Exploring relationships of grocery shopping patterns and healthy food accessibility in residential neighborhoods and activity space

Applied Geography 116 (2020) 102169

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Exploring relationships of grocery shopping patterns and healthy food accessibility in residential neighborhoods and activity space Jingjing Li a, Changjoo Kim b, * a b

Department of Geography & GIS, 401 Braunstein Hall, University of Cincinnati, Cincinnati, OH, 45221, USA Department of Geography & GIS, 400B Braunstein Hall, University of Cincinnati, Cincinnati, OH, 45221, USA

A R T I C L E I N F O

A B S T R A C T

Keywords: Healthy food accessibility Grocery shopping patterns Spatial contextual units Neighborhood effects Activity space

Spatial access to healthy foods has drawn much attention due to its potential to facilitate healthy eating habits, and subsequently reduce the risk of chronic diseases. However, the associations of healthy food accessibility with grocery shopping patterns depending on various spatial contextual units remain little known. This study aimed to (1) compare healthy food accessibility among residential neighborhoods with varying buffer sizes and activity space; (2) examine the associations of grocery shopping patterns and healthy food accessibility in residential neighborhoods and activity space; (3) explore the differences in healthy food environment and the associations between healthy food accessibility and grocery shopping patterns by populations density depending on resi­ dential neighborhoods and activity space. Data came from GPS-based Household Interview Survey for the Cin­ cinnati Ohio Region conducted during 2009–2010, of which final sample consisted of 1625 participants. The results illustrated that (1) healthy food accessibility in activity space had greater variability than in residential neighborhoods; (2) healthy food accessibility had significant associations with grocery shopping patterns across various spatial contextual units; and (3) healthy food accessibility and its associations with grocery shopping patterns differed significantly by population density in residential neighborhoods but not in activity space. This study provides a deeper insight into the relationships between grocery shopping patterns and healthy food accessibility by accounting for various spatial contextual units. It also improves nuanced understanding of the spatial heterogeneity in healthy food environment and its associations with grocery shopping patterns between more urbanized areas and more rural areas depending on residential neighborhoods and activity space. One implication is that improving healthy food exposures to people’s residence and other routine activity destinations may encourage them to visit the healthy food stores for food purchase. Another implication is that future research should carefully consider the place effects in evaluating people’s food environment in residential neighborhoods and its associations with grocery shopping behaviors.

1. Introduction Healthy food accessibility has drawn much attention due to its po­ tential to shape or strengthen healthy eating habits, and subsequently reduce the risk of obesity and other chronic diseases. The concept of accessibility encompasses multiple dimensions. We focused on the spatial dimension and measured it using the ‘container approach’, which was the number of facilities or services in a pre-specified spatial scale (Talen & Anselin, 1998). We defined healthy food accessibility as the number of healthy food stores in a spatial scale. Healthy food stores consist of supermarkets and large grocery stores that sell a variety of fresh fruits and vegetables. Research has indicated that healthy food

accessibility is associated with grocery shopping behaviors: greater healthy food accessibility is associated with shorter grocery shopping distance (Hillier et al., 2011; Hirsch & Hillier, 2013; Kerr et al., 2012; Shannon & Christian, 2017; Thornton, Crawford, Lamb, & Ball, 2017), and is associated with more frequent grocery shopping (He et al., 2012; Shearer et al., 2015; Widener et al., 2018). Despite growing empirical evidence, there remain some challenges in the literature. The uncer­ tainty in the spatial delineation of contextual units could have affected the associations between food accessibility and health-related behaviors (Black, Moon, & Baird, 2014; Kwan, 2012a, 2012b; Roux, 2007; Wilkins, Morris, Radley, & Griffiths, 2017). Most studies measured food acces­ sibility in residential neighborhood (Feng, Glass, Curriero, Stewart, &

* Corresponding author. E-mail addresses: [email protected] (J. Li), [email protected] (C. Kim). https://doi.org/10.1016/j.apgeog.2020.102169 Received 4 June 2019; Received in revised form 17 January 2020; Accepted 3 February 2020 Available online 17 February 2020 0143-6228/Published by Elsevier Ltd.

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Applied Geography 116 (2020) 102169

Schwartz, 2010; Larson & Story, 2009; Walker, Keane, & Burke, 2010), either by an administrative boundary (e.g. census tract) (Apparicio, Cloutier, & Shearmur, 2007; Bower, Thorpe, Rohde, & Gaskin, 2014; Eckert & Vojnovic, 2017; Grow et al., 2010; Lamichhane et al., 2013; Lee & Lim, 2009; Li, Harmer, Cardinal, Bosworth, & Johnson-Shelton, 2009; Mooney et al., 2018; Moore & Diez Roux, 2006; Morland, Wing, & Roux, 2002; Pearce, Hiscock, Blakely, & Witten, 2008; Powell, Slater, Mirtcheva, Bao, & Chaloupka, 2007; Zenk et al., 2005), or by a home location buffer (Bernsdorf et al., 2017; Frank et al., 2009; Helbich, Schadenberg, Hagenauer, & Poelman, 2017; Jeffery, Baxter, McGuire, & Linde, 2006). A few studies compared healthy food accessibility among multiple delineations of residential neighborhoods (e.g. census tract and block group (Barnes et al., 2016); home location buffers with varying sizes (DuBreck et al., 2018; Burgoine, Alvanides, & Lake, 2013; Thorn­ ton, Pearce, Macdonald, Lamb, & Ellaway, 2012)) and found that different definitions of residential neighborhoods could affect the magnitude of food accessibility and its associations with health out­ comes. Another aspect of the uncertainty is that an individual’s resi­ dential neighborhood may represent an incomplete geographic context for assessing health effects of food accessibility as it does not account for the areas around other relevant activity destinations along the course of people’s daily mobility (Chen, 2017; Chen & Kwan, 2015; Cummins, 2007; Holliday, Howard, Emch, Rodriguez, & Evenson, 2017; Inagami, Cohen, & Finch, 2007; Inugamis et al., 2006; Kwan, 2013, 2018; Li, Kim, & Sang, 2018; Perchoux et al., 2015, 2016). Indeed, some studies found that food environment at school locations (He et al., 2012), workplaces (Burgoine & Monsivais, 2013; Thornton, Lamb, & Ball, 2013), or other activity destinations (Kerr et al., 2012) also had impacts on food pur­ chase/consumption behaviors. The relationships between food envi­ ronment and individuals’ food purchase behaviors are complex (Ghosh-Dastidar et al., 2017; Zhang & Mao, 2019), highlighting research needs on investigating food accessibility at more dynamic contextual units (Clary, Matthews, & Kestens, 2017; Shareck, Frohlich, & Kestens, 2014). Recent studies have introduced the concept of activity space, rele­ vant space along the course of daily activities (Golledge & Stimson, 1997), to assess food exposures at both residential and non-residential locations during daily movement (Crawford, Pitts, McGuirt, Key­ serling, & Ammerman, 2014; Kestens, Lebel, Daniel, Th�eriault, & Pam­ palon, 2010; Li & Kim, 2018; Ravensbergen, Buliung, Wilson, & Faulkner, 2016; Widener et al., 2013, 2018; Zenk et al., 2011). A couple of methods have been developed to construct an individual’s activity space: standard deviational ellipse (SDE), road network buffer, activity location buffer, convex hull, and kernel density surface (Chaix et al., 2012; Sherman, Spencer, Preisser, Gesler, & Arcury, 2005). For instance, a few studies extended the spatial contextual units from residential neighborhoods to workplaces by incorporating commuting patterns to estimate the spatial/spatio-temporal healthy food accessibility (Salze et al., 2011; Widener et al., 2013). Besides, a probability function method has been applied to generate a kernel density estimation surface (Hughey et al., 2019; Kestens et al., 2010; Wei, She, Zhang, & Ma, 2018; Widener et al., 2018) or a time–geographic density estimation surface (Horner & Wood, 2014) to estimate individuals’ food accessibility along their daily mobility paths. An alternative is a travel-time surface in which the value in each cell represents the accumulate cost between the centroid of the cell and the location of the nearest food stores (Mul­ rooney, Beratan, McGinn, & Branch, 2017). Some studies have employed GPS-tracking data to explore the associations between food environment in various geographic shaped activity spaces (i.e. standard deviational ellipse or road network buffer) and dietary behaviors (Christian, 2012; Kestens et al., 2012; Sadler, Clark, Wilk, Connor, & Gilliland, 2016; Zenk et al., 2011). Empirical findings derived from GPS-based mobility of exposure measures have demonstrated that food accessibility in activity spaces were strongly correlated with dietary behaviors (Cetateanu & Jones, 2016). A small amount of studies compared food environment in both

residential neighborhoods and activity spaces (Burgoine & Monsivais, 2013; Crawford et al., 2014; Sadler & Gilliland, 2015; Shearer et al., 2015). However, the evidence about the associations between healthy food accessibility in various spatial contextual units and grocery shop­ ping patterns remains scarce. Further, previous studies primarily focused on food shopping frequency (He et al., 2012; Minaker et al., 2016; Shearer et al., 2015; Widener et al., 2018), or food shopping distance (Hirsch & Hillier, 2013; Kerr et al., 2012; Thornton et al., 2017). The geographic fit of each spatial contextual unit with the gro­ cery shopping destinations was understudied. This study attempted to bridge the gaps by using detailed household GPS survey data to inves­ tigate the spatial scale effects on the relationships between grocery shopping patterns and healthy food accessibility. This study aims to: (1) compare healthy food accessibility among residential neighborhoods with varying buffer sizes and activity space; (2) examine the associations between grocery shopping patterns and healthy food accessibility in residential neighborhoods and activity space; (3) explore the differences in healthy food environment and the associations between healthy food accessibility and grocery shopping patterns by populations density depending on residential neighborhoods and activity space. 2. Methods 2.1. Data This study primarily relied on the GPS-based Household Interview Survey for the Cincinnati Ohio Region (Ohio DOT, 2012), which was initiated by the Ohio Department of Transportation, in cooperation with the OKI Regional Council of Government and the Metropolitan Planning Organization for Cincinnati. The survey randomly recruited over 5000 households during August 2009 and August 2010 with proportional distribution, of which participants were living in the OKI region con­ sisting of 8 counties (Butler, Clermont, Hamilton, Warren, Boone, Campbell, Kenton, Dearborn) (Fig. 1). Each household member over the age of 12 was asked to carry a personal GPS device with them every­ where they went for a period of 3 consecutive days. Each participant was also asked to report their household and personal information in a separate survey form. A total of 2796 households completed survey forms and carried GPS devices with them. Other data included the spatial data of food outlets with latitude/longitude information, which were obtained from the Database of Supplemental Nutrition Assistance Program (SNAP) (SNAP, 2018). As we focused on healthy food acces­ sibility, we selected healthy food stores from the SNAP database as a proxy for healthy food environment, which were defined as large gro­ cery stores/supermarkets selling a variety of fresh fruits and vegetables. We identified the large grocery stores and supermarket chains from the database based on their names, websites, and Google Map Imagery (Google Map Data©, 2018). Road data were obtained from U.S. Census Bureau (USCB, 2017). 2.2. Identifying grocery shopping trips Supermarkets/grocery stores were identified based on their names, websites, and the SNAP database. Grocery shopping trips referred to trips for actual visits to the supermarkets/grocery stores. The GPS survey recorded the activity purposes in five categories: home, work, school, shopping, and others. To identify grocery shopping trips, first, we selected trips of which the activity purpose was ‘shopping’. Second, we identified grocery shopping trips by selecting shopping trips of which the destinations spatially coincided with the locations of the grocery stores/supermarkets using spatial queries in ArcMap version 10.5 (ESRI, 2016) as in Shearer et al. (2015). We verified the grocery trips by checking if the GPS destinations falling in the parking lots of the cor­ responding supermarkets/groceries using Google Map Imagery (Google Map Data©, 2018), which could improve the accuracy of grocery trips. 2

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Applied Geography 116 (2020) 102169

Fig. 1. The spatial context of study area.

2.3. Definitions of spatial contextual units

excluding those that fell beyond a 10-mile buffered area around the study area and the actual grocery shopping destinations.

We defined two types of spatial contextual units: residential neigh­ borhood, and activity space. A residential neighborhood was repre­ sented by a street network buffer around an individual’s residence. The buffer size ranges included 1-mile (Fig. 2a), 2-mile (Fig. 2b), and 3-mile (Fig. 2c). We employed one-standard deviational ellipse (SDE) to delineate a participant’s activity space (Fig. 2d). An SDE was con­ structed based on the statistical dispersion of the activity destinations. The origin of the ellipse is the mean center of the points; the major axis and minor axis of the ellipse correspond to the maximum and minimum standard deviations of the points, respectively (Yuill, 1971). Regarding the activity destinations for constructing the SDE, we excluded those that fell beyond the 10-mile buffered area of the study area as they were non-local with little interactions with local food environment. We also excluded the grocery shopping destinations to mitigate the confounding bias (Chaix et al., 2013) as they were related with both the grocery shopping patterns and healthy food environment. Therefore, the final activity nodes to construct the SDE for each participant consisted of the residence and other activity destinations during the 3-day survey period,

2.4. Analytical sample We retained participants who had at least one grocery shopping trip. We further excluded those with missing values for sociodemographic characteristics except annual household income. Missing values for in­ come were recoded as a dummy category (Table 1). These selections yielded 1625 participants with 2633 grocery trips and 29,023 other trips. The sampled weekly average grocery shopping frequency corre­ sponds with the U.S. consumers’ average weekly grocery shopping trips of 1.5 in 2017 (FMI, 2018). The descriptive statistics for the sample characteristics is shown in Table 1. The participates lived across the study area with little clustering. The sample consisted of 58% females. The average age was 50 (SD: 17). About 62% of the participants had annual household income greater than or equal to $50,000. In general, the participants in the analytical sample were diverse in age, gender, annual household income, and employment status. The sociodemo­ graphic characteristics of the retained participants were similar to those 3

Applied Geography 116 (2020) 102169

J. Li and C. Kim

Fig. 2. Spatial contextual units: (a) Residential neighborhood at 1-mile (b) Residential neighborhood at 2-mile (c) Residential neighborhood at 3-mile (d) Activity space: One-standard SDE.

of population in census areas from which they came, except that the participants in our sample were older than census population (Table 1).

Table 1 Descriptive statistics for sample characteristics. Sample characteristics (N ¼ 1,625)

2010 Census population characteristics (N ¼ 1,980,114)

Median

Interquartile Range

Median

Interquartile Range

6.46

–

–

Percent (%)

Number

Percent (%)

35.14 45.66 19.20

1,222,905 520,271 236,938

61.76 26.27 11.97

42.09 57.91

965,827 1,014,287

48.78 51.22

30.52 62.52 6.95

675,981 1,304,133

34.14 65.86

57.72 42.28

1,040,025 940,089

52.52 47.48

Home-Grocery 5.14 distance (km) Sociodemographic Number characteristics Age <45 571 45-64 742 >64 312 Gender Male 684 Female 941 Annual Household Income < $50,000 496 � $50,000 1,016 Missing 113 Employment status Work 938 Not to work 687 Population density/ per tract (/km2)

Median 760.01

2.5. Derived variables 2.5.1. Grocery shopping patterns Two variables were created to represent grocery shopping patterns. The first one was the network distance (in km) from home to the actual visited grocery stores, representing how far people travel to grocery stores for shopping from home. For participants who had multiple gro­ cery shopping trips, the average distance was taken. It was referred to as home-grocery distance for simplicity. The second one was a dichoto­ mous (no/yes) variable indicating whether an actual grocery destination was inside each of the spatial contextual units. For participants who had multiple grocery shopping trips, the value of this variable was assigned as ‘yes’ if at least one of the grocery shopping destinations was inside the corresponding spatial contextual unit. 2.5.2. Healthy food environment The healthy food environment included geographic size and healthy food accessibility for each spatial contextual unit. Geographic size was measured in area (km2). Healthy food accessibility was defined as the count of healthy food stores (grocery stores/supermarkets) within a spatial contextual unit.

Interquartile Range 967.94

2.5.3. Sociodemographic characteristics Sociodemographic variables included age, gender, annual household income, and employment status. We also included population density at census tract level to represent the degree of urbanization (Table 1). We referred areas where population density was equal to or greater than 4

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J. Li and C. Kim

The 1-mile buffer had the smallest variation in geographic size (Median ¼ 2.31 km2, Interquartile range ¼ 1.84 km2). The variability in geographic size became greater with the increasing buffer ranges for residential neighborhood. The activity space had the largest variability in geographic size (Median ¼ 64.77 km2, Interquartile range ¼ 123.31 km2). The healthy food accessibility had similar variation patterns for the four spatial units, with activity spaces having the largest variation in healthy food accessibility (Median ¼ 4, Interquartile range ¼ 7), and 1mile residential neighborhood having the smallest variation in healthy food accessibility (Median ¼ 0, Interquartile range ¼ 0). The correlation between geographic size and healthy food accessibility was the strongest in activity space (rho ¼ 0.81).

median value as more urbanized areas and referred areas where popu­ lation density was less than the median value as more rural areas. 2.6. Statistical analysis For Aim 1, we conducted descriptive statistics to examine variations in geographic size and healthy food accessibility across various spatial contextual units. We also showed the percent of the correspondence between grocery destinations and each of the spatial contextual units by healthy food accessibility. For Aim 2, a multiple linear regression was used to examine the associations of home-grocery distance with healthy food accessibility, accounting for sociodemographic characteristics. As the home-grocery distance was skewed to right, we conducted logtransformation in the regression models. Healthy food accessibility was converted to a binary variable as in Thornton et al. (2017) and Hirsch and Hillier (2013): lower food access (no supermarket in each spatial unit), and higher food access (1 or more supermarkets in each spatial unit). We further used a logistic regression to predict the odds of grocery shopping destinations falling inside each of the spatial contex­ tual units, adjusting geographic size and sociodemographic character­ istics. We standardized healthy food accessibility and geographic size in each spatial contextual unit by calculating a z-score (the number of standard deviations from the mean). Regarding Aim 3, we tested the differences between two subgroups of population density: (a) more rural areas with population density less than median value and (b) more ur­ banized areas with population density equal to or greater than the me­ dian value. We employed Mann-Whitney U test to examine the differences of healthy food environment in each of the spatial contextual units by population density. To examine whether the spatial scale effects of healthy food accessibility on the odds of grocery destinations inside each of the spatial contextual units, we tested the interaction effects between population density and healthy food accessibility in each of the spatial contextual units and presented stratified analysis by population density.

3.2. Associations between healthy food accessibility and grocery shopping patterns

The variations in healthy food environment across various spatial contextual units are shown in Table 2. The geographic size and healthy food accessibility varied greatly across various spatial contextual units.

Table 3 shows the relationships between home-grocery distance and healthy food accessibility. After controlling for sociodemographic characteristics, home-grocery distance was significantly shorter (p < 0.001) for participants with higher healthy food accessibility than those with lower healthy food accessibility regardless of varying residential neighborhood scales (Table 3). The relationship between home-grocery distance and healthy food accessibility in the activity space was not significant. Table 4 shows the percent of participants whose grocery shopping destinations were inside each of the spatial contextual units. The percent of correspondence increased with increasing buffer ranges for residen­ tial neighborhood, with 1-mile captured the lowest percent (9.05%). Comparing with residential neighborhoods, the activity space captured the highest percent (61.91%). This pattern remained consistent when we split the percent of participants’ grocery destinations inside by the percentile of healthy food accessibility. From the lowest to the highest percentile of healthy food accessibility, the activity space measure al­ ways captured the highest percent of participants’ grocery destinations. Meanwhile, the percent of participants whose grocery destinations being located inside each spatial contextual unit increased by the increase in the healthy food accessibility (by percentile) in the corresponding spatial unit (Table 4). Table 5 shows the odds of grocery destinations falling inside each of the spatial contextual units by healthy food accessibility, adjusting geographic size, sociodemographic characteristics, and population density. The odds of capturing the grocery destinations in each spatial contextual unit increased significantly by the increase in healthy food accessibility (all p < 0.05).

Table 2 Geographic size and healthy food accessibility in various spatial contextual units.

3.3. The differences in healthy food environment and the associations between healthy food accessibility and grocery shopping patterns by population density

3. Results 3.1. Variations in healthy food accessibility across various spatial contextual units

N ¼ 1,625

Geographic size (km2): median (IQR) By Population density < Median � Median Mann-Whitney U test Healthy food accessibility (count): median (IQR) By Population density < Median � Median Mann-Whitney U test Spearman’s rank correlation between geographic size and healthy food accessibility

Residential neighborhood

Activity space

1-mile

2-mile

3-mile

SDE

2.31 (1.84) Mean 1.74 3.08 p< 0.001 0 (0)

10.26 (7.42) Mean 7.35 13.34 p< 0.001 1 (2)

26.08 (15.85) Mean 19.74 32.58 p< 0.001 2 (3)

64.77 (123.31) Mean 162.86 104.21 p < 0.001

Mean 0.14 0.44 p< 0.001 0.40

Mean 0.60 1.77 p< 0.001 0.67

Mean 1.38 3.85 p< 0.001 0.75

Mean 7.33 6.96 p ¼ 0.43

The Mann-Whitney U test in Table 2 shows that geographic sizes and healthy food accessibility in all three residential neighborhood measures in more rural areas (population density < median) were smaller than those in more urbanized area (population density�median). For the activity space measure, geographic size was larger in more rural areas than in more urban areas, whereas healthy food accessibility did not differ significantly between denser areas and more sparse areas. Table 5 shows the interaction effects (p-value) between healthy food accessi­ bility and population density and the results of stratified analysis. While we observed significant differences in the odds of the correspondence between grocery destinations and residential neighborhood measures between more urbanized areas with higher population density and more rural areas with lower population density, we found the odds of the correspondence did not differ significantly in the activity space measure.

4 (7)

0.81

5

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Applied Geography 116 (2020) 102169

Table 3 Associations between home-grocery distance and healthy food accessibility. N ¼ 1,625

Outcome: home-grocery distance (Log-transformed) 1-mile

2-mile

Coef. Health food accessibility Lower access (¼0) (Ref.) Higher access (>0) Age <45 (Ref.) 45-64 >64 Gender Male (Ref.) Female Household income a < $50,000 (Ref.) � $50,000 Employment Work (Ref.) Not to work

SE

3-mile

Coef.

SE

Coef.

SDE SE

Coef.

SE

0.71***

0.05

0.74***

0.04

0.87***

0.05

0.13

0.07

0.02 0.12

0.05 0.06

0.00 0.11

0.04 0.06

0.01 0.07

0.04 0.06

0.01 0.07

0.05 0.07

0.05

0.04

0.02

0.04

0.02

0.04

0.03

0.04

0.00

0.05

0.01

0.05

0.03

0.05

0.11*

0.05

0.04

0.06

0.05

0.06

0.05

0.03

0.04

0.07

Note: a. The missing income was coded as a dummy category and the results were not shown. *p < 0.05. ***p < 0.01. ***p < 0.001.

4. Discussion

Table 4 Percent of participants whose grocery destinations falling inside each of the spatial contextual units by healthy food accessibility. N ¼ 1,625

Residential neighborhood

Activity space

1-mile

SDE

2-mile

3-mile

We had three research aims in this study: (1) to compare healthy food accessibility among residential neighborhoods with varying buffer sizes and activity space; (2) to examine the associations of grocery shopping patterns and healthy food accessibility in residential neighborhoods and activity space; (3) to investigate the differences in healthy food envi­ ronment and associations between healthy food accessibility and gro­ cery shopping patterns by population density depending on residential neighborhoods and activity space. The healthy food environment varied greatly across various spatial contextual units. Greater healthy food accessibility in residential neighborhoods was associated with shorter home-grocery distance; greater healthy food accessibility was associated with higher odds of capturing the grocery destinations in each of the spatial contextual units. We also found that the spatial scale effects of healthy food accessibility on grocery patterns differed by population density in residential neighborhoods but not in activity space.

Percent of participants whose grocery destinations were inside the spatial contextual unit (%) All 9.05 29.11 47.02 61.91 By healthy food accessibility 0–25% percentile 0.00 0.00 26.24 39.71 25–50% percentile 0.00 42.88 51.16 62.78 50–75% percentile 0.00 48.54 61.22 75.00 75–100% percentile 37.89 50.00 69.62 79.34

Table 5 Predicted odds ratio of grocery destinations inside each of the spatial contextual units by healthy food accessibility (z-score). N ¼ 1,625

4.1. Variations in healthy food environment across different spatial scales

Grocery destinations being located inside/outside a spatial unit (model prob. ¼ inside) Residential neighborhood

Comparing with the residential neighborhood measures with varying buffer ranges, the activity space measure had the greatest variations in both geographic size and healthy food accessibility. This finding is in line with the results in previous studies that activity spaces have larger geographic sizes and greater environmental exposures than traditional residential neighborhoods (Crawford et al., 2014; Howell, Farber, Widener, & Booth, 2017; Hurvitz & Moudon, 2012; Sadler & Gilliland, 2015; Shearer et al., 2015). Further, we observed drastic variations in the correspondence of grocery destinations with various spatial contextual units by healthy food accessibility. The strongest correlation between geographic size and food accessibility in the activity space measure also supports the idea that activity spaces may be more relevant with food environment than residential neighborhoods (Crawfords et al., 2014; Cummins, 2007; Shearer et al., 2015; Zenk et al., 2011). Meanwhile, there remain challenges in constructing individuals’ activity spaces. First, the delineation of an individual’s activity space requires detailed travel diary data to record his/her daily activity locations. Second, the greater variations in the geographic sizes of activity spaces make it difficult to translate the delineation into policy implications.

Activity space

1-mile

2-mile

3-mile

SDE

OR (95% C. I.)

OR (95% C. I.)

OR (95% C. I.)

OR (95% C. I.)

Food accessibility 2.49 (2.11, (z-score) 2.94) Stratified by population density < Median 4.80 (3.17, 7.28) � Median 2.06 (1.73, 2.45) p < 0.001

2.37 (2.02, 2.76)

2.27 (1.92, 2.68)

3.26 (2.51, 4.22)

6.05 (4.11, 8.90) 1.86 (1.57, 2.20) p < 0.001

5.03 (3.54, 7.14) 1.75 (1.45, 2.12) p < 0.001

3.12 (2.18, 4.48) 3.23 (2.14, 4.87) p ¼ 0.47

Note: The logistic regression modeled the probability of grocery destinations falling inside each of the spatial contextual units; each model was adjusted by geographic size (z-score), sociodemographic variables (age, gender, annual household income, and employment), and population density.

6

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4.2. Associations between grocery shopping patterns and healthy food accessibility

research improves the nuanced understanding of the differences in healthy food environment and the heterogeneity in the associations between healthy food accessibility and grocery shopping patterns by degree of urbanization. Present study also has some limitations. First, we identified grocery trips by selecting those trips which spatially coincided with the locations of groceries as in Shearer et al. (2015). This may produce bias if the supermarkets were clustered with other shopping stores. To improve the accuracy, we further verified if the grocery trip locations were falling inside the parking lots of the supermarkets/grocery stores. Second, while we moved forward examining where people visited groceries, we lacked detailed information about food purchase and consumption. More detailed data about what food people purchase in groceries and their dietary behaviors are needed to complete the link between food environment and food purchase and consumption behaviors. Besides, regarding the relationships between home-grocery distance and healthy food accessibility, we only showed the differences between the presence of supermarkets and no supermarket available in each spatial unit. The count of supermarkets may play a more important role than the avail­ ability of one store (Thornton et al., 2012). Additionally, while activity space measures provide a comprehensive way to estimate environment exposure, they introduce the selection bias as well. Chaix et al. (2013) pointed out that GPS based mobility of exposure methods were subject to selection bias, which stemmed from the fact that it was not the environmental exposures around these selected mobility locations that affected participants’ behaviors, but the choices to visit these specific locations that led to the exposures. In order to mitigate the selection bias, we removed the activity locations of interest of behaviors (grocery shopping activity locations) to construct activity spaces as Chaix et al. (2013) suggested. However, the selection bias cannot be eliminated completely. Future research needs to design more complicated methods for GPS tracking data to avoid bias. Another limitation is that while we examined the spatial scale effects across four different spatial units, the threshold where the spatial effects of healthy food accessibility on gro­ cery shopping patterns become negligible remained largely unknown. Future research can extend discussions on how the spatial effects change by distance and identifying the threshold, providing a guide for policy interventions. Moreover, our study was restricted to an older adult population with higher income, of which the findings would not trans­ late in other populations. Older people with higher income may have increased mobility because of increased car usage (Figueroa, Nielsen, & Siren, 2014) than other populations, which could affect the geographic sizes of activity space and the spatial food accessibility. Overall, the disparities suggest that the spatial food accessibility in activity space and its associations with grocery patterns may be heterogeneous across population groups.

Healthy food accessibility had significant associations with grocery shopping patterns in varying spatial contextual units even after con­ trolling sociodemographic characteristics and population density. Greater healthy food accessibility was associated with shorter distance from home to the actual grocery destinations and associated with higher odds of the grocery destinations falling inside each of the spatial contextual units. Our finding has important policy implications as it suggests that the associations between healthy food accessibility and grocery shopping patterns persist in various spatial contextual units after controlling for sociodemographic characteristics and population density. It indicates that improving healthy food accessibility can encourage people to patronize these accessible healthy food stores, which subsequently has a potential to increase the chances of purchasing and consuming healthy foods and reduces the risk of obesity and other chronic diseases. 4.3. Differences in healthy food environment and associations between healthy food accessibility and grocery shopping patterns by population density We found that the geographic sizes at all three residential neigh­ borhoods were larger in more urbanized areas, whereas the size at ac­ tivity space was smaller in more urbanized areas than that in more rural areas. More urbanized areas have denser road networks, leading to longer distance threshold for constructing the network buffers around residence (Oliver, Schuurman, & Hall, 2007). Meanwhile, the size of the activity space is influenced by the number and the spatial distribution of the activity destinations (Gesler & Meade, 1988; Sherman et al., 2005). More urbanized areas have more facilities located close to each other that provide services for daily lives, resulting in the smaller sizes of activity spaces. This result is consistent with the finding in previous work that urban participants have larger sizes in network neighborhoods and smaller size in activity space (Crawford et al., 2014). Healthy food accessibility at residential neighborhood measures were greater in more urbanized areas. It supports the findings in previous studies that su­ permarkets are more common in urban areas than in suburban and rural areas (Crawford et al., 2014; Powell et al., 2007; Reisig & Hobbiss, 2000; Shearer et al., 2015). For activity space measure, we did not find noticeable differences in healthy food accessibility between more ur­ banized areas and more rural areas. The potential reason is that people in more rural areas need to travel longer to inner cities for work and other errands, resulting in larger activity spaces covering more healthy food stores. This result resonates with the finding in a previous study that home-based scales may exaggerate differences in food availability (Shearer et al., 2015). Additionally, population density also moderated the associations between healthy food accessibility in residential neighborhood measures and grocery shopping patterns, whereas no moderating effect was found in the activity space measure. These find­ ings suggest that the healthy food environment delineated in residential neighborhoods are not uniform across different levels of urbanization. It highlights future research need to carefully consider the place effects when delineating food environment within residential neighborhoods.

5. Conclusions This study contributes to the literature in two aspects. First, ac­ counting for various spatial contextual units in both residential neigh­ borhoods and activity space provides a deeper insight into the associations between healthy food accessibility and grocery shopping patterns. The associations between healthy food accessibility and gro­ cery shopping patterns persist across various spatial contextual units although healthy food accessibility varies. This finding has important implications that improving healthy food exposures to people’s resi­ dence and other routine activity destinations may encourage them to visit the healthy food stores for food purchase, which has a potential to improve their healthy eating behaviors, and subsequently reduce the risk of obesity and other chronic diseases. Second, this work improves nuanced understanding of the spatial heterogeneity in healthy food accessibility and its associations with grocery shopping patterns be­ tween more urban areas and more rural areas across various spatial contextual units. We found differences between more urbanized areas and more rural areas in residential neighborhoods, whereas the

4.4. Strengths and limitations One strength of this study is that the rich data from the GPS-based household interview survey allows us to describe grocery shopping patterns and construct activity spaces to represent individuals’ food environment exposures. Another strength is that this research advances food environment studies by reconciling the differences between food accessibility and grocery shopping patterns. It provides empirical evi­ dence to link food environment exposures with where people visit gro­ cery stores/supermarkets for healthy foods purchase. Further, this 7

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differences were not noticeable in activity space. Such disparities sug­ gest that the healthy food environment delineated in residential neigh­ borhoods are not uniform across different levels of urbanization. Future research and policy interventions should carefully consider the place effects in evaluating people’s food environment in residential neigh­ borhoods and its associations with grocery shopping behaviors.

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