received 16 November 2016; Received in revised form 9 August 2017; Accepted 20 September 2017 Available online xxxx 0967-070X/© 2017 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Chen, Y., Neighborhood form and residents' walking and biking distance to food markets: Evidence from Beijing, China, Transport Policy (2017), https://doi.org/10.1016/j.tranpol.2017.09.015

Y. Chen

Transport Policy xxx (2017) 1–10

(2011) found that street density, quick-service restaurants around homes and non-chain grocery stores near the primary grocery store used were positively related to non-driving mode choices for food shopping trips. Household surveys from Berlin in Germany and in the central Puget Sound region in the US have shown that the proximity of grocery stores from home are associated with lower car usage for shopping trips and the increased probability of using nonmotorized travel modes (Holz-Rau, 1991; Hess et al., 1999). Residential density, employment density and land use mix were also found to be negatively correlated with car usage for shopping trips but were positively related to walking and/or transit (Frank and Pivo, 1994; Moudon et al., 2006; Scheiner and Holz-Rau, 2007). When it comes to food market access patterns in China, we would intuitively expect the built environment to exert some influence on a food market's catchment area, given that a continuously growing base of research consistently reveals associations between shopping travel behavior and the built environment. Unfortunately, the aforementioned existing studies have focused primarily on general-purpose shopping. Another limitation has to do with the fact that in those empirical studies based in the US and Europe, the majority of respondents were car owners, which skewed the data set highly, and concentrated on car driving modes (Aggarwal et al., 2014). This challenges the generalization of conclusions for less car-oriented settings, such as China, India and other developing countries. For example, Rahul and Verma (2014) found that the walking distance for commuting in Bangalore, especially for those who do not own a private vehicle, is longer than the distance travelled in countries like the USA, Canada or Germany. In China's context, although the development of motorization and transport infrastructures have grown at a fast pace over last decades, non-motorized transportation remains a significant mode share in most cities (Darido et al., 2010). This is likely to be particularly true for food shopping trips, since they tend to be completed in short distances. Additionally, the Chinese government has been prioritizing food access for people on its political agenda, by implementing a 15-minute Living Circle programme at a community level. In addition to the Western-style chain supermarkets, residents in Chinese cities can also purchase food at convenience stores, regular farmers' markets, informal markets and other food establishments; however, to our knowledge, travel patterns to these different types of markets have rarely been explored in the literature.

Ho and Lau, 1988; Veeck and Veeck, 2000). On the supply side, after the transition from a state-owned system to a privately-owned system with profit-making operators since the 1980s (Skinner, 1978; Zhang and Pan, 2013), today's food markets in Chinese cities take a variety of forms, including formal and informal traditional farmer's markets as well as chain supermarkets (Goldman, 2000; Hu et al., 2004). Therefore, we believe the food market access pattern as well as the influence of built environment on it in Chinese cities could be different from that in the western world; however, few literature exist in this field. In this paper, we examine the residents' walking and biking patterns for food shopping in China, aiming at answering two research questions: (1) How does neighborhood form impact the residents' walking and biking distance to food markets in China? (2) Could food market characteristics in physical design and site location also make an influence? We have attempted to answer these questions by analyzing data collected from resident surveys at 15 food markets in three neighborhoods in the city of Beijing. We expect the findings will improve the understanding regarding different types of neighborhoods and their effects on people's walking and biking distances to food markets in a high-density urban context. We also hope to inform the design and planning of food markets so as to increase overall market patronage for walking and biking, and to enhance neighborhood livability. The following section provides a review of the literature related to the subject. Section 3 describes the research context and the approach, including a description of the resident survey. Section 4 presents the results of the survey analysis, including a comparative analysis and a multivariate regression attempting to identify factors influencing walking and biking distances to food markets. Section 5 discusses some planning implications of, and limitations to, the analysis. Section 6 concludes. 2. Literature review The concept of a market “catchment” is often used to measure the market accessibility in non-work travel behavior studies and food environment analyses. The term refers to the geographical area served by a particular market within a particular distance or time, presumably an area within which a majority of residents shop. So far, no consensus exists among researchers regarding a uniform standard for catchment area size, nor is there a uniform approach to estimating it. The adopted standards in the literature have varied from a 500 m-airline buffer (Donkin et al., 1999; Furey et al., 2001; Wrigley, 2002), to a 0.5-mile or 833 m buffer (Hess et al., 1999; Guo et al., 2010; Jiao et al., 2011), to a 1000-meter buffer (Smoyer-Tomic et al., 2006; Apparicio et al., 2007). Furthermore, empirical evidence suggests that there is no standard of measurement that guarantees an accurate index of exposure to markets. For example, it was observed that stores and markets visited for food shopping trips were often beyond the 0.5-mile radius (Kestens et al., 2010; Zenk et al., 2011; Hirsch and Hillier, 2013). In theory, people make shopping trip decisions based on the positive utility originating from the attractiveness of the shopping place (e.g., choice and quality of products, etc.) and the cost of getting there (e.g., time, money, physical effort, etc.). To maximize their utility, people may opt for a more distant destination in order to get higher quality, greater choice, and/or cheaper products (Maat et al., 2005). For example, Drewnowski et al. (2012) found that only 1 in 7 respondents in the Seattle Obesity Study reported shopping at the nearest supermarket. Such a pattern is consistent with studies in other places, including Newcastle, UK and North Philadelphia, US, where participants rarely shop at the nearest supermarket (White et al., 2004; Hillier et al., 2011). Regarding the factors influencing shopping travel behavior, there is a general consensus that both socioeconomics (e.g., gender, income, education, occupation, car ownership, etc.) and built environment can affect the mode choice and travel frequency to some extent, although their impact on the travel distance for shopping trips were rarely explored. Specifically on the dimension of built environment, for example, Jiao et al.

3. Research method 3.1. Selection of neighborhoods In this study, we focus on the city of Beijing, the capital of China with a long history. Like many other Chinese cities, Beijing has a traditional city center, and has been undergoing fast urban expansion and dramatic changes in land use through the past 30 years of reform (Zhao, 2010). In the same period, Beijing has been undergoing a process of transformation from a state-owned system to a privately-owned system in its food markets. These features make Beijing a good case with which to examine the effects of neighborhood form on residents' food shopping behavior. We selected three neighborhoods to conduct our survey based on their built periods. The sampled neighborhoods were considered representative of a full range of local neighborhood built environments in Beijing, from the traditional and enclave types to the superblock type. The definition of the neighborhood types was borrowed from previous empirical studies in China (e.g., Jiang et al., 2015). Table 1 summarizes the primary features of these three neighborhoods. Figs. 1 and 2 show the location of selected neighborhoods in Beijing and their visual forms, respectively. Xi Si is located inside Old Beijing City (within the 2nd ring road), only 2.6 km from Tian'an men. It is a traditional-type neighborhood, consisting of a “hutong-courtyard” system, which can be traced back to the Qing Dynasty (A.D. 1636–1912). The residential quarters in Xi Si are featured by introverted yards surrounded by single-story houses. The basic residential block is bordered by east-west hutongs at a spacing 2

Y. Chen

Transport Policy xxx (2017) 1–10

Table 1 Summary of features of three neighborhood typologies. Building

Block/Street

Function

Access/Parking

Traditional

Single-story courtyards

Mixed use with housing, temples and schools

Fully open, limited car access, some parking along hutongs

Enclave

Primarily 3-6 story walk-up apartments

75*150 m~ 100*200 m 2-4 lanes 300*350 m 4-6 lanes

Moderately gated, security guards at some entries; surface parking within the blocks and on streets

Superblock

Primarily 20-30 story high-rises

Mixed use with work places, housing, educational and cultural facilities and other living services Homogeneous residential use

400*500 m 6-8 lanes

Completely gated; sufficient parking both underground and on-site; illegal parking prevails due to underused wide streets and poor traffic management

Source: Adapted from Jiang et al. (2015).

Fig. 1. Locations of three selected neighborhoods in Beijing.

three neighborhoods. He Ping Li is located between the 2nd and 3rd ring roads, 6.1 km from Tian'an Men. It is an enclave-type neighborhood built during the 1950s1980s under the planned economy regime. Based on the principle of the job-housing balance, He Ping Li is mixed with work places, housing, educational, and cultural facilities as well as other living services. Most residential blocks feature 3-6 story walk-up apartments with primary schools in the center and retail facilities along the edges. According to our surveyed sample, the average personal income of residents in He Ping Li is about 6058 RMB per month. While the size of a block can be as large as 300 meters by 350 meters, approximately, the neighborhood is more permeable and accessible as the blocks are rarely fully gated, allowing residents to pass through. The main streets in He Ping Li are often 4 or 6 lanes wide with ample shade trees and small-scale street-front stores. Crossing those streets may not be as easy as in Xi Si. Another challenge for the walking and biking environment in He Ping Li is its ever-growing car parking demand; nowadays cars block many bike lanes along both

distance of approximately 75–150 meters, and south-north streets at a spacing distance of approximately 350 meters (Deng et al., 2002). The land use in Xi Si is mixed, with primary schools and temples within the residential blocks and major commercial facilities along the main streets. The dense street network shapes an efficient transportation network, particularly for walking and biking. Regarding the scale of streets, hutongs are often under 9 meters wide, whereas most main streets have only 4 lanes, allowing safe and convenient crossing for pedestrians and cyclists. Despite some conflicts with car parking along hutongs, the sidewalks and bicycle lanes along main streets are well protected from cars as a result of strict on-street parking management. In addition, large, old trees along the streets provide delightful shade for people. In sum, the Xi Si traditional neighborhood, with its dense street network, human scale, mixed land use and green amenities is very walk and bike friendly. However, as the housing condition of the old courtyards is relatively poor, the average personal income of residents in Xi Si, estimated at 5683 RMB per month based on our surveyed sample, is the lowest among the

3

Y. Chen

Transport Policy xxx (2017) 1–10

Fig. 2. Comparison of traditional, enclave and superblock neighborhoods.

The farmer's market has a long tradition in urban China and was once the single food source residents could access in cities. Although the ownership of farmer's markets has gradually shifted from local government to private sectors since the 1980s, the form and environment of this type of market have hardly changed (Skinner, 1978; Zhang and Pan, 2013). Since farmer vendors often rent stalls from the market operator and then sell their grown or wholesale food products at the market, residents could compare different stalls and get a bargain on price through face-to-face communication with the vendor. The size of a typical farmer's market ranges from 1000 m2 to 1500 m2, with the possibility of being larger than 5000 m2. Most farmer's markets are on the ground level; some are covered, while others are open-air markets (Fig. 3, left). As for their location, markets of this type often occupy an undeveloped parcel, or are located at the corner of newly developed areas; however, in the inner city where land is more valuable and less available, traditional farmer's markets stand either on their original sites or on a piece of vacant land next to a park. Generally speaking, the food price in the farmer's markets is relatively low because of cheap stall rental fees, the direct supply chain, and competition among vendors. The supermarket, which was originally invented and has prevailed in western countries, was introduced to China in the 1990s. Supermarkets feature chain businesses and operate with an entrepreneurial management model (Goldman, 2000; Hu et al., 2004). Resident customers choose their desired food items in a designated zone and go through a checkout process to complete the purchase (Fig. 3, right). Since each item in a supermarket should have a fixed price with a tag on it, bargaining is not allowed. While the total size of a supermarket can be very large, the food areas tend to be relatively small, ranging from approximately 500 m2–1500 m2. In addition, the food area is often placed in the basement of multi-level supermarket buildings. Regarding price, although the

external streets and sidewalks inside these blocks. Wang Jing is located between the 4th and 5th ring roads, 11.5 km away from Tian'an Men. It is a superblock-type residential area, which was one of the ten city edge groups in the Beijing City Master Plan (1991–2010), and was built by a quasi-government real estate developer beginning in 1993. The Wang Jing neighborhood is dominated by homogenous residential use, with 20–30 story high-rise residential towers. A few business centers are concentrated at some street intersections. The permeability and accessibility of this neighborhood is quite poor, since each block is large in size (often 400 meters by 500 meters) and fully gated. The main streets in Wang Jing have 6–8 lanes, which makes crossing inconvenient and dangerous. The illegal on-street parking problem in Wang Jing is the most severe of the three neighborhoods. Because the area is relatively newly built, the tree shade is not as good as in the other two neighborhoods. All above features indicates that Wang Jing is more car-oriented than the other two neighborhoods. Not surprisingly, the personal income of residents in this area, averaged at 6460 RMB per month, is also higher than the other two neighborhoods. Yet it is worth noting that the income for this neighborhood is mixed, since it includes several relocation housing projects for local farmers.

3.2. Selection of markets Beijing, like many other Chinese cities, has three main types of food shopping markets-namely, the farmer's market, the supermarket and the community stalls (Zhang and Pan, 2013). In this paper, we have focused on the first two types of markets, because the community stalls, featured by their small size, limited food variety and extensive distribution, serve only as a supplement to the mainstream food shopping venues for local residents. 4

Y. Chen

Transport Policy xxx (2017) 1–10

Fig. 3. Farmer's market (left) and supermarket (right) in Beijing.

respondent was willing to stay. If not, the surveyor would follow the respondent entering the market or to his/her next destination through the interview process. After the interview was finished, the surveyor would then return to the same entrance to select the next participant. In the survey, we asked the residents if they walked or biked to the food market. For those who did, we then asked them to point out on a map their approximate residential locations as well as their trip routes. We also collected information regarding socio-economic, demographic and travel characteristics. In order to address the confounding issue of trip chaining, we asked respondents to say whether they came to the present food market on their way to work places or other destinations (e.g., parks for physical exercises, schools for sending kids, other markets for additional shopping, etc.). In total, 2144 resident shoppers accepted the survey with a response rate of 37%; among them, 1677 (78%) chose to walk or bike and participated in the full interview. Of these, 1417 valid observations were obtained after excluding responses with incomplete information and trip records. Table 2 presents the distribution of the resident sample across 15 market cases. We recorded the reported home addresses, pedestrian and bike paths and geo-coded them in a geographic information system (GIS). We also geo-coded the street network and food establishments (including food markets and smaller food stalls) within the three neighborhoods, and calculated the relevant distances (e.g., path distances, straight-line distances, the nearest distance from home to the nearest food establishment, etc.).

operating costs for a supermarket is higher than a farmer's market, the food price of the former can be competitive thanks to the direct supply from producers and occasional promotion programs. Of course, in Beijing there are some specialized supermarkets that target high-income residents; prices there are more expensive. In order to illustrate the effects of market characteristics on residents' shopping behavior, we selected the farmer's market and supermarket in the three neighborhoods to maximize their variety in physical design and site location. In our sampling frame, there are four, five, and six food markets in Xi Si, He Ping Li, and Wang Jing, respectively. Each neighborhood has roughly equal numbers of farmer's markets and supermarkets. The sizes of the selected food markets range from 500 m2 to 5000 m2. It is worth noting that two markets (i.e., Liu Pu Kang and He Ping Li) are located next to city parks. Unsurprisingly, compared with supermarkets, the food price in farmers' markets tends to be cheaper due to lower rents for stalls and more competition among vendors. Table 2 shows the variation in the characteristics comprising the market-based sampling frame. 3.3. Resident survey Based on the derived sampling frame, we collected walking and biking behavior information by interviewing people at each market during the last week of August 2015. The intercept survey was conducted from 7a.m. to noon on weekdays and weekends, respectively, in an attempt to cover working and non-working groups. In selecting survey participants, surveyors stood at the entrance of the markets and talked to whoever walked by for an interview invitation. Once the invitation was accepted, the surveyor would conduct the interview at the entrance if the

3.4. Analytical approach Descriptive statistics was first used to help depict the respondents' overall characteristics and their travel patterns to food markets. We

Table 2 Typology features of fifteen markets in the sampling frame. Neighborhood

Floor level

Food price (RMB) c

Close to park

Number of resident sample

1.58 5.25 1.33 1.31

Ground Ground Upper Ground

7.50 6.50 6.55 6.60

No Yes No No

124 104 97 110

F F F S S

1.20 1.20 1.40 0.80 0.50

Lower Lower Ground Lower Lower

5.33 8.50 9.90 6.86 11.10

Yes No No No No

92 79 97 87 110

F F F S S S

1.48 2.67 7.40 0.95 0.46 1.38

Ground Ground Ground Lower Ground Upper

7.87 7.27 7.95 10.39 10.77 7.52

No No No No No No

117 97 79 58 76 90

Market name

Market type

Traditional (Xi Si)

Fu Guo Li Liu Pu Kang Merry Mart 1 Shun Tian Fu

F F S S

Enclave (He Ping Li)

He Ping Li Tian Feng Li Yan Feng Merry Mart 2 Jing Ke Long 1

Superblock (Wang Jing)

Hua Cheng Guang Long Wang Jing Cai Xian Guo Mei Jing Ke Long 2 Carrefour

a b c

a

Space size

b

(1000 sq.m.)

F denotes farmer's market; S denotes supermarket. The space size of supermarket corresponds to the food-shopping zone only. Food price for each market is calculated by averaging the prices of pork, green cabbage, and egg on the first survey day (i.e., Aug 24, 2015). 5

Y. Chen

Transport Policy xxx (2017) 1–10

markets. The map reveals that markets in the traditional neighborhood seem to have the largest actual catchment areas, followed by the enclave neighborhood, whereas the catchment size for markets in the superblock neighborhood is the smallest. Such an impression is also confirmed by the analysis of the distribution of distances by neighborhood type and trip mode. Only 30% of respondents walked further than 600 meters to a market in the superblock neighborhood, while 51% and 48% of respondents did this in traditional and enclave neighborhood, respectively. The trends for bike distance comparison by neighborhood are similar to those for walking routes, although the average distance of cycling is longer (see Table 4). Overall, the average walking and biking distances to food markets in our sample range from 0.67 km to 1.17 km. This makes an interesting contrast to empirical studies in US, which observed much longer travel distances to supermarkets ranging from 1.58 mi to 6.3 mi (i.e., 2.54 km–10.14 km), probably due to the dominating access mode of car driving (Hillier et al., 2011; Kerr et al., 2012; Hirsch and Hillier, 2013). The market characteristics also matter in the walking and biking distances for food shopping. As shown in Table 4, the average travel distance to farmer's markets was 717 meters (median, 576 meters) for walking, 1399 meters (median, 1211 meters) for biking and 955 meters (median, 716 meters) for the combined modes. The corresponding distances from home to the supermarket are consistently shorter, by a magnitude of 85–225 m. The difference may be due to the fact that farmers' markets tend to have larger spaces, lower food prices and better accessibility. Another important finding from descriptive analysis is that residents rarely shop at the nearest food stores and prefer larger farmer's markets and supermarkets instead. Specifically, 85% of respondents in our sample reported that there were other food stores even closer to their homes, but they did not consider them as primary places for daily food shopping. This is consistent with findings from some western literature (e.g., White et al., 2004; Hillier et al., 2011; Drewnowski et al., 2012). In terms of the directness of walking and biking trips to food markets, we measured the detour factor using the ratio of the actual walking and biking distance to the straight-line distance from home to the pertinent food market for a given trip. A higher value indicates more physical efforts people have to exert when traveling from A to B. By averaging detour factors in our sample at the neighborhood level, results show that the superblock has an average detour factor of 1.41, indicating less directness, whereas the traditional and enclave neighborhoods have lower values of 1.36 and 1.30, respectively. The ANOVA analysis reveals that the three values were significantly different from each other (p < 0.0001). This finding

aggregated travel distance levels at both the neighborhood level and the market type level, and then compared the average travel distances of on foot and biking. Second, to further isolate the influence of the built environment for markets and neighborhoods from the actual distance of walking and biking, while controlling for confounding factors such as individual and household socioeconomics, we have specified an ordinary least squares (OLS) regression of the basic form:

Di ¼ fðSEi ; TRi ; MKTi ; NBHDi Þ; εi

(1)

where: Di ¼ walking/biking distance of surveyed resident i, SEi ¼ a vector of socio-economic status variables of surveyed resident i, TRi ¼ a vector of trip-specific variables of surveyed resident i, MKTi ¼ a vector of market-specific variables associated with surveyed resident i, NBHDi ¼ the neighborhood type (dummy) in which surveyed resident i is interviewed, and εi ¼ a random error term. 4. Results 4.1. Descriptive analysis The salient characteristics of the participants are presented in Table 3. Of the 1417 survey respondents included in the analysis, 66% were female and 34% male. This is not unexpected, given that in Chinese families women often play a more important role in food preparation than men. The average household size is 3.2. The personal income for the majority of sample ranged from 3000 to 7500 RMB per month and was consistent with Beijing city statistics. It was observed that the car ownership ratio in the three neighborhoods varied from 39% to 60%, with 48% as the total average. This confirms that the superblock neighborhood is much more car-oriented compared with the traditional and enclave neighborhoods. Employed workers constitute about one third of the sample and did not vary across the neighborhood type. On average, 40% of the sample had a college education background and 5% had master degrees or higher. Fig. 4 shows the recorded walking and biking routes to food markets in the three neighborhoods. Green dots indicate the 15 surveyed food markets. Lines with different colors represent the routes to different food Table 3 Participant Characteristics by neighborhood type.

Female Age <30 30–40 40–50 50–60 60–70 >70 Education High school or lower College Master or higher Employed Household size (people) Household with a car or more Personal income (RMB/month) <3000 3000–5000 5000–7500 7500–10,000 >10,000 No. of valid observations

Traditional (Xi Si)

Enclave (He Ping Li)

Superblock (Wang Jing)

Total

58%

64%

74%

66%

5% 11% 14% 29% 29% 13%

5% 12% 13% 19% 27% 24%

9% 14% 14% 20% 28% 16%

6% 12% 14% 22% 28% 18%

65% 32% 3% 33% 3.3 39%

50% 46% 4% 33% 3.0 45%

49% 42% 9% 35% 3.4 60%

54% 40% 5% 34% 3.2 48%

17% 36% 26% 15% 7% 435

12% 41% 25% 10% 12% 465

16% 28% 26% 14% 15% 517

15% 35% 26% 13% 11% 1417

6

Y. Chen

Transport Policy xxx (2017) 1–10

Fig. 4. Walking and biking routes to 15 food markets in three Beijing neighborhoods (left- Xi Si, traditional type; middle- He Ping Li, enclave type; right- Wang Jing, superblock type). Table 4 Walking and biking distances to food markets, by neighborhood type and market type. Neighborhood Type

Market Type

Traditional

Enclave

Superblock

Farmer's Market

Supermarket

Walk Distance Mean Median 95% confidence interval for mean (lower bound) 95% confidence interval for mean (upper bound) No. of valid observations

767 604 688 845 179

698 576 644 753 320

624 507 584 664 459

717 576 675 759 500

631 483 587 674 458

Bike Distance Mean Median 95% confidence interval for mean (lower bound) 95% confidence interval for mean (upper bound) No. of valid observations

1444 1243 1324 1563 256

1187 1053 1065 1309 145

999 854 816 1182 58

1399 1211 1281 1518 269

1175 1055 1075 1274 190

Combined Mode Distance Mean Median 95% confidence interval for mean (lower bound) 95% confidence interval for mean (upper bound) No. of valid observations

1165 954 1082 1249 435

851 711 794 908 465

666 538 624 708 517

955 716 901 1010 769

790 638 744 837 648

coefficient of personal income at 5000–7500 RMB per month shows a marginal significance (at the 0.1 level). Compared with socioeconomic factors, trip characteristics seem to have a more significant impact. Results show that higher trip frequency and trip chaining increases the walking and biking distance to food markets. The coefficient of the travel mode of bike is significant, positive, and large. This suggests that food shopping trips by bike are longer than shopping trips made on foot, reflecting the fact that cyclists can move faster and are willing to go further (Forsyth and Krizek, 2010). Anecdotally, from the survey implementation, we observed that many bikes in Beijing were equipped with a basket at the front and were convenient for residents to carry their purchased food products. In the “full model” column of Table 5, by adding variables on the built environment and market characteristics to the regression, we can observe a notable improvement (from 22.4% to 28.6%) in the explanatory power of the model. As expected, the coefficients on the “traditional” and “enclave” variables were both positive and significant. This suggests that compared with the superblock neighborhood, and all else being equal, food markets in the traditional and enclave neighborhoods can attract residents to travel an average of 124 meters and 82 meters longer for shopping, respectively. This may reflect the superiority of the traditional

reflects the fact that the superblock neighborhood has a low street network density, which further reduces the catchment area size of food markets due to the walk detour effects. 4.2. Regression analysis Table 5 presents the regression model estimation results on the resident survey data. The “control model” includes only the socioeconomic and trip-related control variables from equation (1). The adjusted R square is 0.217. Among the socioeconomic variables, females with an education level of a master's degree or higher are most significant (at the 0.05 level). Specifically, female residents travel shorter distances to food markets than men do. This is as expected, since food shopping trips using non-motorized modes involve physical effort regarding both the transportation and food carrying. Residents with master or higher educational degrees are associated with shorter walking and biking distances. The reason may be that people with high education attainment tend to value the travel time more than others and prefer shorter distance for food shopping. Factors such as employment status, personal income, and car ownership were found to be not significant when predicting the walking and biking distances to food markets, with the exception that the 7

Y. Chen

Transport Policy xxx (2017) 1–10

at the ground level. This may be due to the reason that markets on the ground floor tend to provide a better micro-climate (e.g., openness, sunlight, ventilation) for shopping activities. More importantly, food shoppers in China are mainly elderly women (as indicated from Table 3); these people do not like to use stairs because of their physical conditions. On the other hand, placing the food market adjacent to a park can increase the walking and biking distance by as long as 191 meters. One explanation for this significant result may be that the proximity to parks can provide not only a more pleasant environment for food shopping trips (potentially involving voluntary detours through the park), but also an attractive amenity place for residents to exercise, or engage in social and leisure activities after or before food shopping, thus contributing to extra utility for their travels to food markets. Anecdotally, many respondents, the elderly women in particular, reported that they have the habit of first exercising in the parks in the morning (e.g. guangchang wu) and when they go home, they go to the markets to buy vegetables and meat to prepare the food for the family. The significance of socioeconomic and trip-related variables remains the same as the control model, with the exceptions that the gender effect is somewhat less significant (at p < 0.1 in the “full model” compared to p < 0.05 in the “control model”), and that the effect of a personal income between 5000–7500 RMB per month becomes insignificant. Employment status and household size do not affect the walking and biking distance significantly, nor does weekend trip-making. While Rahul and Verma (2014) observed that people with a private vehicle in Bangalore, India prefer significantly smaller walking distances than people who do not own a private vehicle, our data from Beijing suggest the difference between the two groups is insignificant, at least for food shopping trips.

Table 5 OLS regression models predicting the walking and biking distance to food markets. Variable

Control Model Coefficient

Socioeconomic and Trip Characteristics Gender: female 76.170** Age <30 ref Age 30-40 14.024 Age 40-50 11.122 Age 50-60 92.021 Age 60-70 53.567 Age >70 2.927 Education: high school or lower ref Education: college 46.644 Education: master or higher 168.741** Occupation: employed 10.534 Household size 0.355 Car ownership 3.992 Income: <3k RMB/month Ref Income: 3k-5k RMB/month 39.425 Income: 5k-7.5k RMB/month 70.985* Income: 7.5k-10k RMB/month 28.54 Income: >10k RMB/month 50.643 Trip time: weekend 13.355 Trip frequency: less than Ref 3 times per week Trip frequency: 3–5 times 160.559** per week Trip frequency: more than 271.266** 5 times per week Trip mode: walk Ref Trip mode: bike 450.468** Trip chaining 72.255**

Full Model T-test

Coefficient

T-test

2.67

53.236* ref 38.332 8.277 56.425 44.013 3.059 ref 39.698 136.732** 9.693 0.064 18.565 Ref 31.836 51.524 22.947 47.61 9.911 Ref

1.93

0.22 0.18 1.49 0.82 0.04 1.55 2.59 0.26 0.03 0.14 0.98 1.65 0.55 0.92 0.5

713.888** 1417 (20,1396) 21.50 0.224

1.36 2.18 0.25 0.01 0.65 0.82 1.24 0.46 0.9 0.39

5.04

166.299**

5.41

8.48

267.615**

8.55

15.68 2.43

Ref 345.816** 58.696**

11.5 2.04

124.780**

3.25

82.778**

2.32

5. Conclusion and discussion

Built Environment and Market Characteristics Neighborhood: traditional (Xi Si) Neighborhood: enclave (He Ping Li) Neighborhood: superblock (Wang Jing) Home: more food establishments nearby Home: distance to the nearest food establishment Market: average food package price (RMB) Market size (1000 sq.m) Market: at upper or lower levels Market: adjacent to a park (Constant) No. Observations (df) F Adjusted R2

0.63 0.14 0.95 0.7 0.05

Over the past decade there has been a growing base of research investigating neighborhood food environment and shopping travel behavior. Previous studies were mostly conducted in US and Europe, and tended to focus on car driving patterns to supermarkets and grocery stores. The present study contributes to the literature by offering evidence on the food shopping travel pattern as well as the influence of built environment on it in a high-density and less car-oriented urban context. This research, to our knowledge, also presents one of the first efforts of examining the impact of the food market characteristics (e.g., physical design, site location, etc.) on walking and biking distance for food shopping. Based on 1417 resident surveys at 15 food markets in three neighborhoods in the city of Beijing, we found that residents rarely shop at the nearest food stores and prefer larger farmer's markets and supermarkets instead. Still, they travel much shorter distances than residents in western cities. An ordinary least squares regression analysis reveals that, after controlling for the effect of personal socioeconomic and trip characteristics, traditional neighborhoods with dense street networks, mixed land uses and ample tree shades present the largest walk/bike catchment area, followed by enclave neighborhoods and superblock neighborhoods. In addition, markets with larger size, ground-level and adjacency to a park is significantly associated with longer walking and biking distances. Some personal characteristics (i.e., gender, education) and trip factors (i.e., trip frequency, trip mode, trip chaining) also played important roles in defining the walking and biking distance. Our findings have a number of implications for urban planners and policy makers in China. First, flexible food market catchment area definitions that vary by neighborhood form and market type are preferred in urban management and planning. For farmer's markets and supermarkets, current practices in urban China tend to apply a single catchment standard at the city level. While some cities use a range-based standard (e.g., 500–1000 m), there is little guidance on how to adjust them at the neighborhood level. Our analysis suggests that incorporating neighborhood form and market types in the food market catchment area definitions would be desirable and are likely to match with the actual demand. Second, improving walkability around food markets would directly

Ref

8.82

113.350**

3.13

0.019

0.21

32.142**

3.28

16.720* 77.186** 191.092**

1.69 2.47 4.15

804.721** 1417 (28,1388) 21.28 0.286

6.28

*p < 0.10, ** p < 0.05.

and enclave neighborhoods in terms of their walkability and bikeability, as discussed earlier. The presence of other food establishments near home was found to be positively significant to the walking and biking distances to a food market. This echoes findings from studies in the western countries that stated increasing diversity of destinations and more access opportunities for residents who lived nearby would support non-motorized travel (Hess et al., 1999; Scheiner and Holz-Rau, 2007; McCormack et al., 2008; Jiao et al., 2011). All four market-related variables are also significant. As expected, enlarging the market size increases the walking and biking distance, whereas the average food package price is negatively related to walking and biking distances to a market. The strong effects of other two market variables are particularly interesting. On the one hand, all else being equal, the walking and biking distance to the markets at upper or lower levels is in average of 77 meters shorter than the distance to the markets 8

Y. Chen

Transport Policy xxx (2017) 1–10

Acknowledgements

benefit the disadvantaged population in Chinese cities. According to our survey data, females and elderly people constitute the majority of our sample interviewed at the food markets no matter which neighborhood type they were located at. This reflects that although Chinese societies like Beijing are becoming increasingly motorized in general, women and the elderly, both of whom are less likely to drive a car, are still the major group for routine food shopping activities. Therefore, it would be desirable for city governments to initiate a Safe Route to Market program (similar to the Safe Route to School program in the United States) to improve the street environment and get more disadvantaged people safely walking and biking to food markets, and enhancing their food access and quality of life in cities. Third, encouraging people to ride their bikes could be an efficient measure to enlarge the catchment of the food markets. Our analysis shows that in Beijing, the distance for biking is 346 meters longer than that for walking, after controlling for confounding factors. The bike use in Beijing has been shrinking dramatically. We believe that policies or interventions aimed at providing higher destination accessibility, more exclusive bicycle lanes, a mixed environment and greater street connectivity may help increase bicycle use (Zhao, 2014). In our survey, some residents also complained about the inadequacy of spaces and the poor security for bike parking, which prevented them from biking to the markets. Addressing these problems may also lead to more bike uses for food shopping and to enhance the accessibility of food markets. Finally, from a livability perspective, traditional farmer's markets in cities should be valued during the process of urban redevelopment. As a result of the perception that supermarkets are always superior to the farmer's markets, which are thought of as being crowded and dirty, some Chinese cities have been taking measures to remove existing farmer's markets and expanding modern-style supermarkets and small food stores. However, our survey suggests that the catchment areas of the farmer's markets in Beijing are generally larger than those for supermarkets; the majority of residents shop at larger food markets instead of their nearest food stores. Farmer's markets have the advantage in that they include more food diversity, more freshness, lower prices, better ventilation and last-meter accessibility, and sometimes their proximity to a park. Such features embedded in this type today have evolved during history and have fit into the Chinese culture and lifestyle. Thus, preserving and promoting farmer's markets in urban China can not only provide better food access for residents, but also can facilitate social interaction and create a unique identity for neighborhoods and cities. We recognize that this research has a number of limitations. First, different neighborhood form elements may affect the walking and biking distances to food markets in different ways. Unfortunately we could not examine the individual effects of those elements due to the absence of spatial data and the limited number of neighborhoods in our sampling frame. Future research would require a greater number of neighborhood samples so that individual urban form factors (e.g., density, land use mix, width of roadways, etc.) could be computed and included in statistical models. Second, we combined walking and biking in our modeling analysis due to limited number of bike trips for shopping (especially in the superblock neighborhood). Future studies are suggested to investigate the influencing mechanisms on travel patterns to food markets by walking and biking, respectively. Third, we could not build a causal relationship between the neighborhood form, market type and the residents' walking and biking distances to food markets. Indeed, people may travel further to markets in traditional neighborhoods because they like walking and biking and choose to live in such areas in the first place, presenting a so-called “self-selection” effect (Mokhtarian and Cao, 2008). Fourth, we could not include the possible effects of the season on the catchment size of the food markets. Because people may walk and bike less during cold weather, the overall access pattern in the winter might be different. Finally, our result could be possibly biased due to the fact that our survey was conducted in the morning only and that those who go to the markets in other time of a day might be missing in our sample.

This work is supported by the National Social Science Fund of China (No. 16CRK020), the Humanity and Social Science Research Youth Foundation of China's Ministry of Education (No. 15YJCZH016) and the National Natural Science Foundation of China (No. 51378278). References Aggarwal, A., Cook, A.J., Jiao, J., et al., 2014. Access to supermarkets and fruit and vegetable consumption. Am. J. Public Health 104 (5), 917–923. Apparicio, P., Cloutier, M.S., Shearmur, R., 2007. The case of Montreal's missing food deserts: evaluation of accessibility to food supermarkets. Int. J. Health Geogr. 6 (4). BTRC (Beijing Transport Research Center), 2015. Beijing Transport Performance Annual Report 2014 (in Chinese). Chen, H., Jia, B., Lau, S., 2008. Sustainable urban form for Chinese compact cities: challenges of a rapid urbanized economy. Habitat Int. 32, 28–40. Clifton, J.K., 2004. Mobility strategies and food shopping for low-income families: a case study. J. Plan. Educ. Res. 23, 402–413. Darido, G., Torres-Montoya, M., Mehndiratta, S., 2013. Urban transport and CO2 emissions: some evidence from Chinese cities. WIREs Energy Environ. https:// doi.org/10.1002/wene.71. Darido, G., Torres, M., Mehndiratta, S., 2010. Urban transport and CO2 emissions: some evidence from Chinese cities. In: Paper Presented at the Transportation Research Board Annual Meeting 2010. Deng, Y., Funo, S., Shigemura, T., 2002. A study on the block formation and its subdivision into the housing lots in the inner city of Beijing: an analysis of Qianlong Jingcheng Quantu, map of the capital city of Qianlong period (1750). J. Asian Archit. Build. Eng. 1 (2), 209–217. Donkin, A., Dowler, E., Stevenson, S., Turner, S., 1999. Mapping access to food at a local level. Br. Food J. 101 (7), 554–564. Drewnowski, A., Aggarwal, A., Hurvitz, P.M., et al., 2012. Obesity and supermarket access: proximity or price? Am. J. Public Health 102 (8), 74–80. Forsyth, A., Krizek, K.J., 2010. Promoting walking and bicycling: assessing the evidence to assist planners. Built Environ. 36 (4), 429–446. Frank, L.D., Pivo, G., 1994. Impacts of mixed use and density on utilization of three modes of travel: single-occupant vehicle, transit, and walking. In: Transportation Research Record 1466. TRB, National Research Council, Washington, D.C., pp. 44–52 Furey, S., Strugnell, C., McIlveen, H., 2001. An investigation of the potential existence of “food deserts” in rural and urban areas of Northern Ireland. Agric. Hum. Values 18 (4), 447–457. Gibson, D.M., 2011. The neighborhood food environment and adult weight status: estimates from longitudinal data. Am. J. Public Health 101 (1), 71–78. Goldman, A., 2000. Supermarkets in China: the case of shanghai. International review of retail. Distrib. Cons. Res. 10 (1), 1–21. Guo, J.Y., Bhat, C.R., Copperman, R.B., 2010. Effect of the built environment on motorized and nonmotorized trip making: substitutive, complementary, or synergistic?. In: Transportation Research Record: Journal of the Transportation Research Board. Transportation Research Board. No. 2010. Hess, P.M., Houdon, A.V., Snyder, M.C., et al., 1999. Site design and pedestrian travel. In: Transportation Research Record: Journal of the Transportation Research Board. TRB, National Research Council, Washington, D.C., pp. 9–19. No. 1674. Hillier, A., Cannuscio, C., Karpyn, A., et al., 2011. How far do low-income parents travel to shop for Food? Empirical evidence from two urban neighborhoods. Urban Geogr. 32 (5), 712–729. Hirsch, J.A., Hillier, A., 2013. Exploring the Role of the Food Environment on Food Shopping Patterns in Philadelphia, PA, USA: A Semiquantitative Comparison of Two Matched Neighborhood Groups. Int. J. Environ. Res. Public Health 10 (1), 295–313. Ho, S., Lau, H., 1988. Development of supermarket technology: the incomplete transfer phenomenon. Int. Mark. Rev. 5 (1), 20–30. Holz-Rau, H.-C., 1991. Verkehrsverhalten beim Einkauf. Int. Verkehrswes. 43 (7–8), 300–305. Hu, D., Reardon, R., Rozelle, S., et al., 2004. The Emergence of Supermarkets with Chinese Characteristics: Challenges and Opportunities for China's Agricultural Development. Dev. Policy Rev. 22 (5), 557–586. Inagami, S., Cohen, D.A., Finch, B.K., et al., 2006. You are where you shop: grocery store locations, weight, and neighborhoods. Am. J. Prev. Med. 31 (1), 10–17. Jiang, Y., Zegras, P.C., He, D., et al., 2015. Does energy follow form? The case of household travel in Jinan, China. Mitig. Adapt. strat. Glob. Change 20, 701–718. Jiao, J., Moudon, A.V., Drewnowski, A., 2011. Grocery Shopping: How Individuals and Built Environments Influence Choice of Travel Mode. In: Transportation Research Record: Journal of the Transportation Research Board. Transportation Research Board of the National Academies, Washington, D.C., pp. 85–95. No. 2230. Kerr, J., Frank, L., Sallis, J.F., Saelens, B., Glanz, K., Chapman, J., 2012. Predictors of trips to food destinations. Int. J. Behav. Nutr. Phys. Act. 9, 58. Kestens, Y., Lebel, A., Daniel, M., et al., 2010. Using experienced activity spaces to measure foodscape exposure. Health Place 16, 1094–1103. Laraia, B.A., Siega-Riz, A.M., Kaufman, J.S., et al., 2004. Proximity of supermarkets is positively associated with diet quality index for pregnancy. Prev. Med. 39 (5), 869–875. Laska, M.N., Hearst, M.O., Forsyth, A., et al., 2010. Neighbourhood food environments: are they associated with adolescent dietary intake, food purchases and weight status? Public Health Nutr. 13 (11), 1757–1763.

9

Y. Chen

Transport Policy xxx (2017) 1–10 Scheiner, J., Holz-Rau, C., 2007. Travel Mode Choice: Affected by Objective or Subjective Determinants? Transportation 34 (4), 487–511. Skinner, G.W., 1978. Vegetable Supply and Marketing in Chinese Cities. China Q. 76, 733–793. Smoyer-Tomic, K.E., Spence, J.C., Amrhein, C., 2006. Food deserts in the prairies? Supermarket accessibility and neighbourhood need in Edmonton, Canada. Prof. Geogr. 58 (3), 307–326. USDA (United States Department of Agriculture), 2009. Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences. United States Department of Agriculture, Washington, DC. Veeck, A., Veeck, G., 2000. Consumer Segmentation and Changing Food Purchase Patterns in Nanjing, PRC. World Dev. 28 (3), 457–471. Wang, R., Shi, L., 2012. Access to food outlets and children's nutritional intake in urban China: a difference-in-difference analysis. Italian J. Pediatr. 38 (30). White, M., Bunting, J., Williams, L., et al., 2004. Do Food Deserts Exist? a Multilevel Geographical Analysis of the Relationship between Retail Food Access, Socioeconomic Position and Dietary Intake. University of Newcastle, Newcastle-onTyne (UK). Wrigley, N., 2002. 'Food deserts' in British cities: Policy context and research priorities. Urban Stud. 39 (11), 2029–2040. Zenk, S.N., Schulz, A.J., Matthews, S.A., et al., 2011. Activity space environment and dietary and physical activity behaviors: A pilot study. Health Place 17, 1150–1161. Zhang, Q., Pan, Z., 2013. The Transformation of Urban Vegetable Retail in China: Wet Markets, Supermarkets and Informal Markets in Shanghai. J. Contemp. Asia 43 (3), 497–518. Zhao, P., 2014. Private motorised urban mobility in China's large cities: the social causes of change and an agenda for future research. J. Transp. Geogr. 40, 53–63. Zhao, P., 2010. Sustainable urban expansion and transportation in a growing megacity: consequences of urban sprawl for mobility on the urban fringe of Beijing. Habitat Int. 34 (2), 236–243.

Maat, K., van Wee, B., Stead, D., 2005. Land use and travel behavior: expected effects from the perspective of utility theory and activity-based theories. Environ. Plan. B Plan. Des. 32 (No. 1), 33–46. McCormack, G.R., Giles-Corti, B., Bulsara, M., 2008. The Relationship Between Destination Proximity, Destination Mix and Physical Activity Behaviors. Prev. Med. 46 (1), 33–40. Michimi, A., Wimberly, M.C., 2010. Associations of supermarket accessibility with obesity and fruit and vegetable consumption in the conterminous United States. Int. J. Health Geogr. 9, 49. Mokhtarian, P., Cao, X., 2008. Examining the impacts of residential self-selection on travel behavior: a focus on methodologies. Transp. Res. B 42, 204–228. Moore, L.V., Diez Roux, A.V., Nettleton, J.A., et al., 2008. Associations of the local food environment with diet quality: a comparison of assessments based on surveys and geographic information systems: the multi-ethnic study of atherosclerosis. Am. J. Epidemiol. 167 (8), 917–924. Morland, K., Diez Roux, A.V., Wing, S., 2006. Supermarkets, other food stores, and obesity. Am. J. Prev. Med. 30 (4), 333–339. Morland, K., Wing, S., Roux, A.D., 2002. The contextual effect of the local food environment on residents' diets: The Atherosclerosis Risk in Communities Study. Am. J. Public Health 92, 1761–1767. Moudon, A.V., Lee, C., Cheadle, A.D., et al., 2006. Operational Definitions of Walkable Neighborhood: Theoretical and Empirical Insights. J. Phys. Activity Health 3, S99–S117. Pan, H., Shen, Q., Zhang, M., 2009. Influence of urban form on travel behaviour in four neighbourhoods of Shanghai. Urban Stud. 46 (2), 275–294. Passmore, J., Reid, D.P., 1982. The Complete Chinese Cookbook. Exeter Books, New York. Powell, L.M., Han, E., Chaloupka, F.J., 2010. Economic contextual factors, food consumption, and obesity among U.S. adolescents. J. Nutr. 140 (6), 1175–1180. Rahul, T.M., Verma, A., 2014. A study of acceptable trip distances using walking and cycling in Bangalore. J. Transp. Geogr. 38, 106–113. Rundle, A., Neckerman, K.M., Freeman, L., et al., 2009. Neighborhood food environment and walkability predict obesity in New York City. Environ. Health Perspect. 117 (3), 442–447.

10