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published online 5 January 2012Urban StudChristopher Zegras, Jae Seung Lee and Eran Ben-Joseph

Baby Boomers' Local Travel Behaviour in Suburban Boston, USBy Community or Design? Age-restricted Neighbourhoods, Physical Design and

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By Community or Design? Age-restrictedNeighbourhoods, Physical Design and BabyBoomers’ Local Travel Behaviour inSuburban Boston, US

Christopher Zegras, Jae Seung Lee and Eran Ben-Joseph

[Paper first received, October 2009; in final form, August 2011]

Abstract

This article analyses the travel behaviour, residential choices and related preferencesof 55+ baby boomers in suburban Boston, USA, looking specifically at age-restrictedneighbourhoods. For this highly auto-dependent group, do neighbourhood-relatedcharacteristics influence local-level recreational walk/bike and social activity trip-making? The analysis aims to discern community (for example, social network)versus physical (for example, street network) influences. Structural equation models,incorporating attitudes and residential choice, are used to control for self-selectionand to account for direct and indirect effects among exogenous and endogenousvariables. The analysis reveals modest neighbourhood effects. Living in age-restricted, as opposed to unrestricted, suburban neighbourhoods modestly increasesthe likelihood of residents being active (i.e. making at least one local recreationalwalk/bike trip) and the number of local social trips. Overall, the age-restricted com-munity status has greater influence on recreational and social activity trip-makingthan the neighbourhood physical characteristics, although some community–neighbourhood interaction exists.

1. Introduction

Globally, the growing numbers of olderadults, combined with changes in metropol-itan settlement patterns, lifestyles and atti-tudes, have important implications forurban futures (for example, Champion,

2001). In many industrialised countries,‘baby boomers’, the generation born duringthe period of sustained high birth rates fol-lowing World War II, are now associatedwith distinctive approaches to consumption,

Christopher Zegras, Jae Seung Lee and Eran Ben-Joseph are in the Department of Urban Studiesand Planning, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. E-mail:[email protected], [email protected] and [email protected].

1–30, 2012

0042-0980 Print/1360-063X Online� 2012 Urban Studies Journal Limited

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politics, personal finance, work and retire-ment, health and leisure (for example,Phillipson et al., 2008). Many of the indus-trialised world’s baby boomers came of ageduring a period of mass motorisation and sub-urbanisation and their travel behaviour hasemerged as an important issue. Particularly inthe US, the majority of older adults live in thesuburbs and the great majority of their trips(87 per cent in 2009) are by car (US DOT,2009). These lifestyle preferences, and ageingitself, pose challenges related to promotingactive lifestyles and healthy ageing and reduc-ing transport environmental and safety con-cerns (Rosenbloom, 2003). Examining babyboomers’ residential preferences and travelbehaviours can help, at least, to inform neigh-bourhood design, transport policy and mobi-lity service provision for the older adultcohort.

The range of innovations in products,markets and services increasingly gearedtowards an older adult society (Coughlin,2007) includes residential living alternatives,such as age-restricted neighbourhoods. TheUS’ first age-restricted neighbourhood,Youngtown, Arizona, was built in 1954 on a320-acre cattle ranch outside Phoenix andwas designed as a socially active, affordableand child-free setting (Blechman, 2008). Inthe US as of 2005, 43 per cent (29.6 million)of owner-occupied housing had at least onemember aged 55+ ; of these, approximately3.3 per cent (1 million) were in age-restricted neighbourhoods; age-restrictedhousing accounted for 11 per cent of newhomes bought by persons 55+ in 2003/04(Emrath and Liu, 2007). To a lesser extent,similar developments are appearing in otherWestern nations (Grant and Mittelsteadt,2004; Kennedy and Coates, 2008).

Our study explores the relationshipbetween age-restricted neighbourhoods andbaby boomers’ local travel habits. Ostensiblydesigned for older adult lifestyle prefe-rences, age-restricted neighbourhoods

might influence physical and/or social activ-ity among residents, leading to healthier life-styles. We examine this possibility, focusingon recreational walk/bike and local socialtrip-making among ‘leading-edge’ babyboomers (age 55–64 during data collection in2008): comparing age-restricted neighbour-hoods in suburban Boston with nearby non-age-restricted neighbourhoods; and assessingthe effects of neighbourhoods’ physical char-acteristics. That is, we test two sources ofbehavioural effects: those arising from social(and other unobserved) characteristics ofage-restricted neighbourhoods and thoseresulting from particular physical attributes.Although the setting is a metropolitan area inthe US, insights may apply elsewhere.

2. Background and ResearchQuestions

2.1 Key Concepts and Questions

In the US, baby boomers are generallyrecognised as those born between 1946 and1964—78.2 million persons (25 per cent ofthe nation) in 2005. We focus on ‘leading-edge’ baby boomers, now approachingretirement age and currently qualifying forage-restricted (55+ ) housing residency.This cohort is the ‘‘key demographic tar-geted by developers and marketers of activeadult housing’’ in the US (Heudorfer, 2005,p. 22). Here, ‘baby boomers’ refers to thisleading-edge cohort while ‘older adults’refers more generally to those 55+ .

The two basic categories of older adultneighbourhoods are: planned develop-ments, which include continuing careretirement communities offering on-sitenursing/care facilities and leisure-orientedretirement communities, typically orientedaround recreation (for example, golfcourses); and unplanned communities—i.e.‘naturally occurring retirement commu-nities’ (NORCs) that organically evolve

2 CHRISTOPHER ZEGRAS ET AL.



into neighbourhoods with the majority ofresidents aged 55+ (Hunt and Gunter-Hunt, 1985).

We examine a particular type of plannedolder adult neighbourhood, the age-restricted, active adult neighbourhood,1

and behavioural differences its residentsmay display relative to residents in unrest-ricted neighbourhoods. From this point:‘neighbourhood’ means a ‘‘geographicallybounded unit in which residents share prox-imity and the circumstances that come withit’’ (Chaskin, 1995, p. l); and, ‘community’means the broader network of interpersonalrelationships providing ‘‘sociability, sup-port, information, a sense of belonging, andsocial identity’’ (Wellman, 2005, p. 53). Acommunity might coincide with a neigh-bourhood; age-restricted neighbourhoodsaim, partly, to create community. Weexclude assisted living and congregated carefacilities to control for the potentially differ-ent travel capabilities of individuals withassisted-living needs and, subsequently, thepossible influences of neighbourhooddesigns specific to such residents. Thus,‘age-restricted’ refers to age-restricted,active adult neighbourhoods and ‘unrest-ricted’ refers to neighbourhoods withoutexplicit age restrictions. We use the age-restricted status as a proxy for community.

Finally, we study two types of individuals’local activities

—local recreational walking and bicycleuse—hereafter, ‘recreational non-motorised transport’ (NMT), becauseincreasing physical activity helpshealthy ageing and local NMT can sat-isfy recommendations for older adults’regular moderate physical activity (forexample, DiPietro, 2001; Eyler et al.,2003); and

—local social engagement—hereafter,‘social trips’—since being socially ‘dis-engaged’ may lessen physical andmental health and residential

neighbourhoods can maintain andincrease social networks via proximityand shared physical settings, enhan-cing residents’ well-being (for exam-ple, Kweon et al., 1998; Yang andStark, 2010).

Separating these trip types adds impor-tant nuance to the analysis, as neighbour-hoods and communities might vary in theirimpacts on different travel behaviours andindividuals may choose particular settings tosatisfy certain behavioural preferences; thesettings, in turn, may then influence otherbehaviours. In this paper, being ‘social’ andmaking social trips refer to individual char-acteristics and activities; ‘community’ refersto the broader network of interpersonal rela-tionships, as already defined and as we dis-tinguish based on the restricted/unrestrictedneighbourhood of residence.

2.2 Research Precedents

Scholars and others have long been inter-ested in older adults’ travel behaviour (forexample, Wachs, 1979). Relevant recentresearch includes transport’s contributionto older adults’ well-being in Vancouver(Cvitkovich and Wister, 2001), trip genera-tion rates and travel distances in London(Schmocker et al., 2005) and satisfactionwith travel opportunities in Sweden(Wretstrand et al., 2009). Studies have onlymore recently focused specifically on therelationship between the built environmentand older adults’ travel behaviour.

Due to our research focus, we limit theliterature review to studies of: ‘objective’measures of the built environment andrelationship with NMT use; and, effects ofneighbourhood and community character-istics on older adults’ walking behaviourand social engagement.

The built environment and walking.Research consistently reveals associations

BY COMMUNITY OR DESIGN? 3



between utilitarian walking and factors likeproximity to destinations and public tran-sit, street connectivity, mixed land use andhigher residential and job density (forexample, Baran et al., 2008; Giles-Corti andDonovan, 2002; Giles-Corti et al., 2005;Huston et al., 2003; Lee and Moudon,2006; Moudon et al., 2005; Saelens andSallis, 2003). On the other hand, researchfocused on walking for recreation and exer-cise provides inconsistent results (Owenet al., 2004). Some studies show that side-walks (Giles-Corti and Donovan, 2002),accessible destinations (Giles-Corti et al.,2005), hilliness (Lee and Moudon, 2006)and perception of attractiveness and safety(Alfonzo et al., 2008; Giles-Corti andDonovan, 2002) are associated with a higherlevel of recreational walking; other studiesfail to reveal such correlations (Rodriguezet al., 2006; Saelens and Sallis, 2003).

Older adults’ NMT use. King et al.(2003), examining older women (averageage 74) in suburban and urbanPennsylvania, find a positive correlationbetween physical activities (pedometer-measured) and convenient destinations andperceived walkability. Berke et al. (2007a)find neighbourhood walkability in KingCounty, Washington—measured via a spa-tial buffer of households and accountingfor characteristics like dwelling unit densityand proximity of grocery stores—to beinversely associated with depressive symp-toms in older (65+ ) men (but notwomen). Berke et al. (2007b) also find astatistically significant relationship betweenthe same walkability measure and fre-quency of older persons’ (65+ ) walking forphysical activity. Examining older people’s(65+ ) travel behaviour in northernCalifornia and controlling for attitudes,Cao et al. (2010) find that several neigh-bourhood characteristics (for example,

safety, distances) influence walk trip fre-quencies. Joseph and Zimring (2007)examine older adults’ (age 77–83) pathchoice in three continuing care retirementneighbourhoods in Atlanta, finding anassociation between: well-connected,destination-oriented paths and utilitarianwalking; and longer, well-connected pathswithout steps and recreational walking.Finally, using multilevel regression, Nagelet al. (2008) find that high-volume streetsand proximity to destinations positivelyinfluence total walking time among olderadults (average age 74) in Portland(Oregon), while low-volume streets have anegative influence on total walking time.They find no association between the builtenvironment and the odds of not walking,suggesting no neighbourhood influence onsedentary older adults’ walking behaviour.

Neighbourhood, community and olderadults’ social activities and/or well-being. Early US studies took a building-level perspective, often focusing ongovernment-supported housing (Lawtonet al., 1975). In Portland (Oregon),Chapman and Beaudet (1983) found olderadults’ (average age 78) interactions withneighbours to be highest in ‘good quality’neighbourhoods, more distant from thecity centre, and with low shares of olderpeople. Kweon et al. (1998) found a posi-tive association between time spent incommon outdoor green spaces and mea-sures of social integration and ‘sense oflocal community’ among poor 64+ adults(average age 68) in Chicago’s age-integrated public housing. Finally, Yangand Stark (2010), using qualitative meth-ods, find apparent behavioural influencesof social features related to expectations ofencounters and homogeneity of residentsin assisted living facilities (stand-alonebuildings).




2.3 Age-restricted Neighbourhoods andTravel Behaviour: Hypotheses

Little research has focused specifically onlocal travel behaviour in age-restrictedneighbourhoods. As mentioned, Joseph andZimring (2007) examined walk path choicein continuing care retirement neighbour-hoods. Flynn and Boenau (2007) estimatedvehicular traffic counts for a suburbanVirginia age-restricted neighbourhood, find-ing trip rates comparable to those recom-mended for detached senior adult housingby the Institute of Transportation Engineers.

Avoiding the broader debate about age-restricted neighbourhoods (see Blechman,2008), we identify features that may influ-ence local NMT use and social engagement.Specifically, relative to unrestricted neigh-bourhoods, age-restricted neighbourhoodsmay differ by (Hebbert, 2008)

—demographics: people of similar agesand interests, combined with physicaldisconnection from surroundingneighbourhoods, may decrease thelikelihood of encountering strangersin day-to-day activities;

—community: programmes, events, club-houses may increase residents’ activitylevels;

—suitability: targeting the 55+ demo-graphic and offering lifestyle choiceamenities (like golf courses, pools)may support more active living; and

—walkability: trails and sidewalks, andlittle, if any, through-traffic mayincrease walking.

Age-restricted and unrestricted neigh-bourhoods also share many similarities. Thegreat majority (71 per cent) of age-restrictedneighbourhoods in the US are suburban,even more suburban than overall locationsof older adult households (Emrath and Liu,2007).2 This implies limited connectivity toother neighbourhoods, limited local retail,

dispersed employment and other services,and limited public transport. In this subur-ban context, we focus locally, where physicaland community differences and, thus,potential behavioural effects may arise.

Do age-restricted neighbourhoods influ-ence local travel behaviour? The socialecological model offers a theoretical framefor local travel behaviour in age-restrictedneighbourhoods, emphasising the recipro-cal interactions between behavioural andenvironmental factors. Presuming thatchanges in community alter individualbehaviours, the model focuses on relation-ships between environmental interventionsand interpersonal, organisational and othercommunity factors (Sallis and Owen,1996). Age-restricted neighbourhoods maysupport older adults through peer groups,social programmes and higher perceivedsafety, among other things (for example,Ahrentzen, 2010). We hypothesise that,after controlling for physical characteristics,age-restricted neighbourhoods have morerecreational NMT and social trips due tocommunity effects.

Do neighbourhood characteristics influ-ence local travel behaviour? As Maatet al. (2005) propose, a neighbourhood’sphysical characteristics may influence travelbehaviour via effects on net utility—theutility of travel (for example, number,quality, distribution of destinations) less itsdisutility (actual and perceived travelcosts). Consider, for example, prototypicalstreet configurations: linear, loop and grid(see Figure 1). The latter two reduce non-duplicative routes (reducing travel’s disuti-lity) and, by clustering dwellings, increaseopportunities to meet neighbours (increas-ing travel’s utility). Thus, we hypothesisethat grid- and loop-type neighbourhoodspromote more recreational NMT and




social trips, as do higher intersection den-sity, neighbourhood facilities (for example,parks, golf course) and proximity to otherdestinations, including public transportstops.

2.4 Analytical Challenges and SpecificModelling Precedents

Aiming to show whether neighbourhoods’community and physical characteristicsproduce different activity patterns, poses theclassic causality challenge, associated with‘self-selection’ (Mokhtarian and Cao, 2008).At least two related forms of bias may bepresent: simultaneity bias (for example,individuals who prefer walking choosing tolive in walkable neighbourhoods); andomitted variable bias (unobserved variables,like preferences for walking, produce thetravel outcome (walking), but also correlatewith neighbourhood characteristics).3 Inother words, the presumed exogenouscausal variable, the neighbourhood, is actu-ally endogenous, which can produce incon-sistent and biased estimators. Mokhtarianand Cao (2008) review the issues and possi-ble analytical and research design solutions.Cao et al. (2009) review 38 empiricalstudies using nine different approaches tocontrol for ‘self-selection’—direct ques-tioning, statistical control, instrumentalvariables, sample selection models, propen-sity score matching, other joint models ofresidential and travel choices (for example,structural equation models) and longitudi-nal studies.

Only statistical control and structuralequation models are reviewed here.Statistical control directly incorporates atti-tudes and preferences into the behaviouralmodel, thereby isolating these effects fromneighbourhood-level effects. Studies typi-cally use specialised survey data, includingattitudes and preferences (for example,measured on a Likert scale), in a two-step

approach: factor analysis on the indicators(since multiple preferences/attitudes aremeasured); and, behavioural modelling,including fitted values from the first step(for example, Cao et al., 2006, 2010).Problematically, the estimation of thesecond step is inconsistent because thefitted latent variables (from the first step)include measurement error by droppingerror terms (Ben-Akiva et al., 2002).

The latter problem can be addressed withstructural equation modelling (SEM), ananalytical tool introduced in the travel beha-viour field in the 1980s (Golob, 2003) andmore recently applied to the self-selectionissue (Cao et al., 2009). A full SEM usessimultaneously estimated measurementmodels, for endogenous and exogenousvariables, and a structural model, and cancapture influences of exogenous on endo-genous variables and among endogenousvariables (Golob, 2003). SEM measurementmodels are similar to exploratory factor ana-lytical approaches, except in restricting theparameters defining factors and specifyingcovariances among unexplained portions ofboth unobserved and latent variables(Golob, 2003). The estimated parametersmake the predicted variance–covariancematrix as similar as possible to the observedvariance–covariance matrix, subject tomodel constraints. SEM can distinguishbetween direct and total effects and, withsimultaneous measurement equations oflatent variables, allows consistent incorpora-tion of attitudes and preferences in beha-vioural models and captures potentialbi-directional influences between attitudesand travel behaviour (Mokhtarian and Cao,2008).

Few studies have used SEM to introducelatent attitudinal variables in the built envi-ronment/travel behaviour context. Abreuet al. (2006) used SEM in analysing adultworkers’ travel in Lisbon, treating short-and longer-term travel behaviours and




Figure 1. Three categories of neighbourhood street patterns, descriptive diagrams and proto-typical examples of the categorisation.Source: World Imagery, provided by ESRI (http://www.arcis.com/home/item.html?id=10df2279f9684e4a9f6a7f08febac2a9).




residence and workplace land use character-istics (latent variables identified throughexploratory factor analysis) as endogenousvariables and individual socioeconomicvariables as exogenous. The approach par-tially accounts for self-selection while notexplicitly including attitudinal effects; thestructural and measurement models are notestimated simultaneously. Bagley andMokhtarian (2002) included attitudes in aSEM, including endogenous variables (tworesidential type variables, one job locationvariable, three travel demand variables andthree attitude variables) and exogenous vari-ables (socio-demographics, lifestyle factors,attitude measures). They found that atti-tudes and lifestyles exerted the greatestinfluence on travel behaviour, while residen-tial location type had little impact. Thestudy represented neighbourhood charac-teristics via factor scores on two dimensions(traditional versus suburban) and includedlatent variables as fitted values of factoranalysis on indicators, rather than simulta-neously estimating structural and measure-ment equations. Similar to Abreu et al.(2006), their model is path analysis ratherthan complete SEM.

In summary, for the highly automobile-dependent, yet relatively understudied, babyboomer generation in the suburban US, weask the question: do neighbourhood-relatedcharacteristics influence local-level recrea-tional walk/bike and social activity trip-making? Drawing from social ecologicaltheory and utility-based travel behaviourtheory, our analysis aims to discern com-munity (for example, social network) versusphysical (for example, street network) influ-ences. Unlike most previous research in thisfield, we use full structural equation models,incorporating attitudes and residentialchoice, to control for self-selection and toaccount for direct and indirect effectsamong exogenous and endogenousvariables.

3. Research Context and Design

Greater Boston includes 164 cities andtowns, with 4.45 million persons (in 2000),across 2832 square miles (6107 square km).Just over 20 per cent of residents are olderadult (US Census Bureau, 2002), a cohortexpected to increase by 50 per cent between2000 and 2020 (Heudorfer, 2005).Approximately 8.5 per cent of GreaterBoston residents in 2000 were ‘leading-edge’ boomers (US Census Bureau, 2002),a group slightly more suburban than theoverall population.4

These demographic trends, and local landuse policies and fiscal considerations, havefuelled age-restricted development. State-wide, Heudorfer (2005) found 150 age-restricted neighbourhood developmentscompleted or under construction in 93 citiesand towns, implying a supply of more than10 000 housing units, with another 170 age-restricted developments in pre-constructionor seeking permissions in 109 towns. Mostdevelopments have fewer than 100 dwellingunits and include walking paths, meetingrooms and clubhouses, with fewer providingon-site shops, bike trails and golf facilities(Heudorfer, 2005).

3.1 Survey Design and Data

We use a quasi-experimental, cross-sectionalresearch design comparing suburban age-restricted and unrestricted neighbourhoodsin Greater Boston. The age-restricted neigh-bourhoods were first identified—via realestate listings, information from developersand other resources5—based on the follow-ing criteria: built out and occupied; entirelyor mainly age-restricted; and ‘active adult’(for example, not a continuing care facility).Thirty-five age-restricted neighbourhoodsmet the initial criteria. From this list, 20neighbourhoods were selected (see Table 1),by filtering out recent developments (to




ensure potential residency of at least threeyears) and small developments (less than 30units on a single street). The final sampledage-restricted neighbourhoods range in sizefrom 40 to 1100 dwelling units with a meanof 160 and median of 66 units. Our modelscontrol for the possible influence of neigh-bourhood size by including total streetlength in each age-restricted and unrestrictedneighbourhood. Overall, the selected neigh-bourhoods are biased towards more recentdevelopments and/or ones with recent realestate activity.

Each age-restricted neighbourhood wasmatched with unrestricted surroundingsusing postal codes to approximate similarregional accessibility and demographics.Mailing addresses were requested fromUSAData, a commercial data vendor, forresidents aged 55–65, generating 34 108names. We identified 1237 households inage-restricted neighbourhoods by matchingstreet names against the purchased list. Wethen randomly sampled 5763 households

from unrestricted areas, producing a totalsample size of 7000 households. We purpo-sely oversampled unrestricted areas, expect-ing to receive a lower response rate fromthe cohort of interest there. Our samplingapproach is endogenously stratified.

Mailed survey packages included a $5non-contingent cash incentive, a travelsurvey for retrospective trip counts over thepast week; attitudinal questions, such as pre-ferences for walking and cycling (five-pointLikert scale); and household/individualquestions (for example, income, employ-ment status). We received 1650 householdresponses, 1422 after excluding problematicresponses (effective response rate of 20 percent): 349 from age-restricted neighbour-hoods (28 per cent response rate) and 1073from unrestricted neighbourhoods (19 percent response rate). Households included1859 individuals (470 age-restricted; 1389unrestricted).6 Among the 20 age-restrictedneighbourhoods, responses came from 15(Table 1).

Table 1. Age-restricted neighbourhoods examined (15 from which we received responses: 28per cent response rate)

ID Community Households Persons Map

1 Adams Farm 14 218 Deerfield Estate 7 109 Delapond Village 2 211 Eagle Ridge 11 1517 Leisurewoods 25 3120 Oak Point 95 12821 Pinehills 87 11623 Red Mill 6 825 Southport 35 4527 Spyglass Landing 5 630 The Village at Meadwood 16 2231 The Village at Orchard Meadow 17 2332 Village at Quail Run 11 1533 Vickery Hills 14 2335 Wellington Crossing 4 5

Total 349 470

Notes: The shading on the map indicates towns with one or more ARAACs, as tabulated byHeudorfer (2005). Numbered dots indicate locations of ARAACs identified for this study.




Neighbourhood characteristics weremeasured using a geographical informationsystem (GIS) and public and private datasources, based on household location (iden-tifiable to the centroid of a 250-metre gridcell; see the example in Figure 1).7

3.2 Measures and Descriptive Statistics

Table 2 includes descriptive statistics of keyvariables, including outcomes of interest,reported weekly: NMT trips, representingrecreational walking/biking trips; and socialtrips, measuring visits to neighbours andrepresenting local social engagement.Respondents in age-restricted neighbour-hoods have only slightly higher averageweekly trip rates for both trip purposes. Alarge share of individuals in both neigh-bourhood types report making zero NMTand social trips during the week (hereafter,these individuals are ‘non-active’ and ‘non-social’). Unrestricted neighbourhoods havea 10 per cent higher share of non-activeand a 13 per cent higher share of non-social individuals. Baby boomers residingin age-restricted neighbourhoods tend tobe less employed, slightly healthier andslightly older, with fewer owning a bike ormore than three cars.

Age-restricted neighbourhoods havemore local facilities, such as public spaces,and, primarily, loop street patterns. Nonehas grid streets. Nearly 50 per cent of unrest-ricted neighbourhoods have linear streetpatterns. Other physical characteristics—such as intersection density, destinationsand proximity to public transport—do notsignificantly differ between sampledrestricted/unrestricted neighbourhoods.

Exploratory factor analysis on theresponses to the questions regarding resi-dential preferences led us to hypothesisetwo latent variables: Pro Walkability, denot-ing preference for walkable neighbour-hoods, and Pro Segregation, representing

preference for neighbourhoods segregatedby age and social class. Confirmatory factoranalysis confirms this latent structure:fixing the indicators most highly correlatedwith the two latent variables at 1 for identi-fication, all other indicators significantlycontribute to the latent variables. Thislatent construct serves as a measurementmodel in the following SEM.8

4. Behavioural Modelling

The large share of zero-reported NMT andsocial trips (Table 2) indicates censoring—ordinary count models may be inappropri-ate. We employ a zero-inflated model,allowing zeros to remain in the count modelby estimating an individual’s likelihood ofbeing in the ‘zero’ group. Taking recrea-tional NMT trips as an example, a binarylogit model estimates the probability ofbeing non-active and active. These probabil-ities weight the zeros in the count modelsuch that the probability of observing zerofor an individual equals the probability ofbeing non-active plus the probability ofbeing active, multiplied by the probability ofobserving zero in the count model (Jones,2005) (Figure 2, Equations (1)–(3)). Thisproduces two sets of coefficients. The logitmodel results indicate the variables’ influ-ence on the likelihood of being non-active;negative coefficients imply a higher prob-ability of being active. The count model esti-mates trip counts for the active group;positive coefficients mean a higher fre-quency of recreational NMT trips.

We apply zero-inflated negative binomial(ZINB) models9 with SEM that simultane-ously incorporates attitudes possibly affect-ing residential choice/travel behaviour anda residential choice model. Three types ofrelationship are examined—residentialchoice, residential preference and travelbehaviour (Figure 2)—and three models




Tab

le2.

Des

crip

tive

stat

isti

csby

nei

ghbourh

ood

type

and

test

sofdif

fere

nce

s

Var

iabl

esT

otal

Gro

up

mea

n(S

.D.)

NM

ean

(S.D

.)A

ge-r

estr

icte

dU

nre

stri

cted

Mea

nd

iffe

ren

ce

Dep

end

ent

vari

able

sN

MT

trip

Las

tw

eek,

ho

wm

any

tim

esd

idyo

uw

alk

or

cycl

efo

rex

erci

sein

you

rn

eigh

bo

urh

oo

d?

1761

2.23

5(2

.417

)2.

629

(2.4

51)

2.10

1(2

.391

)0.

528**

Ind

ivid

ual

sre

po

rtin

gze

roN

MT

trip

so

ver

pas

tw

eek

(i.e

.n

on

-act

ive)

704

0.40

00.

324

0.42

60.

102**

Soci

altr

ipL

ast

wee

k,h

ow

man

yti

mes

did

you

visi

tyo

ur

nei

ghb

ou

rs?

1755

0.80

1(1

.322

)1.

084

(1.4

72)

0.70

6(1

.253

)0.

378**

Ind

ivid

ual

sre

po

rtin

gze

roso

cial

trip

so

ver

pas

tw

eek

(i.e

.n

on

-so

cial

)10

750.

613

0.51

40.

646

0.13

2**

Qu

esti

onpr

edic

tor

(tre

atm

ent)

AR

AA

CA

ge-r

estr

icti

on

stat

us

(0.n

ot

rest

rict

ed;1

.age

-res

tric

ted

)18

590.

253

——

—

Soci

o-ec

onom

icch

arac

teri

stic

sE

mpl

oyE

mp

loym

ent

stat

us

(0.

un

emp

loye

d;

1.em

plo

yed

)18

460.

637

0.51

00.

680

0.17

0**

Hea

lth

yH

ealt

hst

atu

s(0

.u

nh

ealt

hy;

1.h

ealt

hy)

1859

0.85

10.

892

0.83

70.

054*

Mal

eG

end

er(0

.fe

mal

e;1.

mal

e)18

490.

472

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are estimated (the Appendix provides fullresults). Model 1 has no control for self-selection. Model 2 attempts to control for

individuals’ self-selection for neighbour-hood physical characteristics by simultane-ously estimating the ZINB model and the

Figure 2. Path diagrams and equations of three models that hypothesise relationships amongthe built environment, residential preference and travel behaviour.




latent variable (attitudinal) model’s struc-tural and measurement equations. Model 2estimates residential preferences conditionalupon socioeconomic characteristics; thus,travel behaviour and residential preferencesare endogenous while socioeconomic statusand neighbourhood physical characteristicsand age-restricted status are exogenous.Model 3 includes a binary choice model forage-restricted status, assuming that peopleselect age-restricted neighbourhoods to sat-isfy community preferences, and that neigh-bourhood and individual characteristicsinfluence age-restricted choice.10 In thiscase, age-restricted neighbourhood choice,residential preferences and individual andneighbourhood characteristics jointly affectlocal travel behaviour. We estimate themodels in Mplus 5.0, using a normal theorymaximum likelihood (ML) estimator andaccounting for non-independence amongobservations from the same household11

(Muthen, 1998–2004; Muthen and Muthen,1998–2007).

4.1 Recreational NMT

The recreational NMT trip results directlysupport our first hypothesis regarding theeffect of age-restricted setting, with somesupport for neighbourhood physical charac-teristics, but only ‘bundled’ with age-restric-tion. Figure 3 orients the discussion.

Examine, first, the age-restricted neigh-bourhood choice: after controlling forneighbourhood characteristics, age-restrictedneighbourhoods attract older, higher-income people who prefer segregated neigh-bourhoods. Males are less likely to chooseage-restriction. Age-restricted neighbour-hoods with loop-type streets, higher inter-section density and on-site facilities aremore attractive.

Looking at the likelihood of being non-active, neighbourhood physical characteristics—loop street type, intersection density,

presence of local facilities, total streetlength—do have an influence, but onlyindirectly, via the age-restricted choice.Community and design primarily influencethe non-active likelihood, with only nearbydestinations exerting a significant effect onnumber of trips among the active. Nearbycommuter rail, interestingly, negatively cor-relates with number of recreational NMTtrips.

For individuals, being employed increasesthe likelihood of being non-active anddecreases the number of NMT trips amongthe active. Being healthy decreases the likeli-hood of being non-active. Finally, ‘pro-Walkables’ are less likely to be non-active,while ‘pro-Segregated’ are more likely to be.This latter effect is partly offset by the pro-Segregated choosing age-restricted neigh-bourhoods that, in turn, increase the likeli-hood of being active.

There is little evidence of self-selectionfor local NMT trips. While both latent atti-tudinal constructs significantly affect thechoice to be active, they do not change thesign, significance or magnitude of the age-restricted effect. Age-restricted communitysettings increase the chance that residentswill make local recreational NMT trips;perhaps, in part, due to neighbourhoodphysical characteristics. Other than nearbydestinations’ effect on NMT trip counts,neighbourhood physical characteristics donot directly affect baby boomers’ beingactive or the number of recreational NMTtrips.

4.2 Social Trips

The social trip results also only partially sup-port our hypotheses, with distinct, some-what counter-intuitive, differences relativeto recreational NMT. Age-restricted settingsand, indirectly, their bundled physical char-acteristics exert an uncertain influence onthe number of social trips. Physical




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characteristics themselves only modestly(and uncertainly) influence the likelihood ofbeing ‘social’. Again, Figure 3 guides thediscussion.

The same age-restricted choice model asfor recreational NMT holds. However, con-trary to the NMT case, age-restricted neigh-bourhoods do not affect being social; amongthe social, age-restriction increases socialtrip-making. This result should be viewedwith some uncertainty (p-value = 0.075)and suggests residential self-selection vis-a-vis social trip-making (compare the signif-icance of the age-restricted neighbourhoodcoefficient from model 1 with models 2 and3; Appendix, Table A2). Those inclined tomake more social trips may select age-restricted settings (and, possibly, theirphysical characteristics) to satisfy socialtrip-making tendencies. Regarding directphysical effects, street typologies are insig-nificant. Nearby commuter rail is associatedwith being social and making more localsocial trips (p\0.10).

For individuals, being employedincreases the likelihood of being non-social.Unsurprisingly, being employed reducesweekly local social trip-making. Olderboomers have greater likelihood of makingmore social trips. Finally, those preferringsegregated neighbourhoods have a higherlikelihood of making more social trips.

Social trips offer stronger evidence of self-selection in this study. While age-restrictedneighbourhoods appear to be associatedwith more weekly social trips among thesocially inclined, statistical support for thiseffect declines once accounting for attitudesand residential preferences.

5. Implications and Shortcomings

Our findings must be viewed in light of thedemographic geography of older adults inthe metropolitan US: the majority live in

auto-dependent suburbia. Among oursampled individuals, for example, 93 percent of daily reported trips were by auto-mobile (Hebbert, 2008), even higher thanthe automobile mode share for GreaterBoston’s baby boomers.12 This study shedslittle light on the larger challenges implied.Nonetheless, with respect to two types oflocal travel activities that may be influencedby suburban neighbourhood and communitycharacteristics and play an important role inhealthy ageing, some influences emerge.

We find modest effects of neighbourhoodage-restricted status and physical characteris-tics on weekly recreational NMT and socialtrip-making. Distinguishing between thosewho do and do not make a recreationalNMT or social trip provides useful informa-tion. Eyler et al. (2003), studying adults inthe US, identified three types of walker: regu-lar, occasional and never. Occasional andnever walkers lacked time for walking andnever walkers reported feeling unhealthier,while regular walkers reported more self-confidence and social support for walking.Our recreational NMT results support thesefindings and suggest a design and commu-nity (social network) role: those with a ‘pro-Walkable’ mindset are more likely to beactive; the community and, indirectly, designaspects of age-restricted neighbourhoodsincrease residents’ likelihood of being active,after controlling for self-selection. This pro-vides some support for the social ecologicalmodel of health promotion—the social-physical setting of the age-restricted neigh-bourhoods apparently provides a mediumfor active living (for example, Wister, 2005).Among the active, however, the neighbour-hood has no effect on increased recreationalNMT trip-making, although nearby destina-tions do play a role. The age-restricted effectmay come from social settings (i.e. commu-nity) or other unobserved (or non-compara-ble) physical characteristics distinguishingage-restricted from unrestricted suburbs. For




example, the age-restricted neighbourhoodsstudied have more local facilities (for exam-ple, clubhouses) than typical suburbs (Table2); while insignificant in the NMT models,these variables’ effects may be masked by theage-restricted label.

As in the recreational NMT case, someage-restricted physical characteristics (inter-section density, neighbourhood facilities anddestinations) indirectly influence social trip-making among the social. In this case, how-ever, residents may be purposefully choosingage-restricted settings and their related designattributes: age-restricted settings will not‘make’ people social, but may attract thosewith higher social trip-making tendencies.

Our findings indicate the importance ofdistinguishing between trip types, includingwhen attempting to control for self-selection. The results confirm intuition: anindividual may choose a neighbourhood tosatisfy desired local social activity; this resi-dential choice to satisfy one activity prefer-ence might then induce changes in otheractivities.

5.1 Limitations and Future Research

Our results are only directly applicable to aspecific demographic, geography and time ofyear (i.e. April 2008) and may not be generali-sable. Even for the specific groups and areasstudied, it is likely that the sampling proce-dure suffers from biases that further limit theresults’ validity and generalisability.

The age-restricted effects may be con-founded by our not knowing whether someof the unrestricted neighbourhoods alsohave a high share of older adults (i.e. beingNORCs), implying similar communitystructures. This relates to spatialdependence—participation in a particularactivity may be influenced by surroundingneighbourhoods, including how well ‘inte-grated’ the neighbourhoods are with their

surroundings, only crudely proxied here.The age-restricted neighbourhoods’ relativenewness may also confound; newer resi-dents13 may still be ‘exploring’ surround-ings, effects indistinguishable from theage-restricted status. Over time, such effectsmay diminish or intensify—an area for fur-ther study.

Analytically, complete SEM—simultaneously estimating measurementmodels of latent attitudinal variables andbehavioural (structural) models—represents an important advance. It con-trols for self-selection based on attitudesand residential choice and allows testingmore complex relationships, includingdirect and indirect effects. The increasedmodelling sophistication also comes at acost—our particular SEM cannot easilyreveal relative or marginal effects, only sig-nificance and directionality. Furthermore,the design remains cross-sectional, asopposed to temporal (i.e. measuringchange). For example, people living in asociable community and/or a social-oriented neighbourhood may increase, overtime, their socialising, which may thenchange the community (for example, walk-ing groups); revealing these dynamicswould require longitudinal analysis.

Questions can be raised about the out-comes measured: self-reported recreationalNMT trips in the neighbourhood and socialtrips to ‘neighbors’. Respondents may inter-pret the extent of ‘neighbourhood’ and/or‘neighbours’ differently. Further, the mea-sures may be weak proxies for outcomesmore closely related to healthy ageing, suchas: minutes of activity per day, health condi-tions, levels of social engagement, strengthof social networks and/or mental healthconditions. Analogously, the validity andreliability of the attitudes/preferences ques-tions are uncertain and treating the ordinalLikert-value attitude scores as continuous




variables (in the factor analysis), althoughcommon practice, may be problematic.

Regarding neighbourhood built environ-ment, we attempted ‘objective’ measure-ment, which may not account for designqualities like sense of safety and human scale(Ewing and Handy, 2009) and may ignoreindividual perceptions of relevant factors.Again, these perceptions may change overtime and be influenced by neighbourhoodand/or community changes. Enhancedbehavioural insights might come from com-bining qualitative measures of the built envi-ronment with ‘objective’ measures.

Further research could examine addi-tional travel behaviours among babyboomers and/or compare suburban andurban baby boomers or age-restrictedneighbourhoods with non-age-restrictedmaster planned neighbourhoods. Suchcomparisons may reveal whether themodest behavioural effects of age-restrictedneighbourhoods derive from the communitystructure, physical features or their recipro-cal interactions. Additional topics worthexamining include: the potential to retrofitexisting neighbourhoods to serve the needsof older adults more effectively; whetherspatial concentrations of older adults in sub-urbia increase possibilities for new transportand/or other older adult services; and therelationship between commuter rail proxim-ity and local trip-making.

Our results indicate the need to reach abetter understanding of how physical andsocial structures interact to influence olderadults’ activities. Overall, however, therelative locations of older-adult-orientedneighbourhoods need attention. For exam-ple, just 13 per cent of the age-restrictedneighbourhoods studied are within 1 kmof a bus stop and 20 per cent within 1 kmof commuter rail. As ageing meansreduced driving capabilities, this relativeautomobile-dependency may pose aproblem.

6. Conclusion

We studied a neighbourhood type cateringfor older adults—age-restricted, activeadult neighbourhoods—and attempted todiscern community (for example, socialnetwork) versus physical (for example,street network) influences on suburbanbaby boomers’ travel behaviour. Usingstructural equation models, the analysisattempts to control for self-selection basedon attitudes and residential choice, allow-ing for direct and indirect effects amongexogenous and endogenous variables.

The age-restricted neighbourhoodsattract older, higher-income baby boomerswho prefer age-segregation. These commu-nities increase the likelihood of boomersbeing active—i.e. making at least one localrecreational NMT trip—but not thenumber of NMT trips among the active.Physical characteristics have only an indi-rect effect, by influencing the decision tolive in age-restricted settings. In contrast,age-restriction has no effect on being social(i.e. the likelihood of ever visiting neigh-bours); among the social, however, age-restriction increases social trip-making,although perhaps due to self-selection. Inother words, age-restricted neighbourhoodsare associated with higher levels of localsocial activity, but because they attractmore socially inclined residents. The age-restricted effect may stem from a sense ofcommunity fostered in age-restrictedneighbourhoods and/or unobserved orintermingling physical characteristics.

Our analysis indicates the importanceof distinguishing between trip types whencontrolling for self-selection in the builtenvironment/travel behaviour research. Italso suffers from a range of limitations,including generalisability, unknown rela-tive magnitude of effects and inability toassess impacts over time. While thisresearch offers some insight into the




influence of age-restricted neighbourhoodson baby boomers’ local travel behaviours,it says nothing about the regional travelpatterns of this highly suburbanised,automobile-dependent generation.

Notes

1. In the US, the Department of Housing andUrban Development (HUD) uses seniorhousing, or 55 and older community;residential developer Del Webb refers to‘active adult communities’ (HarrisInteractive, 2005); the National Association ofHomebuilders suggests that ‘age-qualified’ ispreferred (Emrath and Liu, 2007).

2. Overall older adult (and age-restricted)household locations in the US: 23 per cent(14 per cent) central cities; 50 per cent (71per cent) suburbs; and 27 per cent (15 percent) outside metro areas (derived fromEmrath and Liu, 2007, Tables 1 and 2).

3. A sample selection problem may also exist:among the possible sub-samples of babyboomers, factors influencing residentiallocation choice for our age-restrictedsub-sample could also influence behaviour. Inour case, this problem effectively appears as aform of omitted variable bias (Hebbert, 2008).

4. Based on share of census population in2000, accumulated over the correspondingcensus block centroid’s distance fromBoston’s central business district.

5. Heudorfer (2005) inventoried (apparentlybased on a survey of town officials)age-restricted housing in the state, but didnot identify individual developments.

6. Problematic responses included: addressesnon-geo-locatable or outside the studyarea (due to mail forwarding); nohousehold survey page; age outside thecohort of interest. Hebbert (2008) providesdetail on survey design, implementationand results.

7. The grid cell approach ensures anonymity,making it impossible to identify thehousehold’s address. The centroid of the250-metre grid cell serves as the household‘location’. Each grid centroid was visually

associated to a neighbourhood based onprimary street characteristics (see Figure 1).Basic data, including roads, parcels,commuter rail, come from MassGIS (http://www.mass.gov/mgis/), although availabledata were limited. For example, no buildingfootprints for sample neighbourhoods couldbe located and road networks in the newerage-restricted neighbourhoods wereoutdated. We updated missing data using ahigh resolution aerial photo from ESRI(http://www.arcgis.com/home/item.html?id=a5fef63517cd4a099b437e55713d3d54) toclassify street patterns and computeintersection density. For other neighbourhoodcharacteristics (for example, public spaces,outdoor sports facilities), we used GoogleEarth’s satellite imagery and ‘Places’ layer,and, in some cases, site plans andage-restricted neighbourhoods’ webpages.

8. Space constraints preclude including theconfirmatory factor analysis (CFA). Referto the measurement models in Tables A1and A2; full results available upon request.

9. For the NMT and social trip models, theVuong test indicates that ZINB is preferableto a regular negative binomial; and alikelihood ratio test indicates that ZINB ispreferable to a zero-inflated Poisson.

10. As mentioned in the sample description,our sample is endogenously stratified. Inthe age-restricted neighbourhood choice(logit) models, this choice-based samplingresults in an inconsistent alternative specificconstant, while other coefficients areconsistent (Manski and Lerman, 1977). Weattempt to correct for this samplingstrategy by using weights in the choicemodel estimation: p/s for households inage-restricted communities and (1-p)/(1-s) for households from unrestrictedcommunities, where p is the probability ofa household living in an age-restrictedcommunity from the population and s isthe proportion of households from oursample living in an age-restrictedcommunity. As a value for p is notavailable, we use 3.2 per cent (Emrath andLiu, 2007). We test the sensitivity of results




to this value by estimating the models withp = 1 per cent and 6 per cent (reasonableupper and lower bounds); the results donot vary substantively. Full sensitivityanalyses are available upon request.

11. We sampled households but modelindividuals, thus need to account forpotential correlation of behaviour amongsame-household individuals (i.e. intraclasscorrelation). One option MPlus providesfor dealing with this issue is correctingstandard errors (SEs); using this approach,the SEs in Tables 3 and 4 are ‘corrected’.Our approach may also have intraclasscorrelation among households from thesame neighbourhood, indicating the needfor a multilevel SEM ZINB model. We leavethis approach for future research.

12. The differences may partially result fromundercounting in our survey. The55–65-year-old cohort at the time of themost recent Boston metropolitan areatravel survey (in 1991) had an automobilemode share of 89 per cent (CTPS, 1993);the most recent national travel survey,although with only 194 baby boomers fromthe Boston MA, indicates a 78 per centautomobile mode share for all trips by thiscohort (US DOT, 2009).

13. Residents of age-restricted neighbourhoodsreport having lived there on average for 5years, compared with 19 years forunrestricted neighbourhoods (Hebbert,2008).

Funding Statement

This work was supported by the New EnglandUniversity Transportation Center, under grantDTRS99-G-0001.

Acknowledgements

The authors wish to thank: Frank Hebbert, whoplayed a major role in survey design, implemen-tation and analysis; Joe Coughlin and MosheBen-Akiva, for numerous useful conversations;Kristin Simonson, for assisting with the spatialanalysis; Lee Carpenter, for editorial assistance;and several anonymous referees for detailed andinsightful comments and suggestions.

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Tab

leA

1.

Rec

reat

ional

NM

Ttr

ips

zero

-inflat

edneg

ativ

ebin

om

ial(Z

INB

)m

odel

resu

lts

(N=

1456)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

oice

mod

el

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Log

itm

odel

(zer

o-in

flat

ion

)es

tim

atin

g‘l

ikel

ihoo

dof

bein

gin

non

-act

ive

grou

p’A

ge-r

estr

icte

d2

0.51

9*(0

.244

)2

0.60

2*(0

.249

)2

0.61

0*(0

.250

)P

roW

alka

bili

ty2

0.21

2*(0

.098

)2

0.21

3*(0

.097

)P

roSe

greg

atio

n0.

210*

(0.0

95)

0.20

8*(0

.094

)G

rid

20.

172

(0.1

82)

20.

188

(0.1

81)

20.

188

(0.1

81)

Loo

p0.

059

(0.1

79)

0.05

5(0

.181

)0.

056

(0.1

81)

Inte

rsec

tion

den

sity

20.

296

(0.4

22)

20.

211

(0.4

13)

20.

210

(0.4

13)

Fac

ilit

ies

20.

027

(0.1

65)

20.

010

(0.1

66)

20.

009

(0.1

66)

Des

tin

atio

n40

00.

017

(0.1

60)

0.06

0(0

.161

)0.

060

(0.1

61)

MB

TA

bus

stop

20.

166

(0.2

83)

20.

092

(0.2

84)

20.

093

(0.2

84)

Com

mu

ter

rail

20.

053

(0.1

80)

20.

041

(0.1

84)

20.

041

(0.1

84)

Stre

etle

ngt

h0.

004

(0.0

09)

0.00

2(0

.009

)0.

002

(0.0

09)

Em

ploy

0.57

8**

(0.1

66)

0.60

6**

(0.1

67)

0.60

6**

(0.1

67)

Hea

lth

y2

0.74

3**

(0.1

96)

20.

788**

(0.1

96)

20.

788**

(0.1

96)

Mal

e0.

027

(0.1

34)

20.

048

(0.1

37)

20.

048

(0.1

37)

Age

0.01

1(0

.020

)0.

012

(0.0

20)

0.01

2(0

.020

)H

igh

inco

me

20.

129

(0.2

05)

20.

102

(0.2

07)

20.

102

(0.2

07)

Mid

inco

me

0.05

3(0

.190

)0.

067

(0.1

90)

0.06

6(0

.190

)T

hre

eve

hic

les

20.

096

(0.1

64)

20.

165

(0.1

67)

20.

165

(0.1

67)

Bik

e2

0.44

9**

(0.1

46)

20.

430**

(0.1

48)

20.

430**

(0.1

48)

Con

stan

t2

0.46

6(1

.295

)2

0.50

9(1

.311

)2

0.51

1(1

.311

)

Neg

ativ

ebi

nom

ial

mod

eles

tim

atin

g‘n

um

ber

ofN

MT

trip

sam

ong

acti

vegr

oup’

Age

-res

tric

ted

0.05

3(0

.087

)0.

078

(0.0

87)

0.08

0(0

.088

)P

roW

alka

bili

ty2

0.03

2(0

.038

)2

0.03

1(0

.038

)P

roSe

greg

atio

n2

0.03

7(0

.036

)2

0.03

7(0

.036

)G

rid

0.00

1(0

.069

)0.

007

(0.0

69)

0.00

7(0

.069

)

(con

tin

ued

)

Appen

dix




Tab

leA

1.(C

onti

nued

)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

oice

mod

el

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Loo

p2

0.03

4(0

.068

)2

0.02

9(0

.068

)2

0.02

9(0

.068

)In

ters

ecti

ond

ensi

ty2

0.05

0(0

.115

)2

0.05

2(0

.113

)2

0.05

2(0

.113

)F

acil

itie

s0.

000

(0.0

66)

0.01

0(0

.066

)0.

010

(0.0

66)

Des

tin

atio

n40

00.

128

(0.0

60)

0.13

9*(0

.060

)0.

139*

(0.0

60)

MB

TA

Bu

sst

op0.

101

(0.1

08)

0.09

9(0

.105

)0.

099

(0.1

05)

Com

mu

ter

rail

20.

163*

(0.0

68)

20.

177*

(0.0

68)

20.

177*

(0.0

68)

Stre

etle

ngt

h0.

001

(0.0

03)

0.00

0(0

.003

)0.

000

(0.0

03)

Em

ploy

20.

227**

(0.0

55)

20.

234**

(0.0

56)

20.

234**

(0.0

56)

Hea

lth

y0.

065

(0.0

78)

0.06

3(0

.078

)0.

063

(0.0

78)

Mal

e2

0.02

0(0

.049

)2

0.02

2(0

.049

)2

0.02

2(0

.049

)A

ge2

0.00

4(0

.007

)2

0.00

3(0

.007

)2

0.00

3(0

.007

)H

igh

inco

me

20.

015

(0.0

74)

20.

014

(0.0

74)

20.

014

(0.0

74)

Mid

inco

me

20.

035

(0.0

71)

20.

041

(0.0

71)

20.

041

(0.0

71)

Th

ree

veh

icle

s2

0.03

8(0

.063

)2

0.04

6(0

.062

)2

0.04

6(0

.062

)B

ike

0.03

4(0

.055

)0.

030

(0.0

55)

0.03

0(0

.055

)C

onst

ant

1.56

9**

(0.4

22)

1.55

2**

(0.4

22)

1.55

2**

(0.4

22)

Alp

ha

0.08

9**

(0.0

24)

0.08

8**

(0.0

24)

0.08

8**

(0.0

24)

Mea

sure

men

tm

odel

esti

mat

ing

Pro

Wal

kabi

lity

I 1:

Ip

refe

rto

hav

esh

op

san

dse

rvic

esw

ith

inw

alki

ng

dis

tan

ce1.

000

(0.0

00)

1.00

0(0

.000

)I 2

:I

do

no

tva

lue

spac

ear

ou

nd

my

ho

use

mo

reth

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op

sn

earb

y0.

889**

(0.0

94)

0.88

8**

(0.0

94)

I 3:

Ili

kea

nei

ghb

ou

rho

od

con

tain

ing

ho

usi

ng,

sho

ps

and

serv

ices

0.92

7**

(0.0

46)

0.92

6**

(0.0

46)

I 4:

Id

on

ot

pre

fer

alo

to

fsp

ace

bet

wee

nm

yh

om

ean

dth

est

reet

0.51

5**

(0.0

80)

0.51

5**

(0.0

80)

I 5:

Ip

refe

ra

ho

use

clo

seto

the

sid

ewal

kso

that

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nse

ep

asse

rsb

y0.

621**

(0.0

50)

0.62

0**

(0.0

50)

(con

tin

ued

)




Tab

leA

1.(C

onti

nued

)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

oice

mod

el

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Pro

Segr

egat

ion

I 6:

Ip

refe

rn

eigh

bo

urs

atth

esa

me

stag

eo

fli

feas

me

1.00

0(0

.000

)1.

000

(0.0

00)

I 7:

Ip

refe

rli

vin

gar

ou

nd

peo

ple

wh

oar

esi

mil

arto

me

0.58

9**

(0.0

82)

0.58

6**

(0.0

81)

I 8:

Iam

con

cern

edab

ou

tst

ran

gers

wal

kin

gth

rou

ghm

yn

eigh

bo

urh

oo

d0.

462**

(0.0

87)

0.45

7**

(0.0

85)

I 9:

Ili

keto

live

ina

nei

ghb

ou

rho

od

wit

ho

ut

chil

dre

nin

it0.

354**

(0.0

61)

0.35

3**

(0.0

60)

Stru

ctu

ral

(MIM

IC)

mod

eles

tim

atin

gP

roW

alka

bili

tyE

mpl

oy2

0.01

0(0

.067

)2

0.01

1(0

.068

)H

ealt

hy

20.

024

(0.0

83)

20.

024

(0.0

84)

Mal

e2

0.22

6**

(0.0

53)

20.

227**

(0.0

53)

Age

0.00

4(0

.008

)0.

003

(0.0

08)

Hig

hin

com

e0.

044

(0.0

91)

0.04

4(0

.091

)M

idin

com

e2

0.09

4(0

.080

)2

0.09

4(0

.080

)T

hre

eve

hic

les

20.

301**

(0.0

76)

20.

301**

(0.0

76)

Bik

e2

0.04

6(0

.068

)2

0.04

6(0

.068

)

Pro

Segr

egat

ion

Em

ploy

20.

167*

(0.0

65)

20.

168*

(0.0

65)

Hea

lth

y0.

124

(0.0

81)

0.12

5(0

.080

)M

ale

0.12

6*(0

.049

)0.

126*

(0.0

49)

Age

20.

001

(0.0

08)

20.

001

(0.0

08)

Hig

hin

com

e2

0.03

0(0

.091

)2

0.03

1(0

.091

)M

idin

com

e2

0.07

7(0

.079

)2

0.07

7(0

.079

)T

hre

eve

hic

les

0.02

9(0

.070

)0.

029

(0.0

70)

Bik

e2

0.12

8*(0

.065

)2

0.12

8*(0

.065

)

Log

itm

odel

esti

mat

ing

‘lik

elih

ood

ofch

oosi

ng

age-

rest

rict

edn

eigh

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rhoo

d’

Pro

Wal

kabi

lity

0.14

0(0

.179

)P

roSe

greg

atio

n0.

810**

(0.1

47)

(con

tin

ued

)




Tab

leA

1.(C

onti

nued

)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

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mod

el

Coe

ffic

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t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

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ien

t(S

.E.)

Loo

p5.

979**

(0.7

08)

Inte

rsec

tion

Den

sity

2.20

6**

(0.3

84)

Fac

ilit

ies

3.39

3**

(0.3

41)

Des

tin

atio

n40

02

0.11

6(0

.299

)M

BT

Abu

sst

op2

2.64

7**

(0.9

40)

Com

mu

ter

rail

0.19

9(0

.433

)St

reet

len

gth

0.10

6**

(0.0

14)

Em

ploy

20.

259

(0.2

70)

Hea

lth

y0.

530

(0.3

40)

Mal

e2

0.44

2*(0

.192

)A

ge0.

205**

(0.0

37)

Hig

hin

com

e1.

302**

(0.3

97)

Mid

inco

me

0.47

0(0

.329

)T

hre

eve

hic

les

20.

268

(0.4

37)

Bik

e2

0.32

3(0

.267

)

Nu

mb

ero

fp

aram

eter

s37

8410

2N

um

ber

of

ob

serv

edva

riab

les

1827

27Id

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fica

tio

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veri

den

tifi

ed84

\0.

5*27*

(27+

1)O

veri

den

tifi

ed10

2\

0.5*

27*

(27+

1)

Not

es:

yp

\0.

10;*

p\

0.05

;**

p\

0.01

.




Tab

leA

2.

Soci

altr

ips

zero

-infl

ated

neg

ativ

ebin

om

ial(Z

INB

)m

odel

resu

lts

(N=

1410)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

oice

mod

el

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Log

itm

odel

(zer

o-in

flat

ion

)es

tim

atin

g‘l

ikel

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dof

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gin

non

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ive

grou

p’A

ge-r

estr

icte

d0.

030

(0.5

12)

20.

102

(0.6

11)

20.

111

(0.6

21)

Pro

Wal

kabi

lity

20.

240

(0.2

97)

20.

238

(0.2

97)

Pro

Segr

egat

ion

0.19

2(0

.283

)0.

194

(0.2

81)

Gri

d0.

543

(0.5

87)

0.40

0(0

.528

)0.

399

(0.5

26)

Loo

p0.

147

(0.5

47)

0.08

7(0

.453

)0.

089

(0.4

53)

Inte

rsec

tion

den

sity

21.

379

(1.3

37)

21.

175

(1.0

62)

21.

175

(1.0

62)

Fac

ilit

ies

20.

486

(0.5

18)

20.

360

(0.4

30)

20.

360

(0.4

30)

Des

tin

atio

n40

02

0.39

5(0

.447

)2

0.21

7(0

.452

)2

0.21

7(0

.451

)M

BT

Abu

sst

op2

1.40

4(2

.151

)2

1.06

6(2

.118

)2

1.06

4(2

.109

)C

omm

ute

rra

il1.

200y

(0.6

19)

1.10

8y(0

.600

)1.

110y

(0.6

03)

Stre

etle

ngt

h2

0.00

8(0

.017

)2

0.01

3(0

.016

)2

0.01

3(0

.016

)E

mpl

oy0.

525

(0.3

95)

0.56

0(0

.360

)0.

560

(0.3

60)

Hea

lth

y0.

370

(0.5

73)

0.37

4(0

.595

)0.

374

(0.5

95)

Mal

e0.

819*

(0.3

70)

0.74

8y(0

.421

)0.

749y

(0.4

22)

Age

0.05

1(0

.048

)0.

040

(0.0

45)

0.04

0(0

.045

)H

igh

inco

me

0.40

0(0

.756

)0.

382

(0.6

82)

0.38

4(0

.683

)M

idin

com

e0.

452

(0.6

00)

0.45

8(0

.602

)0.

460

(0.6

05)

Th

ree

veh

icle

s2

0.36

0(0

.649

)2

0.40

2(0

.675

)2

0.40

4(0

.677

)B

ike

0.07

4(0

.321

)0.

048

(0.3

11)

0.04

9(0

.312

)C

onst

ant

24.

486

(3.5

44)

23.

778

(3.5

13)

23.

776

(3.5

19)

Neg

ativ

ebi

nom

ial

mod

eles

tim

atin

g‘n

um

ber

ofso

cial

trip

sam

ong

soci

algr

oup’

Age

-res

tric

ted

0.57

6**

(0.2

12)

0.47

9*(0

.239

)0.

471y

(0.2

42)

Pro

Wal

kabi

lity

20.

018

(0.1

28)

20.

017

(0.1

28)

Pro

Segr

egat

ion

0.15

6(0

.111

)0.

156

(0.1

10)

Gri

d0.

115

(0.1

96)

0.04

6(0

.202

)0.

046

(0.2

01)

(con

tin

ued

)




Tab

leA

2.(C

onti

nued

)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

oice

mod

el

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Loo

p2

0.15

7(0

.200

)2

0.18

2(0

.184

)2

0.18

0(0

.184

)In

ters

ecti

ond

ensi

ty2

0.26

4(0

.303

)2

0.24

4(0

.273

)2

0.24

2(0

.273

)F

acil

itie

s2

0.18

8(0

.186

)2

0.16

6(0

.173

)2

0.16

5(0

.173

)D

esti

nat

ion

400

0.14

2(0

.173

)0.

196

(0.1

95)

0.19

6(0

.195

)M

BT

Abu

sst

op2

0.65

9(0

.481

)2

0.60

3(0

.595

)2

0.60

3(0

.593

)C

omm

ute

rra

il0.

342*

(0.1

67)

0.35

2y(0

.182

)0.

353y

(0.1

82)

Stre

etle

ngt

h2

0.00

8(0

.007

)2

0.00

9(0

.006

)2

0.00

9(0

.006

)E

mpl

oy2

0.36

8*(0

.168

)2

0.32

7*(0

.151

)2

0.32

7*(0

.151

)H

ealt

hy

20.

006

(0.1

87)

20.

006

(0.1

89)

20.

006

(0.1

89)

Mal

e0.

144

(0.1

54)

0.14

4(0

.153

)0.

145

(0.1

52)

Age

0.04

8*(0

.020

)0.

043*

(0.0

20)

0.04

3*(0

.020

)H

igh

inco

me

20.

262

(0.2

68)

20.

259

(0.2

53)

20.

258

(0.2

54)

Mid

inco

me

20.

155

(0.1

98)

20.

126

(0.2

07)

20.

125

(0.2

07)

Th

ree

veh

icle

s2

0.18

5(0

.244

)2

0.19

4(0

.261

)2

0.19

5(0

.261

)B

ike

0.26

2*(0

.133

)0.

261*

(0.1

31)

0.26

2*(0

.131

)C

onst

ant

22.

518y

(1.2

85)

22.

243y

(1.3

19)

22.

241y

(1.3

20)

Alp

ha

0.36

0y(0

.215

)0.

301

(0.2

00)

0.30

1(0

.201

)M

easu

rem

ent

mod

eles

tim

atin

gP

roW

alka

bili

tyI 1

:I

pre

fer

toh

ave

sho

ps

and

serv

ices

wit

hin

wal

kin

gd

ista

nce

1.00

0(0

.000

)1.

000

(0.0

00)

I 2:

Id

on

ot

valu

esp

ace

aro

un

dm

yh

ou

sem

ore

than

sho

ps

nea

rby

0.89

3**

(0.0

93)

0.89

3**

(0.0

93)

I 3:

Ili

kea

nei

ghb

ou

rho

od

con

tain

ing

ho

usi

ng,

sho

ps

and

serv

ices

0.92

9**

(0.0

45)

0.92

9**

(0.0

45)

I 4:

Id

on

ot

pre

fer

alo

to

fsp

ace

bet

wee

nm

yh

om

ean

dth

est

reet

0.52

0**

(0.0

80)

0.52

0**

(0.0

80)

(con

tin

ued

)




Tab

leA

2.(C

onti

nued

)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

oice

mod

el

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

I 5:

Ip

refe

ra

ho

use

clo

seto

the

sid

ewal

kso

that

Ica

nse

ep

asse

rsb

y0.

625**

(0.0

51)

0.62

5**

(0.0

51)

Pro

Segr

egat

ion

I 6:

Ip

refe

rn

eigh

bo

urs

atth

esa

me

stag

eo

fli

feas

me

1.00

0(0

.000

)1.

000

(0.0

00)

I 7:

Ip

refe

rli

vin

gar

ou

nd

peo

ple

wh

oar

esi

mil

arto

me

0.59

3**

(0.0

85)

0.58

8**

(0.0

84)

I 8:

Iam

con

cern

edab

ou

tst

ran

gers

wal

kin

gth

rou

ghm

yn

eigh

bo

urh

oo

d0.

465**

(0.0

90)

0.45

9**

(0.0

88)

I 9:

Ili

keto

live

ina

nei

ghb

ou

rho

od

wit

ho

ut

chil

dre

nin

it0.

354**

(0.0

60)

0.35

2**

(0.0

60)

Stru

ctu

ral

(MIM

IC)

mod

eles

tim

atin

gP

roW

alka

bili

tyE

mpl

oy2

0.01

0(0

.067

)2

0.01

0(0

.067

)H

ealt

hy

20.

024

(0.0

83)

20.

024

(0.0

83)

Mal

e2

0.22

6**

(0.0

53)

20.

225**

(0.0

53)

Age

0.00

4(0

.008

)0.

003

(0.0

08)

Hig

hin

com

e0.

044

(0.0

91)

0.04

4(0

.091

)M

idin

com

e2

0.09

3(0

.079

)2

0.09

3(0

.079

)T

hre

eve

hic

les

20.

300**

(0.0

76)

20.

300**

(0.0

76)

Bik

e2

0.04

6(0

.067

)2

0.04

6(0

.067

)

Pro

Segr

egat

ion

Em

ploy

20.

167*

(0.0

65)

20.

168*

(0.0

65)

Hea

lth

y0.

124

(0.0

81)

0.12

4(0

.081

)M

ale

0.12

6*(0

.049

)0.

126*

(0.0

49)

Age

20.

001

(0.0

08)

20.

001

(0.0

08)

Hig

hin

com

e2

0.03

1(0

.091

)2

0.03

1(0

.092

)M

idin

com

e2

0.07

8(0

.079

)2

0.07

8(0

.079

)T

hre

eve

hic

les

0.02

9(0

.070

)0.

029

(0.0

70)

Bik

e2

0.12

8*(0

.064

)2

0.12

8*(0

.065

)

(con

tin

ued

)




Tab

leA

2.(C

onti

nued

)

Mod

el1:

ZIN

Bw

ith

out

late

nt

vari

able

sM

odel

2:SE

Mw

ith

late

nt

vari

able

sM

odel

3:SE

Mw

ith

age-

rest

rict

edch

oice

mod

el

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Coe

ffic

ien

t(S

.E.)

Log

itM

odel

Est

imat

ing

‘Lik

elih

ood

ofch

oosi

ng

age-

rest

rict

edn

eigh

bou

rhoo

d’

Pro

Wal

kabi

lity

0.13

8(0

.180

)P

roSe

greg

atio

n0.

815**

(0.1

48)

Loo

p5.

982**

(0.7

08)

Inte

rsec

tion

den

sity

2.20

5**

(0.3

84)

Fac

ilit

ies

3.39

7**

(0.3

41)

Des

tin

atio

n40

0-0

.112

(0.3

00)

MB

TA

bus

stop

22.

641**

(0.9

40)

Com

mu

ter

rail

0.19

6(0

.432

)St

reet

len

gth

0.10

6**

(0.0

14)

Em

ploy

-0.2

61(0

.270

)H

ealt

hy

0.52

9(0

.340

)M

ale

20.

445*

(0.1

92)

Age

0.20

5**

(0.0

37)

Hig

hin

com

e1.

303**

(0.3

97)

Mid

inco

me

0.46

7(0

.329

)T

hre

eve

hic

les

20.

266

(0.4

36)

Bik

e2

0.32

1(0

.267

)

Nu

mb

ero

fp

aram

eter

s37

8410

2N

um

ber

of

ob

serv

edva

riab

les

1827

27Id

enti

fica

tio

nO

veri

den

tifi

ed84

\0.

5*27*

(27+

1)O

veri

den

tifi

ed10

2\

0.5*

27*

(27+

1)

Not

es:

yp\

0.10

;*

p\0.

05;**

p\0.

01.




urban studies - mitweb.mit.edu/ebj/www/55_files/zegras lee ben-joseph... · 2012-02-28 · (alfonzo...

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