Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs

Journal of Transport Geography xxx (2015) xxx–xxx

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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs Yue Shen a, Yanwei Chai b,⇑, Mei-Po Kwan c a b c

The Center of Modern Chinese City Studies, East China Normal University, Shanghai 200062, China Department of Urban and Economic Geography, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801-3637, USA

a r t i c l e

i n f o

Article history: Received 14 June 2014 Revised 8 June 2015 Accepted 11 June 2015 Available online xxxx Keywords: Space–time constraints Built environment Daily activities Community types Beijing

a b s t r a c t Space–time fixity constraint is an important concept in transport geography, but the influence of the built environment around both people’s residence and activity locations is not clear. Due to the housing reform and rapid suburbanization in China, various types of residential communities and diverse built environments coexist in the suburbs. Comparing how people’s space–time fixity/flexibility varies among different community types and built environments can thus enhance our understanding of the transition process in Chinese cities. This study investigates how space–time fixity/flexibility and their relationships with the built environment vary among different types of residential communities in Beijing suburbs. Activity-travel diary and 7-day GPS tracking data of 709 respondents in Shangdi-Qinghe area of Beijing collected in 2012 were used. We investigate how variations in space–time flexibility are associated with built environment factors and four different community types: danwei communities, commodity housing communities, affordable housing communities and relocated housing communities, controlling for personal, household and activity attributes. The results suggest the influences of the built environments at residential place and activity place are different, and the relationships between space–time fixity and the built environments of different community types are different. Space–time fixity is not so sensitive to the built environment for residents in danwei communities and affordable housing communities. Gender and age differences in space–time fixity are not consistent with what was observed in Western countries. This seems to reflect the influence of unique social, cultural and family norms in China. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Space–time fixity constraint has long been recognized as an important concept in transport studies, human geography, and urban planning (Kwan, 2000; Schwanen et al., 2008). In time-geographic conceptualizations, certain activities in people’s daily lives are relatively fixed in time and/or space, and two consecutive fixed activities anchor a space–time ‘prism’ in an individual’s daily activity schedule, which represents the person’s opportunity to participate in activities and travel in space–time (Hägerstrand, 1970; Burns, 1979; Timmermans and Arentze, 2002). The space–time fixity and flexibility of people’s everyday activities are thus useful notions for examining people’s accessibility and mobility as important aspects of daily life that are shaped ⇑ Corresponding author. E-mail addresses: [email protected] (Y. Shen), [email protected] (Y. Chai), [email protected] (M.-P. Kwan).

by their space–time constraints (Kwan, 1998; Weber and Kwan, 2003). Space–time fixity and flexibility of activities are also important concerns in transportation and travel behavior studies (Jones et al., 1983; Arentze and Timmermans, 2000; Doherty, 2006). Findings of these studies can inform planning strategies and public policies to help improve people’s life quality and address urban problems such as traffic congestion (e.g., through reducing people’s space–time fixity constraints, increasing the flexibility of work times and places, or implementing teleworking programs) (Schwanen, 2006; Schwanen and Kwan, 2008). Some studies have examined the space–time fixity of everyday activities directly, exploring the relationships between space–time fixity levels and attributes of activities or individuals (e.g., Cullen et al., 1972; Cullen and Godson, 1975; Kwan, 2000; Doherty, 2006). However, few empirical studies on the space–time fixity of activities paid attention to the effects of the built environment, which influences people’s activity-travel behavior and space–time fixity constraints play an important mediating role in shaping their

http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014 0966-6923/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

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Y. Shen et al. / Journal of Transport Geography xxx (2015) xxx–xxx

relationship (Schwanen et al., 2008). While examining the relationship between the built environment and the space–time fixity/flexibility of peoples’ daily activities may yield useful insights for urban and transport planning purposes. In addition, the large number of existing studies on the relationship between the built environment and activity-travel behavior focused mostly on people’s residential areas, while other activity places are not sufficiently considered (Cao et al., 2009; Ewing and Cervero, 2010; Cervero and Murakami, 2010; Boarnet, 2011; Boussauw and Witlox, 2011). The locations of some activities such as workplace are also an important part of the built environment, and its relationship with space–time fixity/flexibility of activities might be different from the built environment around residential areas (Kwan, 2012, 2013). As the data collection method and its integration with location-aware technologies continue to develop, it becomes easier to obtain the location data of individuals’ activities with great spatial and temporal accuracy, which offers new opportunities to consider the built environment around activity places (Palmer et al., 2013; Shen et al., 2013). China’s market-oriented reform and rapid suburbanization have changed the physical structure of Chinese cities and people’s daily life tremendously, and also complicated the relationship between the built environment and space–time fixity of activities. The spatial and temporal organization of residents’ daily activities and travel unfolds in the context of various types of communities (e.g. Danwei, commodity housing, affordable housing, relocated housing, etc.), which are associated with not only the socioeconomic attributes of individuals and households, but also a particular set of unique historical and institutional factors (Wang and Chai, 2009; Zhao and Chai, 2013). It is thus important to consider community factors such as type of housing development (referred to as ‘‘housing type’’ hereafter) when examining the activity-travel behavior of the residents and the space–time fixity/flexibility of activities in Chinese cities. Therefore, this paper addresses the following questions: (1) how the space–time fixity/flexibility of the daily activities in Beijing’s suburbs is associated with the built environment (both around people’s residence and their activity locations), controlling for personal socioeconomic attributes and activity attributes; (2) how the effects of built environments differ among different types of communities. This study would contribute to the understanding of the people’s space–time fixity during transition in China, and the restructuring of urban spaces of Chinese cities. This article presents the study in five parts. Section 2 reviews previous studies. Section 3 describes the study area, the data collection procedures, the sample characteristics and the methodology. Section 4 reports the results of the empirical analysis of the suburban Beijing case. Finally, Section 5 concludes and discusses the analysis.

2. Context of study 2.1. Space–time fixity and flexibility of daily activities In time-geographic conceptualizations, certain activities are relatively fixed due to various reasons (e.g., some activities need to be performed jointly with others, and some need to take place at particular time or/and locations). The notion of space–time fixity was originally conceived by Hägerstrand in binary terms: fixed versus flexible activities (Hägerstrand, 1970). This dichotomy has been used frequently in subsequent research partly because it is simple and can be easily operationalized in accessibility and mobility studies (e.g., Jones et al., 1983; Arentze and Timmermans, 2000). This dichotomy was criticized since the extent to which an activity is spatially and/or temporally fixed may vary and a binary scheme

may not adequately capture such variability (Cullen and Godson, 1975). There are several studies that directly examine the degree of space–time fixity of individual activities. Doherty (2006) and Shen et al. (2013) tried to evaluate space–time fixity/flexibility through observed intra-personal variations of activities over a time-span, which is the observed space–time fixity/flexibility of activities. While Schwanen et al. (2008) asked the respondents to rate the fixity degree of their activities, and examined the self-reported measures which are perceived space–time fixity/flexibility. The perceived space–time fixity measures were considered to play a central role in the scheduling of respondents’ everyday activities, yet there are few studies that directly examine it, which is perhaps due to the extra burden on survey respondents (Cullen et al., 1972; Kwan, 2000; Schwanen et al., 2008). However, as research method and its integration with ICTs continue to develop, self-reported measures of perceived space–time fixity/flexibility would become more available. With respect to factors that influence the space–time fixity of people’s daily activities, some studies focused on the relationship between space–time fixity and individual attributes such as gender and employment status. For example, geographic studies have shown that women engage in fixed activities more frequently and experience more fixity constraints than men (e.g., Hanson and Pratt, 1995; Kwan, 2000). Other studies paid more attention to attributes of activities, especially the relationship between space–time fixity and activity purpose (e.g., Cullen et al., 1972; Cullen and Godson, 1975). Whether information and communication technologies (ICTs) weaken space–time constraints and reduce the fixity of activities was also examined (e.g., Kwan, 2007; Hubers et al., 2008; Schwanen and Kwan, 2008; Lee-Gosselin and Miranda-Moreno, 2009; Ren and Kwan, 2009). According to existing studies and the review provided by Schwanen et al. (2008), the space–time fixity of activities is associated with activity attributes such as activity purpose, time, and location, other persons involved in the activity, the role of the Internet, as well the personal, household and geographic context of the person pursuing the activity. However, the built environment, which has been proved to influence people’s activity-travel behavior, has not got enough attention. Especially, the built environment around activity places rather than residential areas are not sufficiently considered. 2.2. Variations in activity-travel behavior by residential communities in China The relationship between the built environment and activity-travel behavior has been an important issue in urban planning and transportation research, and some studies also took residential neighborhood type into account (Handy, 2005; Cao et al., 2009; Ewing and Cervero, 2010; Boarnet, 2011). In the Western context, neighborhood type was variously referred to as traditional, neo-traditional, urban, New Urban, or suburban (e.g., Waldorf, 2003; Giuliano, 2004). Each of these neighborhood types is identified based largely on land use intensity or density, land use mix and other physical attributes of the built environment. However, the situation in China is quite different from the Western context due to specific historical and institutional background, and the types of residential communities that exist in Chinese cities are also different. Before the 1980s, housing in China was dominated by public rental housing associated with the state welfare system, and danwei was the fundamental socio-spatial unit of urban China (Huang, 2004; Bray, 2005). Danwei, the socialist work-residence compound that is characterized by mixed land use and comprehensive facilities, provided employees a comprehensive package

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

Y. Shen et al. / Journal of Transport Geography xxx (2015) xxx–xxx

of welfare and services including housing (Chai, 1996; Bray, 2005; Wang and Chai, 2009). A danwei compound actually became a self-contained community where people worked, lived, shopped and entertained, often without having to leave the community. The market-oriented reform introduced in the 1980s has reduced the dominance of the socialist danwei system in China (Wang and Chai, 2009). The national housing reform, which began in 1988, aimed to increase private ownership of housing through privatizing part of the public-sector housing and creating a consumer-oriented housing market (Li and Huang, 2006). The welfare-based provision of housing by danwei has been gradually removed. People began to buy their dwellings from danwei, or buy commodity housing from the market (Huang, 2003). Affordable housing is another type of housing with government-controlled prices that target at mid- or low-income groups. It was promoted due to the high prices of commodity housing, especially in big cities like Beijing and Shanghai (State Council, 1994, 1998). Affordable housing is usually for sale at prices lower than the market price and supported by government subsidies. And it is often located in suburbs because of the lower land and construction costs there. Similar to affordable housing, relocated housing is also a type of policy-related housing and supported by the government. But it aims to settle down residents whose housings are demolished due to urban renewal projects or land development. Relocated housing is also usually located in suburban areas, while the income level of residents could be quite diversified. Rapid suburbanization in Beijing happened almost simultaneously with the housing reform. Some danwei compounds were originally located in suburban areas. Due to residential suburbanization, many commodity housing complexes were gradually built in the suburbs. In addition, affordable housing and relocated housing also tend to be built in the suburbs in order to reduce land costs. As a result of this housing development process, highly heterogeneous spaces emerged in Beijing’s suburbs where various types of communities (i.e., housing developments) with different kinds of housing and built environments coexist (unlike the situation in typical suburban areas in the U.S. which are dominated largely by low-density single-family housing; Zhou and Logan, 2008; Kwan et al., 2014). While these various types of communities are located quite close to each other, their residents’ activity-travel behavior and activity spaces are very different (Wang et al., 2011). There are several studies to date that explores the relationships between urban housing and people’s activity-travel behavior in Chinese cities (Pan et al., 2009; Wang and Chai, 2009; Wang et al., 2011; Zhao and Chai, 2013). Pan et al. (2009) examined the effects of urban spatial transformation on travel in Shanghai. It demonstrated that residents in traditional neighborhoods travel shorter distances and provided evidence to support the reduction of their reliance on motorized modes by building pedestrian/cyclist-friendly urban forms. Another study in Beijing by Wang and Chai (2009) investigated the impacts of the housing reform and the influence of danwei on jobs-housing relationships and commuting behavior. It found that danwei commuters have shorter commuting trips and higher usage of non-motorized transport modes than those in commodity housing. Further, two other studies characterized the built environment in terms of different types of residential communities and discussed the effects of housing community type on activity-travel behavior (Wang et al., 2011; Zhao and Chai, 2013). Existing studies showed significant variations in people’s activity-travel behavior among different community types in China. While the influence of built environment factors such as facility density on activity-travel behavior could also be different in various community types (e.g., danwei employees might be more willing to go to several specific service places since they

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are part of their welfare, and residents of relocated communities might link more with the facilities around previous residential areas due to their social networks). However, there is little research on the effect of community type on the space–time fixity/flexibility of the residents who live in different housing communities, and the interactive effects of community type and the built environment are rarely considered. As discussed above, community type in urban China is closely associated with a particular set of unique historical and institutional factors that are beyond the socioeconomic attributes of individuals and households. On the one hand, there may still be some association between socioeconomic attributes with the type of residential community one lives in (e.g., danwei employees live in danwei housing, low-income households live in affordable housing, and high-income households live in commodity housing). On the other hand, facility density might also be correlated with community type to a certain degree (e.g., facilities around affordable housing and relocated housing might not be well developed), but their relationships are very complicated. So the variable of community type, which is a particular combination, can capture the influence of built environment factors other than socioeconomic attributes or facility density. In the Chinese context, it seems reasonable to suggest that the unique combination and density of facilities and services near one’s home and activity locations may still play an important role in shaping the structure of space–time fixity/flexibility in people’s daily life. In other words, community type in this study was used to capture the spatially contingent influence of local geographies on people’s space–time fixity constraints. In summary, it is important to study the space–time fixity/flexibility of daily activities, and the influence of the built environment is not clear, despite the large amount of existing research on the link between built environment and individuals’ activity participation and travel. There is some evidence about the variations in people’s activity-travel behavior by different community types, especially the community type during China’s transition. But the relationship between community type and people’s space–time fixity/flexibility remains unclear. This paper thus seeks to add to the existing literature by investigating the relationship between the built environment (both around people’s residence and their activity locations) and its interaction with community types on people’s space–time fixity/flexibility.

3. Data and methodology 3.1. Study area The study area for this research is in Beijing, China. A typical suburban new town in the northwest of Beijing, namely the Shangdi-Qinghe area, was chosen as the study area (Fig. 1). With a population of 380,000, the Shangdi-Qinghe area is one of the largest residential area and substantial employment center in Beijing. It covers a 16 square-kilometer area with mixed land use that includes residential, commercial, industrial and retail functions. The built environment of this area is shaped by various community types, including danwei communities, commodity housing communities, affordable housing communities, and relocated housing communities. This mixture of different community types provides a good case for research of the effect of community type on people’s space–time fixity/flexibility. The area is served by an express way and a light railway, which connect it with another residential new town to the north (i.e., the Huilongguan area), an employment center to the south (i.e., Zhongguancun Science Park), and the central city of Beijing, resulting in huge traffic flows during the rush hours. Despite the mixed

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

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Y. Shen et al. / Journal of Transport Geography xxx (2015) xxx–xxx

Fig. 1. Location of the study area.

land use, lots of people who work in this area do not live here because they cannot afford the commodity housing and do not meet the requirements for applying for affordable housing (e.g., income and Beijing hukou). Further, a large proportion of residents in the area work in other places of Beijing, which leads to serious jobs-housing spatial mismatch and serious traffic congestion (see jobs-housing distance in Table 1). 3.2. Data collection The data analyzed in the study were from an activity-travel survey conducted in the Shangdi-Qinghe area in 2012. In this survey, tracking devices and a survey website were used to collect daily travel trajectories and activity-travel information of participants for a 7-day survey period. Firstly, a tracking device, which has both GPS and mobile phone positioning functions, was provided to each participant to record his/her daily travel trajectories. The GPS positioning technology, which has better positional accuracy but could not collect reliable coordinates for indoor locations, logged the space–time coordinates of the respondents every 2 min. The mobile phone positioning technology, which was mainly used to collect coordinates for indoor locations, logged the space–time coordinates every hour. Secondly, a survey website was used for collecting activity-travel diaries and socio-demographic information. For each participant, the daily travel trajectories from the tracking device were visualized on the website, and the participant was

required to fill in the activity-travel diaries on the website according to the visualized trajectories in the end of every day during the survey. The website could also show the location and status of each device to the administrators through the monitor interface. Based on this information, the investigators could perform many supporting activities (e.g., check on the devices’ condition and communicate with participants) to ensure the successful completion of the survey. In the activity-travel diaries, we collected information about the types and basic attributes of respondents’ activity-travel behavior (e.g., the start and end time of the activity/trip, the description of the activity, companions in the activity/trip, whether or not the Internet was used during the activity/trip, etc.) as well as space– time flexibility of activities. A series of questions that assess the levels of perceived space–time flexibility of an activity were asked – for example, ‘‘How easy is it for you to change the time of day for this activity/trip?’’, ‘‘How easy is it for you to change the place for this activity?’’ Answers were given by the respondent on a five-point scale, ranging from difficult ( 2) to easy (2). From the survey, we obtained activity-travel data with high levels of spatial and temporal resolution and adequate information about the space–time flexibility of activities. In this study, we categorized the 18 types of activities recorded in the activity-travel diaries into seven categories: personal care (including personal care, sleeping, eating, medical visit), housework (including housework taking place at home, child/old people care), work, shopping, leisure (including recreation, walking,

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

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Y. Shen et al. / Journal of Transport Geography xxx (2015) xxx–xxx Table 1 A summary of sample characteristics. Variables

Classification

Census

Residents

Employees

Total sample

Gender

Male

%

N

%

N

%

N

53.8

229

47.7

102

44.5

331

46.7

11,555

0.266

Age

Female 15–29

46.2 42.7

251 88

52.3 18.3

127 153

55.5 66.8

378 241

53.3 34.0

14,706 9350

0.175 0.211

Hukou

30–49 P50 With Beijing Hukou

34.2 23.1 44.5

331 61 429

69.0 12.7 89.4

73 3 72

31.9 1.3 31.4

404 64 501

57.0 9.0 70.7

14,219 2692 17,927

0.244 0.078 0.213

Education

Without Beijing Hukou High school or lower

55.5 59.9

51 100

10.6 20.8

157 7

68.6 3.1

208 107

29.3 15.1

8334 3995

0.221 0.257

36.1 4.0

Marital status

College/university Graduate school Single

302 78 45

62.9 16.3 9.4

198 24 124

86.5 10.5 54.1

500 102 169

70.5 14.4 23.8

18,598 3668 6370

0.218 0.157 0.197

Driving license

Married Others With driving license

434 1 223

90.4 .2 46.5

103 2 68

45.0 .9 29.7

537 3 291

75.7 0.4 41.0

19,713 178 10,416

0.217 0.708 0.249

Without driving license Fully employed

257 408

53.5 85.0

161 225

70.3 98.3

418 633

59.0 89.3

15,845 23,384

0.193 0.226

72 96

15.0 20.0

4 16

1.7 7.0

76 112

10.7 15.8

2877 3988

0.130 0.094

281 103 3.1 7.4

58.5 21.5

165 48 2.0 9.4

72.1 21.0

446 151 2.8 8.1

62.9 21.3

16,972 5301

480

67.7

229

32.3

709

100.0

26,261

Employment status Monthly income

Continuous variables

Others 2000 RMB or less 2000–6000 RMB More than 6000 RMB Household size (mean) Jobs-housing distance (mean/km)

Total

physical activity, tourism), social activities (including social activity, contact activity, surfing the internet) and other activities. Moreover, we grouped the communities in the study area into four categories according to the type of residential community in which a participant lives: danwei communities, commodity housing communities, affordable housing communities, and relocated housing communities. 3.3. Sample characteristics Survey participants were recruited from the residents and employees in the Shangdi-Qinghe area. A multi-stage cluster sampling procedure based on a 5–10% of the total population was carried out in all communities (except army compounds and urban villages) and some of the companies in this area. For each community or company, we determined the sample size according to the number of residents or employees, and then selected the participants randomly with the help of the residential committee or the human resource department. Due to the survey methods, each participant needed to be over 14 years old and be able to use the Internet by himself/herself or with the help of his/her family. A total of 791 participants were recruited from 23 communities and 19 companies to participate in the survey, and 709 of them completed the online dairies. This is equivalent to an 89.6% completion rate and is pretty high in this kind of survey, largely due to the investigators’ support and the survey completion reward. In the end, dairies and GPS trajectories of 709 respondents were usable for this study, among them 480 were community residents and 229 were company employees. Table 1 shows the sample characteristics. For all the respondents, the share of females (53.3%) is a little higher than males (46.7%). Most respondents are under 50 years old, and more than

%

Average temporal flexibility

Average spatial flexibility

N

ANOVA

N

28.796 (0.000)

11,527

0.990

14,662 9318

0.967 0.940

14,191 2680 17,871

1.008 0.904 0.998

8318 3977

0.933 0.968

18,552 3660 6359

0.955 1.099 0.902

19,652 178 10,392

1.000 1.157 1.006

15,797 23,316

0.958 1.020

2873 3973

0.632 0.789

0.217 0.301

16,934 5282

0.984 1.096

0.215

26,189

0.977

Mean

16.950 (0.000)

0.188 (0.664) 5.248 (0.005)

12.228 (0.000)

10.456 (0.001) 12.606 (0.000) 26.188 (0.000)

Mean

ANOVA 2.163 (0.141) 14.957 (0.000)

14.831 (0.000) 19.835 (0.000)

16.202 (0.000)

9.055 (0.003) 241.439 (0.000) 67.920 (0.000)

70% have Beijing’s hukou (i.e., they are registered residents of Beijing and thus are local residents). Most of the respondents are well educated, and more than 75% are married. 41% of the participants own driving licenses, and nearly 90% have a full-time job. 62.9% of the participants have a monthly income between RMB 2000 and RMB 6000, which is middle-level income in Beijing, China. The average household size is 2.8 persons, and the average jobs-housing distance is 8.1 km. Among the respondents, characteristics of the company employees are different from the community residents, since there are many high-tech companies in the study area. Compared to the residents, the employees are younger, less likely to have Beijing hukou, and a little more likely to have a driving license. Their education and income are more concentrated at the middle level. Meanwhile, they have smaller average household size and longer average jobs-housing distance. Although the respondents are quite diversified in their sociodemographic characteristics, there is a certain degree of sampling bias. First, because of the use of the Internet for the survey, we only recruited people who were able to use the Internet, and this had probably led to a higher education level for the respondents. In addition, because we recruited the community residents with the help of residential committees, migrant workers who live in the area were probably missed. This had likely led to a higher proportion of community residents with Beijing hukou and a higher education level than the population at large. As Table 1 shows, when compared to the population characteristics based on the census data of the Shangdi-Qinghe area, there is an over-representation of residents with Beijing hukou, have higher education level and in the middle-age group in the sample. However, despite the sampling bias, the results of this study still seem to be quite informative of the situation of the sample group.

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

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Descriptive statistics and analysis of variance were used for initial exploration of the flexibility levels and their differences among the participants (as shown in Table 1). Temporal flexibility levels are available for 26,261 activities of the respondents, and the average value is 0.215 which is a little closer to fixed (or difficult to change). As the ANOVA results show, the differences in temporal flexibility are significant with respect to gender, age, education, marital status, driver license ownership, employment status, and monthly income. On the other hand, spatial flexibility levels are available for 26,189 activities, and the average value is 0.977, which indicates that activities tend to be more spatially fixed than temporally time (Cullen and Godson, 1975). As the ANOVA results show, the differences in spatial flexibility levels are significant with respect to age, Beijing hukou ownership, education, marital status, driver license ownership, employment status, and monthly income. 3.4. Ordered logit models In this research, the spatial flexibility and temporal flexibility of activities were estimated using four ordered logit models (OLM), since the flexibility of an activity was assessed at an ordinal scale with five levels ranging from difficult ( 2) to easy (2). There were several considerations in choosing to use the ordered logit model in this study. First and ideally, a hierarchical or multilevel model would be able to take into account the nested structure of the data: spatial or temporal flexibility is a characteristic of activities, these activities were performed by the respondents, and these respondents live in particular community types. However, since there are seven activity categories, 709 respondents, and four 4 community types, the limited degrees of freedom would render the parameter estimates of hierarchical models unstable and the results unreliable. Second, since some of the independent variables in the models may also be influenced by the level of temporal or spatial flexibility, our models do not necessarily indicate causal relationships but only statistical associations. Third, since temporal and spatial flexibility are not entirely independent of each other, we should have used other models to investigate their joint determination by the independent variables (e.g., a joint multinomial-logit ordered-probit model). While spatial flexibility and temporal flexibility are not independent of each other, their relationship is complex and far from clear. Most existing studies on space–time constraints tend to estimate spatial fixity and temporal fixity separately (see Cullen and Godson, 1975; Doherty, 2006; Schwanen et al., 2008) or use a single aggregate measure of space–time fixity (see Kwan, 2000). This study follows an established trend in keeping spatial and temporal flexibility separate and keeping model complexity manageable given the already large number of independent variables used. We recognize that the ordered logit models we used may suffer from specification issues like these and thus would like to remind the readers to be cautious when reading the results. We built four ordered logit models (OLM) in total in this study. Models 1 and 2 use temporal flexibility of each activity as the dependent variable, while Models 3 and 4 use spatial flexibility. In this study, we focused on the impacts of spatial factors and other variables that were observed to have significant effects on temporal or spatial flexibility, including individual and household socio-demographic characteristics. Spatial factors for representing the built environment were facility density around the respondent’s residential location and facility density around the location of the activity. Other spatial factors included distance from the respondent’s residential location to the city center and to the workplace. Tiananmen Square was used as the city center in this study. For facility density, we first identified the location of each respondent’s home and their activities and then calculated the

facility density within a 1-km radius circular zone around each residential location or activity location using points of interest (POIs) data of Beijing and ArcGIS 10.0. The POIs data were collected by the urban planning department of Beijing and include all kinds of facilities in Beijing in 2012, which is over the similar period with the survey data. The individual and household variables include gender, age, individual income, hukou ownership, education, marital status, driver license ownership, household size, car ownership, and presence of children or elderly in the household. In addition, activity attributes such as activity type, duration and start time were also incorporated into the models as independent variables (Schwanen et al., 2008). For Models 1 and 3, the spatial factors are the key independent variables. While the interactions of spatial factors with community type (as a joint effect term) were key independent variables for Models 2 and 4. Estimations were carried out using SPSS 19.0 software. We examined whether residential community type is a significant influence on the space–time flexibility of an activity by testing the statistical significance of the interaction between spatial factors (facility density at the residential and activity location) and community type, which was represented with a series of dummy indicators (1 = yes, 0 = no) for each of the community type: commodity housing, danwei housing, affordable housing, and relocated housing. This allowed us to explore how community type intersects with spatial factors in the shaping of temporal and spatial flexibility. 4. Results The estimated coefficients for the four ordered logit models are shown in Table 2. In the models of temporal flexibility, the R-square values of Models 1 and 2 are 0.215 and 0.213 respectively, while both of the p-values are 0.000. In the models of spatial flexibility, the R-square values of Models 3 and 4 are 0.115 and 0.111 respectively, and the p-values are also both 0.000. The results show reasonable explanatory power of the models, and the models of temporal flexibility explain better than the models of spatial flexibility. 4.1. Spatial factors and space–time flexibility The results of Models 1 and 3 show the relationship between spatial factors and space–time flexibility. Longer job-housing distance is highly correlated with higher levels of temporal and spatial fixity in respondents’ daily activities, which is consistent with general expectations and shows that longer job-housing distance would result in stronger fixity constraints in both space and time. There is significant positive relationship between facility density at the activity location and temporal and spatial flexibility when other individual socio-demographic and activity variables are taken into account. Activities that happen at places with higher facility density are more flexible in both space and time. These results are not surprising since adequate facilities bring about larger choice sets with regard to the time and places of activities, which weaken the space–time constraint for people. There is also a possibility that activities that are more flexible in space and time are more likely to happen at convenient locations with abundant facilities, since the models mainly indicate association but not causal relationships. Interestingly, there is significant negative relationship between facility density at respondents’ residential location and spatial flexibility when other variables are taken into account. This seems to suggest that residential areas with lower facility density tend to have fewer facilities for undertaking fixed activities. As fixed activities are important space–time pegs in people’s daily activity schedule, people who live in areas with lower facility density are

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

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Y. Shen et al. / Journal of Transport Geography xxx (2015) xxx–xxx Table 2 Ordered logit models results. Variable category

Variables

Temporal flexibility Model 1 Coefficient

*

Coefficient

Model 3 P-value

Coefficient

Model 4 P-value

Coefficient

P-value

Male Age Hukou Education Single Driving license Part-time job Low income High income

.140*** .013*** .088 .167*** .052 .157** .151 .046 .183***

.005 .000 .244 .000 .635 .011 .207 .541 .006

.117** .018*** .050 .180*** .138 .165*** .026 .047 .248***

.012 .000 .486 .000 .161 .005 .822 .479 .000

.093* .008** .057 .042 .180 .052 .154 .129* .221***

.075 .038 .481 .103 .117 .427 .212 .097 .002

.086* .005 .090 .035 .118 .019 .066 .247*** .201***

.080 .148 .237 .159 .248 .763 .587 .000 .004

Household factors

Household size Living with children Living with elderly (>50) Numbers of cars

.008 .136** .213** .076

.844 .036 .018 .159

.009 .053 .167** .087*

.823 .388 .046 .091

.103** .125* .083 .013

.016 .068 .380 .816

.069* .069 .044 .126**

.086 .286 .621 .020

Activity attributes

Start time Duration Weekend Out-of-home Companion ICT use

.043*** .037*** .437*** .423*** .090* .136**

.000 .004 .000 .000 .096 .024

.046*** .039*** .418*** .446*** .120** .100*

.000 .002 .000 .000 .016 .077

.015** .034** .119** .317*** .062 .128**

.018 .015 .047 .000 .271 .047

.013** .041*** .091* .330*** .002 .078

.019 .002 .094 .000 .969 .188

Activity types

Personal care Housework Work Shopping Leisure Social activities Others

.126 .762*** 1.037*** .968*** .710*** .673***

.120 .000 .000 .000 .000 .000

.084 .762*** 1.051*** .905*** .682*** .573***

.267 .000 .000 .000 .000 .000

.224*** 1.067*** 1.118*** .444*** .108 .160

.009 .000 .000 .000 .348 .229

.218*** .997*** 1.206*** .439*** .074 .195

.006 .000 .000 .000 .478 .122

.002**

.041

.005***

.000

.004*** .002 .008* .009**

.003 .372 .066 .030

.007*** .000 .007 .021***

.000 .865 .117 .000

Facility density at the activity location Commodity housing ⁄ density Danwei ⁄ density Affordable housing ⁄ density Relocated housing ⁄ density Facility density at the residential location Commodity housing ⁄ density Danwei ⁄ density Affordable housing ⁄ density Relocated housing ⁄ density Jobs-housing distance Distance to Tiananmen

LR Chi2 (P-value) Pseudo R2

**

Model 2 P-value

Individual factors

Spatial factors

***

Spatial flexibility

0

0

.001

0

.006***

.772

.016*** .000 .001 .614 1431.8(0.000) .215

0

.003* .090 .005** .024 .002 .785 .003 .496 .013*** .001 .000 .940 1628.8(0.000) .213

.001

.030*** .000 .001 .775 719.3(0.000) .115

.011*** .000 .001 .026*** .027*** .001 798.2(0.000) .111

.000 .862 .824 .000 .000 .823

Significant at the 0.01 level. Significant at the 0.05 level. Significant at the 0.1 level.

less likely to be restricted to their residential areas when undertaking flexible activities. As a result, they tend to conduct more flexible activities in other locations and less flexible activities near their home. On the other hand, residential areas with higher facility density tend to have more facilities for fixed activities when compared to residential areas with lower facility density. As people can perform more of their fixed activities near their home, their flexible activities also tend to be bounded to their residential areas because of the space–time constraint imposed by the location of their fixed activities. When facilities near people’s home are adequate, they tend to conduct more fixed activities and most of the fixed activity locations are near to their home, and this leads to the observed association between higher levels of spatial fixity and higher facility density near home. 4.2. Community type, the built environment, and space–time flexibility The results of Models 2 and 4 show the relationship between community type, the built environment and space–time flexibility. Unlike Models 1 and 3, these two models consider whether community type influence the effect of two spatial factors (facility

density at the residential and activity location) on spatial and temporal flexibility. They include eight interaction terms (4 community type dummy variables  2 spatial factors) that reveal the relationships between the built environment and space–time flexibility for various community types. For commodity housing communities, there is significant positive relationship between facility density at the activity location and temporal and spatial flexibility, and significant negative relationship between facility density at the residential location and temporal and spatial flexibility. For danwei communities, only the relationship between facility density at the residential location and temporal flexibility is significant and negative. For affordable housing communities, only the relationship between facility density at the activity location and temporal flexibility is significant and positive. For relocated housing communities, there is significant positive relationship between facility density at the activity location and temporal and spatial flexibility, and significant negative relationship between facility density at the residential location and spatial flexibility. As the results show, for the commodity housing communities and relocated housing communities, both temporal and spatial

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

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Y. Shen et al. / Journal of Transport Geography xxx (2015) xxx–xxx

flexibility of activities are more sensitive to the built environment, and the differences between facility density at the activity location and the residential location are more notable. For danwei communities and affordable housing communities, the built environment is only significantly correlated with temporal flexibility. This means that when facility density in the residential areas increases, residents in commodity housing communities and relocated housing communities tend to perform more fixed activities, while residents in danwei communities and affordable housing communities are less affected. 4.3. Socio-demographic attributes and space–time flexibility Among individual socio-demographic attributes, gender, age, individual income turn out to be factors that are significantly correlated with the temporal and spatial flexibility of activities. Respondents who are male, at younger age or with higher levels of income experience higher levels of perceived space–time fixity in their daily activities. Past studies often observed that women tend to experience higher levels of space–time constraint on an everyday basis than do men (Tivers, 1985; Kwan, 2000; Schwanen et al., 2008). Yet the coefficients of the models show inconsistent results in gender differences when other variables including activity type are controlled for. A possible reason is that labor cost is cheap in China, so it is common to hire a housekeeper or a baby-sitter. In addition, many young parents in China live with their retired parents, who can provide help to the respondent in various types of household chores. These strategies weaken the fixity constraints stemmed from housework for women, while men might experience more fixity constraints from their jobs and the need to take care of the retire parents or accompany them to undertake certain activities. Also, results from the perceived space–time fixity (e.g. Schwanen et al., 2008 and this study) might be different from those from the observed space–time fixity. All else being equal, the temporal and spatial flexibility of activities is higher as respondents are older, and age is more positively correlated with temporal flexibility than with spatial flexibility. This is consistent with the situation in China where the pressure of work and life are higher for the younger generation, and the activities of older people are much more flexible in time. With respect to household socio-demographic attributes, those living with elder family members who are over 50 years old enjoy higher levels of temporal flexibility. A possible reason is that respondents living with their retired elderly parents are more likely to receive help in housework from them, which results in less fixity constraints associated with maintenance activities and more flexible time in daily life. Meanwhile, the variable of ‘‘living with children’’ is negatively correlated with temporal and spatial flexibility of activities. This indicates that respondents who live with their children have to allocate more time to taking care of their children, resulting in less flexible time and more coupling constraints in their daily lives. 4.4. Activity attributes and space–time flexibility The results of the models show high correlation between activity types and space–time flexibility. In the models, personal care (including personal care, sleeping, eating and medical visiting) was chosen as the reference category, so the coefficient was set to 0 in Table 2. According to the results, shopping is considered the most flexible activity temporally and spatially, followed by leisure activities. Compared to personal care, social activities tend to be more flexible in time but not in space. While housework (including housework, care of children and elderly persons) is more fixed in space than personal care but not in time. Work is

considered the most fixed activity in both space and time, which is consistent with general expectations. The temporal and spatial attributes of activities are also correlated with space–time flexibility. The models show that activities that happen late during the day are more flexible in both space and time than those that happen early in the day. The same is true for activities that happen in weekends (i.e., people tend to perform more flexible activities in weekends than on week days). Activities that last longer tend to be more fixed. This indicates that shorter activity duration allows for more possibilities to alter one’s activity schedule, and this is consistent with previous studies (Schwanen et al., 2008; He, 2013). When compared to activities performed at home, out-of-home activities are more fixed in time, but more flexible in space (Kwan, 1999). Activities performed with companions are more fixed in time as a result of the constraints stemmed from companion ship. When the Internet was used during an activity, no matter whether Internet use or the activity is the primary activity, the primary activity would be more flexible in both space and time. On one hand, when an activity could be accomplished online, it might become more flexible itself (e.g., online banking is spatially and temporally more flexible than visiting a brick-and-mortar bank). On the other hand, the use of the Internet often allows a person to multi-task, and the online primary or secondary activity might enhance the flexibility of the other activity being simultaneously performed. This is consistent with some of the observations in previous studies that information and communication technologies tend to weaken the links between space, time and activity (Kwan, 2002, 2007; Couclelis, 2004; Hubers et al., 2008).

5. Conclusion and discussion Recent social and economic transformation in China has led to more complex spatial structure and residents’ activity-travel behavior in Chinese cities. Examining people’s space–time fixity/flexibility and their relationships with the built environment can yield important insights for understanding many pressing urban issues in China. This study seeks to contribute to the existing literature by exploring the space–time flexibility of people’s daily activities based on a 7-day activity-travel dataset collected in a typical suburban area of Beijing. Patterns of the space–time flexibility of activities were revealed, and the relationship with the built environment and other contextual factors were analyzed. In this study, much emphasis is put on the relationship between space–time flexibility of activities and the built environment and community type. The results show that the density of facilities around the respondents’ home have a negative correlation with the spatial flexibility of their activities, whereas the density of facilities around activity locations have a positive correlation with the space–time flexibility of their activities. This suggests that the density of facilities near a person’s home and the density of facilities around a person’s activity locations have different relationship with the spatial flexibility of their activities. Since the overall structure of a person’s space–time constraints is significantly influenced by where the person lives and works, as well as by the geographic distribution of facilities and services in the urban area, this study found that the particular type of residential community in which a person live is also an important factor that affect the space–time flexibility of the person’s activity in the context of urban China (because different types of residential communities offer different combination of facilities with different densities). With respect to the different types of residential communities in the study area, the link between the built environment and space–time flexibility is relatively more significant for commodity housing communities and relocated housing communities.

Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014

Y. Shen et al. / Journal of Transport Geography xxx (2015) xxx–xxx

Differences in the relationship between space–time flexibility and the density of facilities around residential or activity locations are also more notable. For residents in danwei communities and affordable housing communities, space–time flexibility of their activities is not so sensitive to the built environment. This seems to suggest that the structure of their space–time constraints is influenced more by institutional and policy factors than by spatial factors, because danwei communities are part of the planned economy and affordable housing communities are controlled by the government and highly related to the housing policy (and as a result these, residents have less freedom to modify their residential location and structure of space–time constraints). With regard to individual and household attributes, gender and age differences in space–time constraints are not consistent with previous studies in Western countries (see Schwanen et al., 2008). This seems to reflect the influence of several important differences in cultural, family, and gender norms between China and Western societies. In China, the younger generation faces high pressure to own their own housing and to ensure their children could get good education. The retired elderly people, on the other hand, have plenty of free time since they do not have to work. Even for the elderly who have not retired yet, people tend not to let them work as long as the others due to the culture of respecting the elderly in China. Further, it is a common practice for the elderly people in China to provide help with the housework of their married adult children during their free time or after they retire. Also, the cheap labor cost in China makes it possible for many households to hire a housekeeper or a baby-sitter to help out with some of the household chores. Both of these help considerably weaken the space–time constraints stemmed from women’s household responsibilities. Lastly and as expected, activity type turns out to be a critical factor that influences the space–time flexibility of people’s activities. Significant relationships with space–time flexibility are also found for other activity attributes (e.g., timing, duration, whether taking place out of home, whether with companion, whether using internet or not). These results are useful in the urban and transportation planning context. In light of the fact that the relationships between space–time constraints and the built environment are different between areas around people’s home and activity locations, it is important to take into account the facilities and services available around people’s workplace and other places they visit when trying to address issues arising from their activity-travel behavior. Urban and transportation planners need to comprehensively consider the entire city. Otherwise, usage of the facilities around residential areas might be low even when great efforts are made to improve these facilities located in residential communities. The different results among various types of communities suggest that for residents of danwei communities and affordable housing communities, improving the facilities in these communities may not significantly improve their situation. Other factors (e.g., institutional and policy factors) need to be addressed in order to give them more freedom to choose where to live, work, shop, socialize, and entertain. With respect to policies for mitigating traffic congestion and improving work-life balance, our analysis suggests that more emphasis should be put on the temporal dimension in urban and transport management in order to provide more choices (e.g., flexible working schedules) for people to avoid rush-hour traffic congestion, as temporal flexibility of activities tend to be more important than spatial flexibility. Several caveats are in order when interpreting the results presented in this paper. First, due to the use of Internet-based survey and GPS, the socio-demographic characteristics of the individuals in our sample are slightly different from those of the large population in Beijing. There is an over-representation of residents with Beijing hukou and participants who are highly educated and in

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the middle-age group in the sample. Second, for various reasons ordered logit models were used in this study (instead of hierarchical or multilevel models). These models did not take into account the nested structure of the data. Third, temporal and spatial flexibility are not entirely independent of each other, we should have used other models to investigate their joint determination by the independent variables (e.g., a joint multinomial-logit ordered-probit model). Due to these limitations, it is important to recognize that the ordered logit models we used may suffer from specification issues and the results presented in this paper should be interpreted with caution. Acknowledgements This research was supported by the National Natural Science Foundation of China (41228001). The authors would like to thank Zifeng Chen for his help in editing early versions of the paper. Insightful comments from the three anonymous reviewers and Donggen Wang are gratefully acknowledged; they significantly improved the manuscript. References Arentze, T., Timmermans, H., 2000. ALBATROSS: A Learning-Based Transportation Oriented Simulation System. Eindhoven University of Technology, EIRASS. Boarnet, M.G., 2011. A broader context for land use and travel behavior, and a research agenda. J. Am. Plan. Assoc. 77 (3), 197–213. Boussauw, K., Witlox, F., 2011. Linking expected mobility production to sustainable residential location planning: some evidence from Flanders. J. Transp. Geogr. 19 (4), 936–942. Bray, D., 2005. Social Space and Governance in Urban China: The Danwei System from Origins to Reform. Stanford University Press, Stanford, California. Burns, L.D., 1979. Transportation, Temporal and Spatial Components of Accessibility. Lexington Books, Lexington, MA. Cao, X., Mokhtarian, P.L., Handy, S.L., 2009. The relationship between the built environment and nonwork travel: a case study of Northern California. Transp. Res. Part A 43 (5), 548–559. Cervero, R., Murakami, J., 2010. Effects of built environments on vehicle miles traveled: evidence from 370 US urbanized areas. Environ. Plan. A 42 (2), 400– 418. Chai, Y., 1996. Danwei-centered activity space in Chinese cities: a case study of Lanzhou. Geogr. Res. 15 (10), 30–38 (in Chinese). Couclelis, H., 2004. Pizza over the internet: e-commerce, the fragmentation of activity and the tyranny of the region. Entrep. Region. Dev. 16 (1), 41–54. Cullen, I., Godson, V., 1975. Urban networks: the structure of activity patterns. Prog. Plan. 4 (1), 1–96. Cullen, I., Godson, V., Mayor, S., 1972. The structure of activity patterns. In: Wilson, A.G. (Ed.), Patterns and Processes in Urban and Regional Systems. Pion, London. Doherty, S.T., 2006. Should we abandon activity type analysis? Redefining activities by their salient attributes. Transportation 33 (6), 517–536. Ewing, R., Cervero, R., 2010. Travel and the built environment. J. Am. Plan. Assoc. 76 (3), 265–294. Giuliano, G., 2004. Land use and travel patterns among the elderly. In: pp. 192–210 Transportation in an Aging Society: A Decade of Experience, Conference Proceedings 27.Washington, DC: Transportation Research Board. Hägerstrand, T., 1970. What about people in regional science? Pap. Reg. Sci. 24 (1), 6–21. Handy, S., 2005. Planning for accessibility: in theory and in practice. In: Levinson, D., Krizek, K.J. (Eds.), Access to Destinations. Elsevier, Oxford. Hanson, S., Pratt, G., 1995. Gender, Work, and Space. Routledge, London. He, S.Y., 2013. Does flexitime affect departure time choice for morning home-based commuting trips? Evidence from two regions in California. Transport Policy 25, 210–221. Huang, Y., 2003. Renters’ housing behavior in transitional urban China. Hous. Stud. 18 (1), 103–126. Huang, Y., 2004. The road to homeownership: a longitudinal analysis of tenure transition in urban China (1949–1994). Int. J. Urban Reg. Res. 28 (4), 774–795. Hubers, C., Schwanen, T., Dijst, M., 2008. ICT and temporal fragmentation of activities: an analytical framework and initial empirical findings. Tijdschrift voor Economische en Sociale Geografie 99 (5), 528–546. Jones, P.M., Dix, M.C., Clarke, M.I., et al., 1983. Understanding Travel Behavior. Gower Publishing Co., Ltd, Aldershot, UK. Kwan, M.-P., 1998. Space-time and integral measures of individual accessibility: a comparative analysis using a point-based framework. Geogr. Anal. 30, 370–394. Kwan, M.-P., 1999. Gender, the home-work link, and space-time patterns of nonemployment activities. Econ. Geogr. 75 (4), 370–394. Kwan, M.-P., 2000. Gender differences in space–time constraints. Area 32 (2), 145– 156.

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Please cite this article in press as: Shen, Y., et al. Space–time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. (2015), http://dx.doi.org/10.1016/j.jtrangeo.2015.06.014