Health & Place 17 (2011) 1202–1211

Contents lists available at SciVerse ScienceDirect

Health & Place

1353-82

doi:10.1

n Corr

E-m

journal homepage: www.elsevier.com/locate/healthplace

Neighborhood green, social support, physical activity, and stress:Assessing the cumulative impact

Yingling Fan n, Kirti V. Das, Qian Chen

Hubert H. Humphrey School of Public Affairs, University of Minnesota, 301 19th Avenue South, Minneapolis, MN 55455, USA

a r t i c l e i n f o

Article history:

Received 17 February 2011

Received in revised form

20 August 2011

Accepted 23 August 2011Available online 31 August 2011

Keywords:

Stress

Green spaces

Neighborhood

Greenness

Social support

Physical activity

Instrumental variables regression

92/$ - see front matter Published by Elsevier

016/j.healthplace.2011.08.008

esponding author. Tel.:þ1 612 626 2930; fax

ail address: yingling@umn.edu (Y. Fan).

a b s t r a c t

We estimate the cumulative stress mitigating impact of neighborhood greenness by investigating

whether neighborhood green mitigates stress directly, and indirectly by encouraging physical activity

and/or fostering social support. Using data from a recent community health survey in Chicago and two-

stage instrumental variables regression modeling, we find that different components of neighborhood

green play distinct roles in influencing stress. Park spaces are found to indirectly mitigate stress by

fostering social support. Overall neighborhood vegetation is found to have direct stress mitigation

impact, yet the impact is counteracted by its negative effect on social support. When comparing the

effect size, park spaces show a more positive impact on health and well-being than the overall

neighborhood vegetation level. Policy makers are recommended to focus on creating structured green

spaces with public recreation and socialization opportunities rather than simply conserving green

spaces in the neighborhood. Previous studies, as they often investigate the direct impact only and rarely

use multiple measures of greenness, may have mis-estimated health benefits of neighborhood green.

Published by Elsevier Ltd.

1. Background

With a faster-than-ever pace of daily life, urban dwellers todayface an unprecedented set of physical and social stressors, e.g.,noise, pollution, crime, work, and relationships (Frug, 1999; Bellet al., 2001). Although human beings are highly adaptable and canadjust to stressful conditions, such adjustments often createstress and threaten one’s well-being (Cohen, 1980; Baum et al.,1985). Researchers have long sought to identify possiblede-stressors for urban dwellers. Among the many plausiblesolutions, open and green spaces have been found to haverestorative and stress-relieving qualities and are recommendedas an effective de-stressor (Ulrich et al., 1991; Grahn andStigsdotter, 2003; Hartig et al., 2003).

The restorative effect of green spaces on stress is not a newconcept. The use of parks and open spaces for psychological well-being can be traced to the Villa gardens of the ancient Egypt andthe gardens of Mesopotamia (Ulrich and Parsons, 1992). Olmsted(1865) indicated that for people who feel stressed, contact withgreen spaces manages to exercise the brain without exposing it tofatigue and therefore tranquilizing it and reinvigorating the wholebody. There have also been systematic efforts to develop unifiedtheories of green spaces’ stress restoration impact. Of the notable

Ltd.

: þ1 612 625 3513.

are the stress reduction theory (SRT) (Ulrich, 1981, 1984) and theattention restoration theory (ART) (Kaplan and Kaplan, 1989;Kaplan, 1995).

SRT and ART outline different mechanisms through whichexposure to green spaces may alleviate stress (Hansmann et al.,2007). SRT emphasizes how exposure to natural elements such asoutdoor plants and water may activate affective response, beha-vioral approach orientation, and continuously relaxed attention.Such affection, orientation, and attention toward natural,unthreatening environments are said to be ‘‘evolutionary adap-tive’’ because natural elements were critical for early humans’survival and well-being, and ‘‘biologically adaptive’’ becausenatural environments encourage exploration for food or waterresources (Ulrich et al., 1991). ART, on the contrary, emphasizeshow natural environments may facilitate recovery from mentalfatigue caused by directed attention that is needed in our day-to-day tasks. Four types of environmental qualities are deemedimportant to the recovery, including being away, extent, compat-ibility, and fascination. Being away refers to settings that call onmental contents different from ordinary, worn ones, therebyallowing directed attention to rest. Extent refers to settings withsufficient content and structure (e.g., Japanese gardens) that canoccupy one’s mind with ‘‘a whole other world’’, further eliminat-ing the need for directed attention. Compatibility refers tosettings supportive of activities that fit ‘‘what one would like todo’’. Finally, fascination refers to settings capable of attractingeffortless, involuntary attention. Natural settings are suggested to

Table 1Characteristics of the study sample (N¼1544).

Variable % Mean (Std. Dev.)

Personal characteristics

Female 59

Older adults(4¼65) 8

White 20

Black 46

Hispanic 31

Married 37

Unemployed 41

Bachelor degree or higher 19

Income

o$10,000 12

$10,000–$19,999 22

$20,000–$29,999 23

$30,000–$49,999 22

$50,000–$69,999 10

4$70,000 11

Greenness

Rescaled NDVI (0–10) 3.58 (0.75)

Park acreage (within half-mile buffer) 98.33 (254.60)

Distance to nearest park in miles 0.24 (0.18)

Endogenous variables

Moderate and vigorous physical activity

(times per week)

5.18 (5.71)

Perceived social support 2.02 (0.66)

Perceived stress scale (0–16) 4.60 (3.22)

Y. Fan et al. / Health & Place 17 (2011) 1202–1211 1203

support ‘‘soft’’ fascination—a more peaceful kind of fascination—

and therefore offer deeper restorative experiences than ‘‘hard’’fascination such as watching auto racing, which may rivet one’sattention (Kaplan and Kaplan, 1989). Many studies have providedsupporting evidence that green environments contain all fourqualities and hence have great restorative and stress-reducingbenefits (Laumann et al., 2001; Purcell et al., 2001; Herzog et al.,2002; Hartig et al., 2003; Herzog et al., 2003).

However, neither SRT nor ART fully describe the potentialimpact of green spaces on stress. Exposure and access to greenspaces have positive impacts on physical activity (Ellaway et al.,2005; Giles-Corti et al., 2005; Cohen et al., 2007; Coombes et al.,2010) and social support (Kuo et al., 1998; Kuo et al., 1998;Kaplan and Kim, 2004; Maas et al., 2009), both of which arepromising de-stressors. Physical activity has been found to havepsychological benefits such as relieving symptoms of stress,reducing depression, and promoting well-being (US Departmentof Health and Human Services, 1996). More specifically, physicalactivity rids the body of some excess fighting and wakefulnesshormones, which cause problems like lack of sleep therebyreducing stress (Grahn and Stigsdotter, 2003). Exposure to day-light during physical activity decreases ‘‘stress hormones’’ such ascortisol, and increases the body’s ‘‘feel-good’’ chemicals such asendorphins (Kuller and Wetterberg, 1996; Grahn and Stigsdotter,2003). Social support has been found to mitigate stress byproviding a sense of security, enhancing self-confidence, prevent-ing the feeling of being alone, and buffering the impacts of astressful situation on an individual (Cohen and Wills, 1985).

While many studies have examined the direct impacts of greenspaces on physical activity, social support, and stress as well asthe direct impacts of physical activity and social support onstress, these impacts are often examined separately in differentstudies, which do not provide a holistic view of the cumulativeimpact of green spaces on mitigating stress. This research repre-sents a direct response to this knowledge gap. Using a communityhealth survey that includes data on physical activity, socialsupport, and stress, we employ two-stage instrumental variablesregression to investigate how neighborhood green spaces maymitigate stress directly and indirectly.

2. Data and the conceptual model

Data for this study comes from the Sinai Improving CommunityHealth Survey conducted by the Sinai Health System in Chicagofrom September 2002 to April 2003 (Whitman et al., 2004; Shahand Whitman, 2005). The survey is a cross-sectional study of six ofChicago’s 77 officially designated community areas, including WestTown, Norwood Park, North Lawndale, South Lawndale, Roseland,and Humboldt Park. The survey used a three-stage probabilitysample design; the first stage of sampling was to select fifteencensus blocks in each community using the Probability Propor-tionate to Size (PPS) sampling method. The second stage involvedusing PPS to randomly select 37 households per block from theblocks identified in the first stage.1 The third stage consisted ofrandomly selecting an individual between the ages of 18 and 75from each household. In-person interviews were conducted tocollect data from each of the sampled adults. The survey had aresponse rate of 43.2% and the final sample includes a total of 1699respondents. After excluding nine percent of the respondents whohad missing values on variables used in this research, this researchincludes a study sample of 1544 respondents.

1 In one of the six neighborhoods, Roseland, some of the selected 15 blocks

had fewer than 37 households. As a result, two additional blocks in the Roseland

neighborhood were selected, resulting in a total of 17 sampled blocks in Roseland.

Table 1 presents the social, demographic, and health-relatedcharacteristics of the study sample. A majority of the respondentsin the sample were female (59%); not married (63%); andemployed (59%). Older adults aged 65 or more accounted for 8%of the sample. The sample contains a significant number of low-income minority participants. Forty-six percent of the respon-dents were Black and 31% were Hispanic. Thirty-four percentreported an annual household income of below $20,000 whileonly 21% reported $50,000 or more.

Fig. 1 illustrates the relationships examined in this study. Wehypothesize that neighborhood greenness and personal characteris-tics all influence physical activity, social support, and stress. As such,neighborhood greenness and personal characteristics are consideredas exogenous variables—variables with no causal links (arrows)leading to them from other variables in the model (i.e., variableswhose variability is determined by variables outside of the model).

We hypothesize that physical activity, social support, and stressare endogenous variables and have recursive relationships betweeneach other (also referred as simultaneous causality: A influences Band at the same time B influences A). The simultaneous causalityamong the three variables is supported by the existing literature.Several studies in the past have found that social support boosts anindividual’s confidence and self-efficacy, which leads to higherlevels of physical activity (Rovniak et al., 2002; Anderson et al.,2006; Ayotte et al., 2010). Additional evidence supports the positiveinfluence of social support on physical activity, especially in studiesthat looked at older adults and women (O’Brien, 1995; Oka et al.,1995; Eyler et al., 1999; Wilcox et al., 2000; Sasidharan et al., 2006;Orsega-Smith et al., 2007). Studies have also shown an impact ofphysical activity on social support: by creating opportunities forsocial interaction physical activity helps promote social integration(Wankel and Berger, 1990) as well as reduces antisocial behavior inyouth (Morris et al., 2004). Giles-Corti and Donovan (2002) foundthat an increase in walking was positively associated with thenumber of persons that exercised with the respondents. It isimportant to point out that while a majority of research pointstowards a positive association between social support andphysical activity, some studies indicate that social influences in

Fig. 1. The conceptual model.

2

Y. Fan et al. / Health & Place 17 (2011) 1202–12111204

impoverished neighborhoods may deter healthy behaviors (Caughyet al., 2003).

Having social support and physical activity been widelyacknowledged as de-stressors (as discussed in the introductionsection), research has also shown that stress reduces physicalactivity participation and perceived social support. Stress evokesa number of emotional responses deterrent to the formation ofsocial ties, including social withdrawal, isolation, increased anger,frustration, irritability, edginess, and hostility (American Instituteof Stress, 2011). Additional research suggests that while stressorsmay lead to enhanced support in the short term, chronic stressorsnegatively impact social networks in the long term therebyreducing social support (Lin and Ensel, 1989; Elstad, 1998).Further, stress reduces physical activity by inducing specificphysical responses such as constant tiredness, weakness, andfatigue (American Institute of Stress, 2011). An increase in stressis found to reduce frequency, intensity, and duration of physicalactivity (Stetson et al., 1997; Heslop et al., 2001; Ng and Jeffery,2003; Lutz et al., 2007; Lutz et al., 2010).

To summarize, the discussion above suggests that the relation-ship between physical activity and social support is likely to bebidirectional and reinforcing. The relationship between physicalactivity and stress is likely to be bidirectional and counteracting,as is the relationship between social support and stress.

Due to privacy issues, the location of the surveyed household is presented

using the centroid of the household’s residence.3 The Landsat program, an Earth-observing program jointly managed by the

National Aeronautics and Space Administration (NASA) and the U.S. Geological

Survey, provides unprocessed spectral reflectance measurements acquired in the

visible (VIS) and near-infrared (NIR) regions at a 30-meter resolution (NASA,

2011). Written mathematically, the formula for calculating NDVI is

NDVI¼(NIR�VIS)/(NIRþVIS). Such calculations for a given pixel always result in

a number that ranges from �1 to 1. Higher NDVI values represent that the land

surface is covered with dense healthy vegetation.

3. Methods

3.1. Measuring neighborhood green

The first task of measuring neighborhood green is to defineneighborhood. This research employs the walkability concept by

Pushkarev and Zupan (1975). As people’s willingness to walksomewhere falls off drastically at about half a mile (Pushkarevand Zupan, 1975), we define ‘‘neighborhood’’ as the half-milebuffer area surrounding each home. The buffer area is createdbased upon the actual street network using the ArcGIS NetworkAnalyst’s service area function. When creating the buffer areaboundaries, expressways, railroads, and highways are consideredas physical barriers impeding access to greenness, as shown inFig. 2.2 The use of the buffer area concept is justified for two majorreasons: (1) it ensures substantial inter-household variation inneighborhood greenness and (2) it captures the immediate envir-onments around each home that can be easily accessed by walking.

Based upon the neighborhood concept above, we developedthree specific measures of neighborhood green, including thefollowing:

�

Normalized Difference Vegetation Index (NDVI): The mean NDVIvalue calculated within the 0.5-mile buffer area. Both unpro-cessed3 and processed NDVI datasets are available. However,in unprocessed NDVI data, clouds significantly contaminatethe measurement and lead to negative NDVI values in areaswith vegetation. More recent versions of NDVI datasets have

Fig. 2. The walkability buffer concept (a Norwood Park example).

Y. Fan et al. / Health & Place 17 (2011) 1202–1211 1205

attempted to account for this complicating factor throughprocessing. The Global Land Cover Facility Program (GLCF) atthe University of Maryland provides processed NDVI data fromthe MODerate-resolution Imaging Spectroradiometer (MODIS)on board NASA’s Terra satellite (NASA, 2007). GLCF’s NDVI datahas a resolution of 250 m and is a composite of sixteen daysworth of images. The data has the benefit of eliminating mostcloud cover found in daily images (NASA, 2007). In thisresearch, we choose to use the processed cloud-free NDVI datacollected in July 2002 to calculate the buffer-level NDVImeasure. Reasons for this decision are described in theparagraph below.

� Park acreage: The total acreage of parks that can be accessed

within a 0.5 mile walk.

�

4 NDVI typically ranges from �1 to 1. As our NDVI data is cloud-free and from

a summer month, negative NDVI values in this research either represent water or

a bright non-vegetated surface. Although the cloud-free NDVI data for the entire

Chicago area contains negative values, the NDVI data in our study area contains

only positive values. As a result, the rescaled NDVI ranges from 0 to 10 rather than

from �10 to 10.

Distance to the nearest park: The distance in miles from theresidential location to its nearest park.

Fig. 3 illustrates the difference between the unprocessedLandsat NDVI index and the processed MODIS NDVI index.Although providing NDVI information at a much finer resolution,the Landsat NDVI (range from �0.967 to 0.981) contains muchmore negative values as compared to the MODIS NDVI (rangefrom �0.25 to 0.935). Our calculations show that the buffer-levelmean NDVI measure calculated using Landsat data has a mean at�0.09, a minimum at �0.25, and a maximum at 0.13. The buffer-level mean NDVI measure calculated using MODIS data has amean at 0.36, a minimum at 0.08, and a maximum at 0.51.Although by design NDVI varies from �1 to 1, negative NDVIvalues often indicate the presence of clouds, snow, water, or abright non-vegetated surface (Yin and Williams, 1997) and do notprovide much information on vegetation levels. As our studyneighborhoods are mostly residential and our NDVI data is from asummer month, unless the data is heavily contaminated byclouds, the area should not include many negative NDVI values.Consequently, the many negative values in the Landsat-derivedNDVI measure in our study area indicate significant cloud con-tamination in the measure. It is therefore important to use NDVImeasures generated from the cloud-free MODIS data to conductthe analysis in this paper.

The multiple greenness measures used in this paper (includingNDVI, park acreage, and distance to parks) are intended to capturethe various forms of neighborhood green including parks, boule-vard trees, grassy verges, private gardens, etc. Different forms ofgreenness may play distinct roles in mitigating stress. Parks tendto provide a more structured environment for physical activityand social gathering activities (Bedimo-Rung et al., 2005; Godbeyet al., 2005). We expect park accessibility and size to be strongpredictors of physical activity and social support. Other forms ofgreenness tend to contribute to an area’s vegetation and bring asense of nature to the area. We expect NDVI to be a strongerpredictor of stress than park-related indicators.

Descriptive statistics of the greenness measures are shown inTable 1. In this analysis, we scale NDVI a factor of 10, so that a 1-unitincrease in this variable corresponds to reasonable land-use changesin urban settings (e.g., from parking lots or industrial sites with littlevegetation to school yards with moderate greenness) (Bell et al.,2008). As shown in Table 1, the average value of rescaled NDVI(measured on a scale of 0–104) in the study neighborhoods was 3.6;average park acreage accessible within half a mile was 98 acres; andaverage distance to the nearest park was 0.24 miles.

3.2. Endogenous variables and the two-stage instrumental-variables

approach

This research includes three endogenous variables: physicalactivity, social capital, and stress. We measure physical activity usingweekly combined moderate and vigorous physical activityfrequency—the total number of times during a typical week thatthe respondent was engaged in vigorous activities for at least 20 minat a time and in moderate activities for at least 30 min at a time. Thecombination approach of measuring physical activity has been

Fig. 3. Processed MODIS NDVI and the unprocessed Landsat NDVI. Note: due to data availability issues, it was difficult to obtain MODIS and Landsat data in the same time

frame. The MODIS NDVI data is from July 2002 and the Landsat NDVI data is from July 2001. Both are from summer months.

Y. Fan et al. / Health & Place 17 (2011) 1202–12111206

supported by the general public health literature as studies suggestlittle key differences in the benefits of moderate and vigorousphysical activity (Scott et al., 2009). We measure social supportusing a composite factor of three self-reported indicators: feelingalone, feeling disliked, and people being unfriendly. The compositefactor scores are derived using principal factor analysis, and higherscores indicate higher levels of social support. We measure stressusing the Perceived Stress Scale (PSS), which is a validated 4-iteminstrument measuring how unpredictable, uncontrollable, unhandle-able, or overwhelmed respondents felt their lives were during thepast month (Cohen et al., 1983). PSS has a range from 0 to 16, andhigher scores indicate more perceived stress. Descriptive statistics ofthese three endogenous variables are shown in Table 1.

Estimating regression models using endogenous variables leadsto a situation where the error term (i.e., the unobserved disturbanceterm) is correlated with regressors and where ordinary leastsquares (OLS) regression generates biased coefficients (Benda andCorwyn, 1997). In this research, we apply the two-stage instru-mental variables approach to address the problem of endogeneity.The two-stage instrumental variables approach has been widelyused in economics for over half a century and is discussed in almostevery econometrics textbook (Wooldridge, 2002; Baum, 2006;Greene, 2008). However, it was only recently that public healthresearchers began to use this approach, typically in situations whena controlled trail has not or cannot be done (Newhouse andMcClellan, 1998). Below we briefly describe this approach. For moredetailed information on this approach, interested readers may referto the econometrics textbooks mentioned above as well as pub-lished papers in the health literature that used the same approach(see Leigh and Schembri, 2004; Gravelle et al., 2008).

Assuming that the dependent variable Y is determined by a set ofendogenous variables X1 and a set of exogenous variables X2, thetwo-stage instrumental variables approach to estimate the dependentvariable Y involves two separate stages of OLS analysis (Baum, 2006):

�

The first stage in which OLS is used to predict the endogenousvariables X1 from a set of instrumental variables Z (also

referred as the excluded exogenous variables) and exogenousvariables X2 (also referred as the included exogenousvariables) and

� The second stage in which OLS is used to estimate the

dependent variable Y from the newly predicted X1 from thefirst stage and the included exogenous variables X2. (In thesecond stage, the newly predicted X1 from the first stageserves as the extracted component of X1 that is not correlatedwith the error term.)

Following this approach, the instrumental variables Z need tomeet the relevance and exogeneity criteria. That is, they need tobe correlated with the endogenous variables X1 and be uncorre-lated with the unobserved disturbance term (Baum, 2006). To bespecific, instrumental variables of physical activity have onlydirect effects on physical activity, but no direct effects on socialsupport or stress other than through physical activity. Instru-mental variables of social support have only direct effects onsocial support, but no direct effect on physical activity or stress.The same rule applies to instrumental variables of stress. Detaileddefinitions of the instrumental variables used in this research aregiven below. Table 2 shows descriptive statistics of each instru-mental variable.

Instrumental variables of physical activity include thefollowing:

�

Importance of getting exercise: The extent to which the respon-dent considered getting exercise as important. This is anordinal scale measure, including categories of not at allimportant, not too important, somewhat important, and veryimportant. � Try to lose weight: Whether or not the respondent was trying

to lose weight at the time of the survey. This is a dummyvariable with ‘‘no’’ being represented as 0 and ‘‘yes’’ beingrepresented as 1.

� Health limiting moderate activity: The degree to which the

respondent’s health limited moderate physical activity. This

Table 2Descriptive statistics of instrumental variables.

Variable % Mean (Std. Dev.)

Instruments for perceived stress

Self or household member being victim

of physical, verbal, or sexual violence

No 69

Yes 31

Threatened or harassed

Never 77

Less than once a year 11

A few times a year 8

A few times a month 2

At least once a week 1

Almost everyday 1

Instruments for social capital

Number of years living in the current house 9.52 (11.34)

Health interfered with social activities

None of the time 62

A little of the time 15

Some of the time 12

Most of the time 7

All the time 4

Instruments for physical activity

Try to lose weight

Yes 23

No 77

Health limited moderate physical activity

Not limited at all 72

Limited a little 17

Limits a lot 11

Importance of getting exercise

Very important 65

Somewhat important 27

Not too important 6

Not at all important 2

Y. Fan et al. / Health & Place 17 (2011) 1202–1211 1207

is an ordinal scale measure, including categories of not limitedat all, limited a little, and limited a lot.

Instrumental variables of social support include the following:

�

Years living in the current house: The number of years therespondent lived in their house. � Health interfering with socializing: The extent to which the

respondent’s health interfered with social activities such asvisiting friends or relatives. This is an ordinal scale measure,including categories of none of the time, a little of the time,some of the time, most of the time, and all of the time.

Instrumental variables of stress include the following:

�

Exposure to violence: Whether or not the respondent or anymember of the respondent’s household had been a victim ofphysical, verbal, or sexual violence. This is a dummy variablewith ‘‘no’’ being represented as 0 and ‘‘yes’’ beingrepresented as 1. � Threatened or harassed because of race/ethnicity: The extent to

which the respondent had been threatened or harassed in day-to-day life because of race or ethnicity issues. This is an ordinalscale measure, including categories of never, less than once ayear, a few times a year, a few times a month, at least once aweek, and almost everyday.

Based upon the instrumental variables defined above and theconceptual model in Fig. 1, the specification of the two-stage

instrumental variables regression models in this research isshown below:

�

First stage estimation:

PA¼ b0,PA_1þb1,PA_1InstrumentsPAþb2,PA_1Greenness

þb3,PA_1PersonalþePA_1

SS¼ b0,SS_1þb1,SS_1InstrumentsSSþb2,SS_1Greenness

þb3,SS_1PersonalþeSS_1

PS¼ b0,PS_1þb1,PS_1InstrumentsPSþb2,PS_1Greenness

þb3,PS_1PersonalþePS_1

�

Second stage estimation:

PA¼ b0,PA_2þb1,PA_2cSSþb2,PA_2

cPSþb3,PA_2Greenness

þb4,PA_2PersonalþePA_2

SS¼ b0,SS_2þb1,SS_2cPAþb2,PA_2

cPSþb3,SS_2Greenness

þb4,SS_2PersonalþeSS_2

PS¼ b0,PS_2þb1,PS_2cPAþb2,PS_2

cSSþb3,PS_2Greenness

þb4,PS_2PersonalþePS_2

where, PA represents the physical activity; SS represents thesocial support; PS represents the perceived stress. cPA, cSS, andcPS are the predicted values of PA, SS, and PS from the firststage estimation. InstrumentsPA, InstrumentsSS, and Instru-

mentsPS represent the instrumental variables defined for eachof the three endogenous variables. Greenness represents thethree neighborhood greenness variables. Personal representsthe individual socio-demographic variables, including gender,age, race, marital status, employment status, education, andincome levels (as shown in Table 1). In this research, we expectGreenness and Personal to influence all the three endogenousvariables.

Among the regression coefficients in the second stage estima-tion, coefficients associated with cSS, cPS, and cPA help to under-stand the recursive effects between social support, physicalactivity, and stress. These coefficients also help to understandthe indirect impacts of neighborhood green. Coefficients asso-ciated with greenness variables help to understand the directimpacts of neighborhood green on physical activity, social sup-port, and stress. Several variables were log-transformed toapproximate normal distribution. These variables include physi-cal activity, stress, and park acreage.

When using two-stage instrumental variable regression, testsof endogeneity and instrument validity are necessary to confirm(1) if the two-stage instrumental variables approach is necessary,and (2) if the selected instrumental variables meet the relevanceand exogeneity criteria. In this research, the test of endogeneityreports Wooldridge’s (1995) robust regression-based F-test. If theendogeneity test statistic is significant, then the endogenousregressors being tested must be treated as endogenous, meaningthe two-stage instrumental variables estimation is necessary. Ifthe endogeneity test statistic is not significant, then the endo-genous regressors are in fact exogenous, meaning the two-stageinstrumental variables estimation is not superior to OLS estima-tion (i.e., OLS estimation may generate more efficient results thanthe two-stage instrumental variables estimation).

Tests of instrument validity include Wooldridge’s (1995)robust score test of overidentifying restrictions and Stock andYogo (2005)’s tests of weak instruments. The test of overidentify-ing restrictions examines two conditions simultaneously. One iswhether the instrumental variables are uncorrelated with theerror term (i.e., the instrument exogeneity criterion). The other iswhether the equation is misspecified and that one or more of the

Table 3Results from preliminary two-stage instrumental variables analysis of physical

activity, social support, and stress.

Variables Log-transformedphysical activity

Socialsupport

Log-transformedperceived stress

Endogenous variablesPhysical activity

(log-transformed)

�0.156 �0.164

Social support �0.769 �1.062nnn

Perceived stress

(log-transformed)

�0.777 �0.871nnn

Neighborhood greennessNDVI (0–10 scale) �0.038 �0.039 �0.045n

nn n n

Y. Fan et al. / Health & Place 17 (2011) 1202–12111208

instrumental variables (i.e., the excluded exogenous variables)should in fact be included in the structural equation as theincluded exogenous variables. Thus, a significant statistic fromthe overidentification test represents either an invalid instrumentor an incorrectly specified structural equation. Tests of weakinstruments, which often include F and Wald tests, provideinformation on whether the selected instruments in fact havedirect effects on the endogenous variable and are strong pre-dictors of the endogenous variable (i.e., the instrument relevancecriterion). Stock and Yogo (2005) suggested that if tests of weakinstruments cannot reject the null hypothesis, coefficients fromthe two-stage instrumental variables estimation may suffer fromsevere size distortion.

Park acreage (log-

transformed)

0.039 0.019 0.022

Distance to the

nearest park

(mile)

0.225 0.054 0.057

Personal characteristicsFemale �0.013 0.052 0.059

Old (age 4¼65) �0.406nnn�0.192nn

�0.219nn

Hispanic �0.541nnn�0.120 �0.128

Black �0.275nn�0.068 �0.089

White �0.129 –0.051 �0.067

Bachelor degree or

higher

0.060 0.060 0.074

Household income 0.090nnn 0.044nn 0.042nn

Unemployed 0.075 0.088nn 0.103nn

Married �0.168nn�0.021 �0.031

Number of children 0.042nn 0.013 0.014

Constant 4.263nn 3.517nnn 3.876nnn

Summary statisticsNumber of

observations

1544 1544 1544

R-square Suppresseda Suppresseda Suppresseda

Wald chi-square 100.41 143.63 274.78

Prob4chi2 0.000 0.000 0.000

Root MSE 1.002 0.740 0.814

Tests of endogeneity and instrument validityTest of endogeneity

(F)

1.804 21.771nnn 71.311nnn

Test of

overidentification

(chi2)

13.534nnn 4.916 3.523

Tests of weak

instruments

F statistic N/Ab N/Ab N/Ab

Minimum

eigenvalue

statistic

1.096 16.702 18.646

5% Wald test

critical value

16.870 19.450 19.450

n po .1.nn po .05.nnn po .01.a Stata’s ivregress command suppresses the printing of an R2 on two-stage

instrumental variables (IV) estimations if R2 is negative, which is to say, if the model

sum of squares is negative. A negative R2 in a two-stage IV model is legitimate

because the two-stage IV model’s residuals are computed over a set of regressors

different from those used to fit the model. This means a constant-only model of the

dependent variable is not nested within the two stage IV model, even though the

two-stage model estimates an intercept, and the residual sum of squares (RSS) is no

longer constrained to be smaller than the total sum of squares (TSS). When RSS

exceeds TSS, MSS and R2 will be negative (STATA Corp LP, 2010).b The F-statistic for the F test of weak instruments cannot be provided because

each of the models contains two endogenous regressors. The F test of weak

instruments can only be applied to two-stage IV models with a single endogenous

regressor.

4. Regression results

Table 3 presents modeling results from the preliminary two-stage instrumental variables analysis. Although all the prelimin-ary models passed the Wald chi-square test, none of the modelspassed the test of weak instruments (as shown in Table 3, theminimum eigenvalue statistics in all the models are lower thanthe 5% Wald test critical value). In addition, the physical activitymodel failed passing the test of endogeneity (F¼1.804, p4 .10)and showed a significant test statistic from the overidentificationtest (chi2¼13.534, po .01). The test results suggest that thephysical activity model may be misspecified and that stress andsocial support may not be considered as endogenous whenestimating physical activity. If that is the case, it is important toisolate physical activity out of the endogenous system and re-estimate the models.

Table 4 shows results from the second-round models in whichphysical activity is estimated using OLS regression and socialsupport and stress are estimated using two-stage instrumentalvariables regression. Note that, the social support and stressmodels in Table 4 are different from those in Table 3 becausephysical activity is no longer included as an endogenous regressorin either model. The tests of endogeneity and instrumentalvalidity suggest that the two-stage instrumental variablesapproach is appropriate for estimating social support and stressand the instruments used in the estimation are all valid andrelevant. Therefore, the second-round models in Table 4 serve asour final models and are discussed below.

Among the three greenness measures, NDVI is negativelyassociated with both social support (b¼�0.047, po .10) andstress (b¼�0.044, po .10); park acreage is positively associatedwith both physical activity (b¼0.024, po .10) and social support(b¼0.020, po .10); and distance to the nearest park shows noassociation with physical activity, social support, or stress. Thereis a recursive relationship between social support and stress:social support has a negative impact on stress (b¼�1.015,po .01) and stress has also a negative impact on social support(b¼�1.094, po .01). To make these key findings easier to inter-pret and compare, Fig. 4 illustrates the empirically tested rela-tionships using elasticities calculated at median values.

As shown in Fig. 4, when holding other variables constant attheir median values, a 1% increase in park acreage would yield a0.024% increase in physical activity and a 0.01% increase in socialsupport. The impact of NDVI on social support and stress shows amore complicated pattern because the impact involves bothdirect and indirect connections (as shown in Fig. 4). NDVI has adirect and negative impact on social support, yet it has an indirectand positive impact on social support by negatively influencingstress. The cumulative impact of NDVI on social support isestimated at �0.005 (¼�0.088þ(�0.159)(�0.523)). This sug-gests that NDVI overall has a minimal impact on social support: A

1% increase in NDVI is associated with a 0.005% decrease in socialsupport. Similarly, NDVI has a direct and negative impact onstress, yet it has an indirect and positive impact on stress throughnegatively influencing social support. The cumulative impact ofNDVI on stress is estimated at 0.077 (¼–0.159þ(�0.088)

Table 4Results from final models of physical activity, social support, and stress.

VariablesLog-transformedphysical activity

Socialsupport

Log-transformedperceived stress

Endogenous variablesPhysical activity

(log-transformed)

Social support �1.094nnn

Perceived stress

(log-transformed)

�1.015nnn

Neighborhood greennessNDVI (0–10 scale) �0.001 �0.047n

�0.044n

Park acreage (log-

transformed)

0.024n 0.020n 0.017

Distance to the

nearest park

(mile)

0.196 0.025 0.024

Personal characteristicsFemale �0.065 0.069 0.070

Old (age 4¼65) �0.252nnn�0.200nn

�0.174nn

Hispanic �0.499nnn�0.060 �0.045

Black �0.225nn�0.067 �0.047

White �0.080 �0.065 �0.050

Bachelor degree or

higher

0.014 0.055 0.074

Household income 0.071nnn 0.025n 0.033nn

Unemployed �0.010 0.114nn 0.103nn

Married �0.152nnn�0.015 �0.002

Number of children 0.036nnn 0.007 0.009

Constant 1.522nnn 3.566nnn 3.677nnn

Summary statisticsNumber of

observations

1544 1544 1544

R-square 0.084 Suppresseda Suppresseda

Wald chi-square 13.97nnnb 119.94nnn 242.78nnn

Root MSE 0.818 0.792 0.823

Tests of endogeneity and instrument validityTest of endogeneity

(F)

N/Ac 32.661nnn 132.836nnn

Test of

overidentification

(chi2)

N/Ac 2.290 1.062

Tests of weak

instruments

F statistic N/Ac 42.660nnn 51.414nnn

Minimum

eigenvalue

statistic

N/Ac 30.577 101.693

5% Wald test

critical value

N/Ac 19.930 19.930

n po .1.nn po .05.nnn po .01.a Stata’s ivregress command suppresses the printing of an R2 on two-stage

instrumental variables (IV) estimations if R2 is negative, which is to say, if the

model sum of squares is negative. A negative R2 in a two-stage IV model is

legitimate because the two-stage IV model’s residuals are computed over a set of

regressors different from those used to fit the model. This means a constant-only

model of the dependent variable is not nested within the two stage IV model, even

though the two-stage model estimates an intercept, and the residual sum of

squares (RSS) is no longer constrained to be smaller than the total sum of squares

(TSS). When RSS exceeds TSS, MSS and R2 will be negative (STATA Corp LP,

2010).b The 13.97 value presented in the physical activity model column is not a chi-

square test statistic. It is an F test statistic because the physical activity model is

estimated using OLS regression, not two-stage IV regression.c These tests are not applicable to the physical activity model because it is

estimated using OLS regression, not two-stage IV regression.

Y. Fan et al. / Health & Place 17 (2011) 1202–1211 1209

(�2.681)). A 1% increase in NDVI is associated with a 0.077%increase in stress.

Table 5 summarizes the direct, indirect, and cumulativeimpacts of greenness variables on physical activity, social support,

and stress. The overall positive effect of NDVI on stress isunexpected at the first glance because this is in contrary to thewidespread belief that vegetation and greenness in generalmitigate stress. However, a closer look at the direct and indirecteffects of NDVI on stress shows that NDVI does have a direct andnegative impact on stress. A 1% increase in NDVI is directlyassociated with a 0.159% decrease in stress. This direct stressmitigation impact of NDVI is counteracted by the negative impactof NDVI on social support because, with the endogeneity betweenstress and social support, the negative impact on social supporteventually translates into positive impact on stress.

5. Conclusions and discussion

This study indicates that different components of neighbor-hood green play distinct roles in influencing physical activity,social support, and stress. Park spaces are found to directlypromote physical activity, and indirectly mitigate stress via thespaces’ positive impact on social support. Overall neighborhoodvegetation is found to have direct stress mitigation impact, yetthe impact is counteracted by the negative effect of overallvegetation on social support.

The findings above are partially in-line with the past literature.Like previous studies, both neighborhood green and social sup-port are found to mitigate stress. Neighborhood green is found toencourage both social support and physical activity. However,besides consistent findings with past literature, this study alsopoints to new findings—some of which contradict conventionalknowledge about physical activity, social support, and stress.First, no significant association is found in this study betweenphysical activity and stress, which contradicts previous research(Kuller and Lindsten, 1992; Kuller and Wetterberg, 1996; USDepartment of Health and Human Services, 1996; Grahn andStigsdotter, 2003). Second, we fail to find any association betweenphysical activity and social support, which contradicts previousresearch (Anderson et al., 2006; Orsega-Smith et al., 2007; Ayotteet al., 2010). Third, this study suggests no evidence that distanceto the nearest park influences physical activity, social support, orstress. This is possibly explained by the universally high parkaccessibility across Chicago neighborhoods. Chicago has morethan 7000 acres of parkland distributed over 500 parks accessibleto over 90% of its residents within half a mile of their homes(Harnik and Simms, 2004). In our sample (according to Table 1),respondents on average are 0.24 mile away from public parks,with a standard deviation of 0.18 mile. As high levels of parkaccessibility are universal in our study neighborhoods, our studysample may not contain sufficient variation in the distancevariable to validate (or to disvalidate) the factor’s influences onphysical activity, social support, or stress.

The findings that NDVI and park acreage play distinct roles ininfluencing health indicate the importance of distinguishingbetween the different components of neighborhood green andthe importance of including multiple measures of neighborhoodgreen when investigating its effects. Neighborhood park acreageis found to be directly connected to physical activity and socialsupport, but not stress. Neighborhood vegetation level is found tobe directly connected to social support and stress, but notphysical activity. When comparing the effect size, park acreageshows much more positive impact on health and well-being thanthe overall neighborhood vegetation level. These findings haveimportant policy implications: structured green spaces withpublic recreation and socialization opportunities provide greaterhealth benefits to nearby residents than other greenness settings(e.g., greenness in backyards). Simple conservation of greenspaces may be inadequate for achieving health benefits. Policy

Legend: * p<.1; ** p<.05; *** p<.01

NDVI (0-10)

Perceived Stress

Physical Activity

Social Support

Park Acreage

0.024* -0.159* 0.010*

-2.681***

-0.088*

-0.523***

Fig. 4. Evidence-based path model with estimated elasticities. Legend: *po .1; **po .05; *** po .01.

Table 5Direct, indirect, and cumulative effects of greenness on physical activity, social

support, and stress.

Physicalactivity

Socialsupport

Perceivedstress

Park

acreage

Direct effect 0.024 0.010

Indirect effect �0.027

Total 0.024 0.010 �0.027

NDVI Direct effect �0.088 �0.159

Indirect effect 0.083 0.236

Total �0.005 0.077

Note: Numbers in the table are elasticities calculated at median values.

Y. Fan et al. / Health & Place 17 (2011) 1202–12111210

makers who are interested in promoting community health needto place stronger emphasis on providing publicly accessible,multifunctional green spaces to community residents than simplegreen space conservation.

This research further contributes to the literature on healthbenefits of urban parks by providing a more accurate quantifica-tion of the impact, incorporating both direct and indirect effects.Nationally, the rising population and new urban planning strate-gies have led to higher densities in cities but the park system hasoften not grown adequately to support additional residents(Sherer, 2003). In many cities, green spaces tend to be viewedas optional amenities rather than necessary components of urbaninfrastructure (Fan and Khattak, 2009). With 92% of U.S. citiesfacing budget shortfalls (Hoene, 2009), cities are likely to beprevented from buying new parkland or maintaining existingparkland. These circumstances highlight the importance andtime-sensitivity of efforts to quantify health impacts of parks.Without fully recognizing the value of these spaces, developmentdecisions in the U.S. and in most parts of the world have beenlargely market-driven and often lead to a shortage of urban parks.By illustrating the direct and indirect impacts of parks on physicalactivity, social support, and stress, the findings and discussion inthis paper may stimulate research on health impacts of parks andhelp to solidify public support of park investments.

Acknowledgement

Data utilized in this paper was graciously provided by the SinaiUrban Health Institute. We thank Steve Whitman for providing usfull access to the data.

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