A model-based approach to select case sites for walkability audits

Health & Place 18 (2012) 1323–1334

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Health & Place

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n Corr

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

A model-based approach to select case sites for walkability audits

Md. Moniruzzaman, Antonio Paez n

School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada

a r t i c l e i n f o

Article history:

Received 29 January 2012

Received in revised form

17 September 2012

Accepted 30 September 2012Available online 9 October 2012

Keywords:

Walkability audits

Site selection

PEDS

Micro-scale built environment

Travel behavior

92/$ - see front matter & 2012 Elsevier Ltd. A

x.doi.org/10.1016/j.healthplace.2012.09.013

esponding author. Tel.: þ1 905 525 9140.

ail addresses: [email protected] (Md. M

mcmater.ca, [email protected] (A. Paez).

a b s t r a c t

Walkability audits provide valuable information about pedestrian environments, but are time-consuming

and can be expensive to implement. In this paper, we propose a model-based approach to select sites for

conducting walkability audits. The key idea is to estimate a model of travel behavior at the meso-scale level,

which can be examined to identify locations where the behavior is under- and over-estimated. We

conjecture that systematic under- and over-estimation can be caused by micro-level factors that influence

the behavior. The results can be used to identify sites for walkability audits. The approach is demonstrated

with a case study in Hamilton, Canada. A model of walk shares forms the basis of the site selection

procedure. After identifying areas with higher and lower shares than predicted by the model we select a

sample of neighborhoods for audits. Analysis of the results reveals elements of the local environment that

associate with greater-than-expected walk shares. The case study demonstrates that the proposed model-

based strategy can be used to better target limited resources, and produce valuable insights into micro-level

factors that affect travel behavior.

& 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Canada, like other developed countries around the world, facesincreasing trends of overweight and obesity incidence, diabetes,cardiovascular disease, various types of cancer, high bloodpressure, depression, stress, and anxiety (Chen and Millar, 1999;Gilmour, 2007; Tjepkema, 2006; Warburton et al., 2007).Evidence shows that regular moderate-to-vigorous physical activ-ity has health benefits that can improve these conditions (Janssen,2007; Pate et al., 1995; Paterson et al., 2007; Warburton et al.,2010; Warburton et al., 2006). However, while current guidelinesindicate that Canadian adults should participate in at least150 mins of moderate-to-vigorous physical activity per week inbouts of at least 10 min (Public Health Agency Canada, 2011;Warburton et al., 2007), participation rates for Canadians inleisure time physical activity is very low (Vanasse et al., 2006).Active transportation, such as walking and cycling, represents analternative way to meet minimum physical activity requirements,in a convenient way that can be weaved into lifestyles withoutadditional time demands. In addition to the health benefits, thereare numerous other benefits of active transportation. Theseinclude (Government of Alberta, 2011): environmental benefits(e.g. reduce traffic congestion, air pollution, noise), economicbenefits (e.g. reduce individual transportation cost, infrastructure

ll rights reserved.

oniruzzaman),

cost for motorized transport), and built environmental benefits(e.g. reduce space requirements for roads and parking lots).

A key factor to realize the potential of active travel as acomplementary physical activity is a suitable built environment withsupportive characteristics (e.g. Frank et al., 2004; Frank et al., 2003;Sallis et al., 1998). Representative characteristics of the built environ-ment are commonly summarized as the 3Ds: Density, Diversity, andDesign (Cervero and Kockelman, 1997). Multiple studies have soughtto clarify the effect of the built environment on travel behavior ingeneral, and active travel in particular. Statistical analyses often haveto grapple with the issue of scale, either implicitly, depending on dataavailability (e.g. the smallest available Census geography), or expli-citly, if further data aggregations are desired or recommended (e.g.Boarnet and Sarmiento, 1998; Guo and Bhat, 2007). When suchaggregations are made, a distinct possibility is that some informationabout the built environments will be lost, especially in terms ofmicro-scale design factors such as sidewalk width, path materials,and number of curb cuts, and more qualitative attributes such asstreet cleanliness (e.g. Frank et al., 2003; Saelens et al., 2003). Theseare all potential factors that affect walking environments, and yet,that are seldom collected by local governments or other agencies insufficiently wide and systematic ways (Parmenter et al., 2008). Even ifavailable, measurements and analysis of administrative-level datamay be insufficient to capture micro-scale effects, due to currentlimitations in the understanding of geo–spatial perception (e.g.mental maps) and a lack of canonical representation approaches.

In order to create inventories and assessments of the localenvironments at the pedestrian scale, a suite of methods has beendeveloped in recent years under the label of walkability audits.

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Md. Moniruzzaman, A. Paez / Health & Place 18 (2012) 1323–13341324

A walkability audit is a data collection instrument used to itemizeand assess different aspects of the local, pedestrian-level environ-ment, including sidewalks, crossing aids, traffic calming devices, andother pedestrian-supportive design features. Numerous audit instru-ments exist that can provide rich and detailed information aboutwalking environments (Clifton et al., 2007; Dannenberg et al., 2005;Day et al., 2006; Millington et al., 2009; Moudon and Lee, 2003;Pikora et al., 2002). A common trait of existing instruments is thatthey require substantial amounts of field work, as auditors conductvisual inspections of the environment and complete inventories ofitems of interest. Considering that resources are usually limited, thishas prompted an emerging interest in the development of methodsto allow a better targeting of efforts. Accordingly, the objective ofthis paper is to propose a model-based approach to select case sitesfor field research into the factors that influence active travel.

In very simple terms, our proposal is to identify sites ofinterest where walking is more or less common than expected.Walkability audits using a suitable instrument can then beconducted to verify the differences between local environmentsthat are more or less supportive of walking. The technical basis forthe approach is based on the inspection of the unexplained partof a model of some relevant behavior. We hypothesize thatsystematic under- and over-estimation of the behavior is relatedto micro-scale factors that affect pedestrian mobility; in otherwords, we hypothesize that better walking environments will befound for locations where the model tends to under-estimatewalking-related travel behaviors, and vice-versa.

As a proof of principle, we implement the proposed approach inthe city of Hamilton, Canada, based on a model of walk shares at theDissemination Area (DA) level obtained from the Census. Theresulting model is then used to identify DAs where the share ofwalk is under-estimated (the actual share is higher than predictedby the model) or over-estimated (the actual share is lower than thepredicted share). Based on the examination of patterns of under-and over-estimation, we select 20 DAs for walkability audits, out ofover 500 DAs in the city. These 20 DAs yield 175 street segments inDAs where the model under-estimates walking, and 168 in DAswhere the model over-estimates walking. This is a total of 343 streetsegments for auditing, out of over 15,000 street and road segmentsin the city. Walkability audits are then conducted, and the dataanalyzed using contingency tables and the w2 test of independence.

The results of the analysis of audit data reveal a number ofitems commonly found in DAs with higher-than-expected sharesof walk, including high volume streets with mixed uses, flat streetsegments with 4-way intersections, complete and highlyconnected sidewalks with presence of crossing aids, presence ofamenities and trees along the sidewalks, and compact develop-ment. Attributes found to associate with lower than expectedshares of walk, include low volume street segments with singleland use development, slight or steep hill streets with other typeof intersections, missing or incomplete and less connected side-walks with no crossing aids, and absence of amenities and treesalong the sidewalks, and sparse development. Differences in theseattributes lend credence to our conjecture that higher shares ofwalking than predicted by the model correspond to neighbor-hoods that provide better walking environments. The experimentthus demonstrates that our model-based selection strategy forwalkability audit sites can be used to more effectively targetlimited resources for field-based work, and produce valuableinsights about the micro-scale factors that affect active travel.

2. Prior research

Previous research has found that pedestrian-friendly walkingenvironments are conducive for people to walk with increased

frequency (Cervero and Kockelman, 1997; Cervero et al., 2009;Guo, 2009; Hess et al., 1999; Kitamura et al., 1997; Rodriguez andJoo, 2004). Many studies assess the pedestrian environment interms of sidewalk density, street density, and street connectivity.These are widely available data items that can be convenientlyprocessed using a geographic information system. Increasingly,however, there is a need to collect environmental data in moredetail, to reflect the fact that walking, as a slow mode, impliesthat pedestrians experience the environment in a qualitativelydifferent way (Moudon and Lee, 2003). Walkability audit instru-ments help fill the gap left by other statistics, as they can be usedto collect micro-scale information about pedestrian environ-ments. Two relevant aspects of these instruments are theirresolution and extent (Moudon and Lee, 2003). Resolution refersto the precision of the items considered for the audits, whereasthe extent refers to the geographic area where the audit takesplace. To date most of the studies pertaining to walkability auditshave dealt with the issue of resolution, and the development ofuser friendly, efficient, and reliable instruments (Clifton et al.,2007; Dannenberg et al., 2005; Day et al., 2006; Millington et al.,2009; Pikora et al., 2002).

The required extent of audit tools, in contrast, has not yetreceived much attention, perhaps because some instruments arestill in their developmental stages (Brownson et al., 2009; Sallis,2009). However, considering the time and cost involved inconducting comprehensive audits over entire study areas, there iscurrently an interest in devising more effective protocols to assist inthe determination of the best extent for a project. McMillan et al.(2010) state that the principal limitation of walkability audits is‘‘the time and cost involved in data collection’’ (p. 1). Clarke et al.(2010) note that ‘‘in-person audits are highly resource intensive andcostly, making them prohibitive for many studies.’’ (p. 1224). Theseopinions are echoed by Rundle et al. (2011), who also report thataudits ‘‘are time-consuming and expensive to conduct largelybecause of the costs of travel’’ (p. 95). Among the walkability auditsavailable, the Pedestrian Environment Data Scan (PEDS) instrument(Clifton et al., 2007) requires on average 3–5 min per segment toaudit on foot, compared to 10 min for the Analytic Audit Tool ofSaint Louis University (Hoehner et al., 2005), 20 min for the IrvineMinnesota Inventory (Day et al., 2006), and 30 min for the WalkingSuitability Assessment Form (Emery et al., 2003). In the case of anew project, the total project cost must also consider the timerequired for training, reliability testing, and other initial items.When these costs are included, the total cost can be substantiallygreater than the nominal cost of conducting an audit.

In principle, the cost of implementing walkability auditscan be reduced by sampling streets within neighborhoods,by sampling neighborhoods, or a combination of these twoapproaches. In a study that assessed the effect of sampling streetson the reliability of audit data, McMillan et al. (2010) collecteddata comprehensively on five variables (presence of sidewalks,observer ratings of attractiveness and safety for walking,connectivity, and number of traffic lanes) for neighborhoods of400 m and 800 m around a specific location. Then, they comparedthe results to those obtained from sampling street segments at75%, 50%, and 25% levels. Based on the results of this analysis,these authors conclude that a sample of 25% residential streetsegments within 400 m radius of a residence is adequate for thepedestrian built environment, although a fuller sample of arterialstreet segments may be required due to higher heterogeneity.

A different possibility is to reduce the cost of implementationby reducing the cost of doing field work. Along these lines, a studyby Clarke et al. (2010) proposes the use of Google Street View as away to limit the cost of objectively measuring pedestrian envir-onments. After selecting neighborhoods for the audits, Clarkeet al. conducted the audits virtually using geo–spatial technology

Md. Moniruzzaman, A. Paez / Health & Place 18 (2012) 1323–1334 1325

provided by Google. In order to test this approach, pre-existingdata collected using the same instrument as part of a previousproject were used to assess the reliability of these ‘‘virtual’’ audits,relative to the ‘‘in-person’’ audits. The results indicate that avirtual audit is able to provide reliable information about recrea-tional facilities, local food environment, and general land uses.However, care must be taken for more finely detailed observa-tions, for instance, presence of garbage, litter, or broken glass.A similar approach is used by Rundle et al. (2011) to find that54.3% of items audited have high levels of concordance in term ofpercentage agreement for categorical measures and spearmanrank-order correlation for continuous measures. In another studyBias et al. (2010) assessed the reliability of a ‘quick and dirty’neighborhood walkability survey instrument in a completelysubjective way. Instead of walking through the street segmentsto collect information, they asked people over telephone abouttheir perception of physical activity friendliness of the commu-nity they belong to.

There is a key difference between the method proposed here andthe methods of McMillan et al. (2010), Clarke et al. (2010), Rundleet al. (2011), and Bias et al. (2010). These four approaches can helpto control the cost of conducting walkability audits by reducing thenumber of street segments visited (after sampling within a neigh-borhood), or by entirely eliminating the need for in-person visits(by conducting virtual audits or asking over telephone). However,neither of these approaches provides guidelines for selectingneighborhoods for the audits or interviews. A random selection ofneighborhoods may fail to capture the wide range of attributes inan entire study area, and as a consequence, the results may beinaccurate or even misleading. Selection based on one or twoattributes of areas (e.g. household income, level of education) risksmissing other confounding factors. Therefore, assuming that even ata reduced cost audits for a moderately large geographic area can beexpensive, the intent of our approach is to provide a way to sampleneighborhoods for walkability audits in a systematic way that canaccount for a wide range of confounding factors. Implementation ofthe audits can subsequently, as desired, proceed by sampling streetsegments (McMillan et al., 2010), by conducting virtual site visits(Clarke et al., 2010; Rundle et al., 2011), or by telephonicallyinterviewing residents (Bias et al., 2010). The proposed approachis described next.

Fig. 1. Example of a generic model.

Fig. 2. Examples of random and spatially autocorrelated residuals.

3. Methods

3.1. Residual spatial pattern and selection of sites

Multivariate statistical models are frequently estimated usingmeso-scale (administrative) data in order to assess the effect ofvarious variables on travel behavior. If Y is the behavior ofinterest, these models are generally composed of two elements,namely the observed/explained and unobserved/unexplainedparts, as follows:

Yi ¼ f Xi,bð Þþei ð1Þ

In Eq. (1), Xi is a vector of (observed) explanatory variables, b isa vector of estimable coefficients, and ei is a residual term(unobserved). Depending on the nature of the dependent variableY, the model can be a linear regression (for continuous variables)or a generalized linear model (for proportions or counts). Regard-less of the specific form of the model, a key assumption is that theresidual terms are random, and thus uncorrelated across units i.In practice, this assumption is violated with sufficient frequencyin spatial modeling, as to have given rise to a specialized set oftechniques to deal with situations where residuals are spatially

autocorrelated (Anselin, 1988; Cliff and Ord, 1973, 1981; Griffith,1988; Haining, 1990).

Spatial autocorrelation of the residuals is often attributed tocommon, but unobserved, factors that influence the process. Considerfor example the situation illustrated in Fig. 1, which depicts a simplegeneric model (a bivariate regression) with seemingly well-behavedresiduals. Clearly, the observations above the regression line areunder-estimated by the model (the actual behavior is more frequentthan predicted), and observations below the regression line are over-estimated (the actual behavior is less frequent than predicted).Depending on the actual spatial distribution of the observations,the residuals could be independent (Fig. 2a) or spatially autocorre-lated (Fig. 2b) – an indication that a relevant variable with a spatialpattern may have been omitted (e.g. a variable common in the northof the region, but absent in the south).

We hypothesize that residual spatial autocorrelation is caused, atleast in part, by the omission of relevant micro-scale variables (i.e.variables at the level of the pedestrian experience) that follow a spatialpattern. Specifically, in the case of under-estimation, our conjecture isthat the omitted variables are related to elements of the environmentthat facilitate/encourage the behavior (e.g. greater connectivity ofwalkable paths, cleanliness), whereas variables that hinder/discouragethe behavior are related to over-estimation of the behavior (e.g. lack ofsidewalks or amenities). The similarity of attributes among spatiallyproximate street segments is in fact the operational basis of thesampling procedure proposed by McMillan et al. (2010), and is aneffect that has been noted as well by Agrawal et al. (2008).

Given a statistical model of a behavior of interest, our proposal isto select sites for walkability audits based on the examination of theunexplained part of the model. An attractive feature of this proposalis that the unexplained part is obtained after controlling for asuitable set (based on availability) of explanatory/confoundingfactors. Assuming that the residuals are independent of the

Fig. 3. Paired control cases.


variables included in the model, the cases can be selected based ondifferent levels of the dependent variable only, since potentialconfounders are accounted for in the model. Thus, if the focus ofthe project is on factors that facilitate the behavior, then sites canbe selected from the set of observations that the model under-predicts. If the focus is on the factors that hinder the behavior, thenselection can be made from the set of observations that the modelover-predicts. When control cases are sought, pairs of observationsat different levels of the behavior can be identified, one each fromthe under-estimated and over-estimated sets, at similar deviationsfrom the model (see Fig. 3).

3.2. Spatial filtering: retrieving the autocorrelation in the residuals

Direct inspection of the residuals can be done visually bymapping them, or in a more quantitative fashion, using anysuitable statistic of spatial autocorrelation (for instance Geary’sc or Moran’s I; see Griffith and Layne, 1999). Alternatively, theautocorrelated pattern can be extracted from the residuals using aspatial filtering approach (Getis and Griffith, 2002). Filteringapproaches are used in regression analysis in order to clean theresiduals, to ensure that these terms are spatially random(Griffith, 2003). The advantage of using a filtering approach, asopposed to direct examination of the residuals, is that the filterextracts the spatial pattern, minus the truly random effects thatremain in the residuals. The pattern retrieved is therefore undis-turbed by random variation.

A spatial filtering approach that we favor is based on the well-known Moran’s I coefficient of spatial autocorrelation, which isdefined in matrix form for a mean-centered spatial randomvariable x as follows:

I¼x0Wx

x0xð2Þ

In Eq. (2), W is a spatial lag operator that codifies relationshipsof proximity between spatial units (for instance wij¼1 if i and j

share a common border, and 0 otherwise). As Griffith (2000, 2003,2004) shows, eigenvector analysis of the spatial lag operator canbe used to generate spatial filters for use in regression analysis.Concretely, the spatial filter is generated using a linear combina-tion of eigenvectors extracted from the spatial lag operator matrixafter the following transformation:

I�110

n

� �W I�

110

n

� �ð3Þ

where I is the identity matrix, 1 is a column vector of ones, and n

is the number of spatial units in the analysis. It can be seen thatexpression (3) is part of the numerator of Moran’s coefficient.The eigenvectors of the transformed lag operator represent n

uncorrelated map patterns each with a distinctive degree of spatialautocorrelation, as measured by Moran’s I (Griffith, 2003; Tiefelsdorfand Boots, 1995). The first eigenvector E1 corresponds to the mapwith the highest degree of positive spatial autocorrelation achiev-able given the spatial configuration represented by W. This isfollowed in decreasing order by E2, and through En, which is theset of numerical values with the highest possible degree of negativespatial autocorrelation.

Spatial filtering is a non-parametric approach, whereby thefilter, constructed through a combination of selected eigenvec-tors, constitutes a synthetic variable that acts as a surrogate forrelevant but omitted variables in the regression model. Since theeigenvectors are uncorrelated, one possible procedure to selecteigenvectors for the filter is based on a forward stepwise searchprocedure as follows:

1.
Initialize an index value i¼1 and an empty vector for thespatial filter S¼[ ]; set Xþ¼X.
2.
Select eigenvector Ei as a candidate for inclusion in the model,and estimate the model Y ¼ f ½Xþ, Ei�,½b,y�
� �þe

3.
If coefficient y is significant at a pre-determined level (e.g.pr0.05), then synthesize the eigenvector and the existingfilter: S¼SþyEi, and continue to step (4), otherwise, return tostep (3)
4.
Re-set matrix Xþ as follows: Xþ¼[X, S], and estimate themodel Y ¼ f Xþ, b
� �þe.

5.
Determine the degree of autocorrelation in the estimatedresiduals; if there is significant residual autocorrelation, seti¼ iþ1 and return to 3, otherwise end. The spatial filter isvector S.
The above procedure sequentially removes autocorrelationfrom the residuals, until the residuals are spatially random, andthe systematic pattern has been transferred to the spatial filternow in the mean of the process. The spatial filter can be examinedat this point to identify positive values (observations that themodel under-estimates and that therefore need a positive correc-tion) and negative values (observations that the model over-estimates). The procedure is illustrated next.

4. Case study

4.1. Context

The proposed approach is demonstrated by means of a casestudy in the city of Hamilton, Canada. Hamilton is a medium-sizedcity (population approx. half-million) that identifies as part of itsofficial urban plan the promotion of alternative modes of transpor-tation, including active modes and transit (City of Hamilton, 2011).

Meso-scale analysis in our case is based on a model of the shareof walk as a mode of transportation for the journey to work. Theshares of walking trips were calculated from the number ofcommuters by different modes of transportation obtained fromthe Canadian Census prepared by Statistics Canada. The level ofaggregation is the Dissemination Area (DA), the smallest CanadianCensus geography. There are 845 DAs in total in the city ofHamilton. Of these, 296 DAs were removed prior to the analysisbecause their shares of walking were zero. Explanatory variablesrelating to the demographic and socio-economic characteristics ofDAs were obtained from 2006 Canadian Census, whereas builtenvironment characteristics were extracted from 2009 geographicfiles provided by the GIS Planning and Analysis Section, Planningand Economic Development Department, City of Hamilton.

The appropriate method to model walk shares is by means of alogistic model for proportions. An extensive set of explanatory

Table 1Results of a logistic model for proportion of commuting trips by walking (dependent variable is proportion of walking trips).

Variable Coefficient p-value

Constant �2.80826 0.00000

Demographic characteristics# of person age less than 20 per household – –

# of person age 20–34 per household – –



# of person age 65 or above per household – –

# of children age less than 15 per household �0.33021 0.00200

# of children age 15–17 per household – –

# of children age 18–24 per household – –

# of children age 25 or above per household – –

Population density – –

Total immigrant – –

Immigrant density – –

Socio-economic characteristicsTotal employment – –

Total employmentL – –

Employment density – –

# of fulltime managerial jobs �0.00342 0.00040

# of part-time managerial jobs – –

# of fulltime service jobs – –

# of part-time service jobs 0.00101 0.00000

Logarithm of median household income in 10’s thousands �0.13188 0.00000

Built environment characteristicsLand use mix 0.62675 0.00000

Sidewalk density 0.01395 0.00000

Sidewalk densityL – –

Street density – –

Street densityL – –

Intersection density – –

Intersection densityL – –

Dwelling density in 10’s of thousands 0.43015 0.00000

Distance to the nearest school – –

Spatial filterFilter (19 eigenvectors) 1.00000 0.00000

n¼549; Over-dispersion parameter (estimated)¼2.53; pseudo�R2¼0.592; Moran’s I (Z)¼0.449

Note: L indicates lag of the corresponding variable.

Table 2Summary of significant attributes using w2 independence tests.

Item Significance (po0.10)

Walk filter (P-N)

0. Segment type Significant

A. EnvironmentUses in segment Significant

Slope Significant

Segment intersections Significant

B. Pedestrian facilityPath condition/maintenance n.s.

Sidewalk completeness Significant

Sidewalk connectivity Significant

Path obstructionsn n.s.

Sidewalk widthn n.s.

C. Road attributesTraffic control devices Significant

Crossing aids Significant

Bicycle facilities Significant

D. Walking environmentPath lightingn n.s.

Amenitiesn Significant

Way finding aids Significant

Trees shading walking arean Significant

Degree of enclosure n.s.

Cleanliness/building maintenance Significant

Building setbacks Significant

Building height Significant

Bus stops n.s.

n.s.¼not significant.n Sample size 314 (29 segments were excluded as they do not have any

sidewalk).


variables was tested, including land use mix, sidewalk density,street density, intersection density, as well as population socio-economic and demographic characteristics. All these variableswere aggregated at the DA level. A backward specification searchwas followed to establish a model for the walking shares,whereby variables that were not significant at the p¼0.05 levelwere removed from the model. Simultaneously, the procedureoutlined in the preceding section was implemented for thegeneration of the spatial filter. The resulting model appears inTable 1, where it can be seen that residual spatial autocorrelationhas been successfully removed by means of a filter composed of19 eigenvectors. An over-dispersion factor is estimated to providesharper inference of the coefficients (see Moniruzzaman and Paez,2012). Table 1 shows the list of variables tested for the model, andthose retained with significance levels at pr0.05.

4.2. Selection of sites

Of the DAs included in the analysis, 288 have negative valuesof the spatial filter, and 261 have positive values. In order to selectDAs for walkability audits, firstly the spatial filter was dividedinto three quantiles. The Quantile 1 contains exclusively DAs withnegative values of spatial filter (walking less prevalent thanpredicted). Quantile 3 contains exclusively DAs with positivevalues of spatial filter (walking more prevalent than predicted).Quantile 2 includes DAs with positive and negative spatial filtervalues close to zero. These are locations where the meso-scalemodel of walk shares provides the most accurate representationof the factors that influence walking. Sites were selected with

Table 3Audit items and summary of contingency tables.

Item Notes (þ: positive filter; -: negative filter)

0. Segment type (þ) More high volume segments, fewer low volume segments; (�) more low volume segments, fewer high volume segments.

A. EnvironmentUses in segment (þ) More mixed uses segments, fewer single use segments; (�) more single use segments, fewer mixed uses segments.

Slope (þ) More flat segments, fewer slight or steep hill segments; (�) more slight or steep hill segments, fewer flat segments.

Segment intersections (þ) More segments with 4-way intersection, fewer segments with other types of intersection; (�) more segments with other

intersection, fewer segments with 4-way intersection.

B. Pedestrian facilitySidewalk completeness (þ) More segments with complete sidewalk, fewer segments with incomplete or missing sidewalk; (�) more segments with

incomplete or missing sidewalk, fewer segments with complete sidewalk.

Sidewalk connectivity/

Crosswalks

(þ) More segments with high connectivity, fewer segments with less connectivity; (�) more segments with less connectivity, fewer

segments with high connectivity.

C. Road attributesTraffic control devices (þ) More segments with presence of traffic control devices, fewer segments with absence of traffic control devices; (�) more

segments with absence of traffic control devices, fewer segments with presence of traffic control devices.

Crossing aids (þ) More segments with presence of crossing aids, fewer segments with absence of crossing aids; (�) more segments with absence

of crossing aids, fewer segments with presence of crossing aids.

Bicycle facilities (þ) More segments with presence of bicycle facilities, fewer segments with absence of bicycle facilities; (�) more segments with

absence of bicycle facilities, fewer segments with presence of bicycle facilities.

D. Walking environmentAmenitiesn (þ) More segments with presence of amenities, fewer segments with absence of amenities; (�) more segments with absence of

amenities, fewer segments with presence of amenities.

Way finding aids (þ) More segments with presence of way finding aids, fewer segments with absence of way finding aids; (�) more segments with

absence of way finding aids, fewer segments with presence of way finding aids.

Trees shading walking arean (þ): More segments with none/very few trees, fewer segments with some/dense greenery; (�) more segments with some/dense

greenery, fewer segments with non/very few trees.

Overall cleanliness/building

maintenance

(þ) More segments with low path cleanliness/building maintenance score, fewer segments with high path cleanliness/building

maintenance score; (�) more segments with high path cleanliness/building maintenance score, fewer segments with low path

cleanliness/building maintenance score.

Building setbacks (þ) More segments with smaller setbacks, fewer segments with large setbacks; (�) fewer segments with smaller setbacks, more

segments with large setbacks.

Building height (þ) More segments with multi-floor buildings, fewer segments with low-rise buildings; (�) fewer segments with multi-floor

buildings, more segments with low-rise buildings.

1 The scripts in ArcPad are available from the authors upon request.


attention to the following considerations: 1) sites are in Quantile1 (negative filter) or Quantile 3 (positive filter); 2) selection ofDAs yields a sufficient number of street segments for statisticalanalysis using contingency Table 3) selection of DAs covers a widerange of actual walking shares; and 4) street segments are evenlydistributed among sites with positive and negative filter values.

Given resource constraints, twenty DAs were selected forwalkability audits. Of these, nine DAs were selected from theQuantile 1 (–), and 11 DAs were selected from Quantile 3 (þ).When selecting DAs from the quantiles, careful attention was paidto the values of walking shares, to ensure that both low and highshares were included in the selection. The twenty DAs selectedyield 343 street segments, a sufficiently large sample of streetsegments for contingency tables and tests of independence, butstill only a fraction of all streets in the region. The distribution ofstreet segments is almost equally distributed among DAs withpositive and negative values of the filter, and therefore thecontingency tables are not dominated by either condition. Thesites selected are shown in Fig. 4.

4.3. Audit instrument and technology

Walkability audits were conducted using the PedestrianEnvironment Data Scan (PEDS) instrument, (Clifton et al., 2007).PEDS was selected because of its relatively low data collectiontime and high resolution. The instrument was developed in such afashion that it can measure various built and natural environ-mental features pertaining to walking in an efficient and reliableway (Clifton et al., 2007). The spatial unit of analysis for auditingpedestrian environments using the PEDS tool is the road orpathway segment. In our study, we have audited road segmentsonly. Further, PEDS resource materials were initially developed asa pencil and paper instrument, but later adapted for use with

handheld technology (Clifton et al., 2007). For our study, we alsoadapted PEDS from its original pencil and paper format to digitalformat using ArcPad Application Builder 7.0.1.

The electronic version of PEDS greatly facilitates data entry.1

Most of the attributes in PEDS are categorical, with the exceptionof sidewalk connectivity, number of lanes, and posted speed limit.For categorical variables, categories can be selected by clicking onthe drop-down arrow. Few of the attributes could have morethan one answer. Check-boxes were provided for such attributes(Fig. 5). The digital version of PEDS audit then transferred into amobile device which can support ArcView geographic informationfile. This technology eliminates the time required for data entry.It also provides improved quality of data and reduces errors(Clifton et al., 2007).

4.4. Walkability audits

Audits of all street segments within selected DAs were con-ducted by the first author in October, 2011, with support fromthree graduate research assistants. The research assistants receivedtraining from the first author using the training materials providedby Clifton et al. (2007). In addition, a pilot audit was conductedbefore the final audits were launched. Street network informationwas collected in electronic format by the City of Hamilton (GISServices, Information Technology Services). In total, 343 streetsegments within the 20 DAs were audited exhaustively. As men-tioned earlier, DAs were selected in such a fashion that two groupsof filter (positive and negative) would have relatively equal numberof street segments. As a result, out of the 343 segments, 175 (51%)were from DAs with a positive filter value and 168 (49%) were from

Fig. 4. Selected sites for audits: DAs with less walking than predicted (SF in Quantile 1); DAs with walking levels more or less as predicted (SF in Quantile 2);

DAs with more walking than predicted (SF in Quantile 3).


DAs with negative values. The average length of the street segmentswas 165.73 m. In addition to completing the audits, segments werephotographed for off-site examination. The total cost of conductingthe audits, excluding the research time and development of theelectronic version of PEDS, did not exceed CAD 3,000.

5. Results

5.1. Contingency tables

The results of the walkability audits were transferred to adatabase for analysis. In addition to segment type, PEDS organizesthe audit items by category, of which there are four (environment,pedestrian facility, road attributes, and walking environment),and two subjective assessments to be completed by the auditor(segment is attractive/safe for walking). A few attributes were notanalyzed as we do not have reasonable hypotheses for them orare not applicable to walking or the city of Hamilton (e.g. pathmaterial, path distance from curb, condition of road, off-streetparking lot spaces, articulation in building design). In order toanalyze the information contained in the audits, contingency tableswere prepared. With the exception of sidewalk connectivity, whichis audited as a continuous variable, all attributes are categorical.Sidewalk connectivity was categorized for analysis using a contin-gency table. All contingency tables were assessed using w2 tests ofindependence, whereby the observed frequency of cases in each cellin the table is compared to the expected frequency of cases underthe null hypothesis of independence.

Tables 2 and 3 show the summary of the tests of indepen-dence. As shown there, the null hypothesis was rejected forfifteen audit items, six of which correspond to the walkingenvironment category and three of which correspond to theenvironment category of PEDS. The table also summarizes the

main findings from the analysis of contingency tables. As seen inthe table, all of the significant attributes, with the exception ofoverall cleanliness and building maintenance, lend support to ourproposed conjecture that DAs with higher-than-expected sharesof walking would display more walkability traits.

5.2. Discussion

First, street segments were categorized as low volume andhigh volume. A low volume segment is defined in PEDS as a streetwith a single lane, or two lanes without clear road marking todemarcate the lanes, and little or infrequent traffic. Streets withmore than one lane and some traffic are classified as high volume.Fig. 6 shows examples of low and high volume street segments.Low volume segments were observed with significantly higherfrequency in DAs with a negative value of the spatial filter,whereas high volumes were observed more in DAs with positivefilter values.

The attribute of land uses in the segment was audited as achecklist with different types of land uses (e.g. housing, office/institution, industrial, or recreational). For analysis, the attributewas reclassified as single use, if only one type of land use wasobserved in the segment, and mixed if two or more land use typeswere observed. The contingency table for this item appears inTable 4. It can be seen that segments with mixed uses weresignificantly more common in DAs where the shares of walkingare higher than predicted by the model (90 cases versus 66.3expected). In contrast, DAs with a negative value of the filter hadsignificantly more segments characterized by single uses thanexpected (128 cases versus 104.3 expected).

The slope condition of the segments was audited as flat, slighthill, and steep hill. For contingency table analysis, this wasrecoded as flat and slight/steep hill. The analysis reveals that flatsegments are found significantly more frequently than expected

Fig. 5. Electronic version of PEDS.

Fig. 6. Low volume street segment (left) and high volume street segment (right).

Table 4Uses in segment: contingency table.

Uses in segment

Single use Mixed uses Total

Positive spatial filter (Quantile 3) Count 85 90 175

Expected 108.7 66.3

Negative spatial filter (Quantile 1) Count 128 40 168

Expected 104.3 63.7

Total 213 130 343

Table 5Segment intersections: contingency table.

Intersection type

other 4-way Total


Expected 67.9 107.1


Expected 65.1 102.9

Total 133 210 343


(128 cases versus 114.3 expected) in DAs with positive values ofthe spatial filter. Contrariwise, slight or steep hill segments areobserved more frequently than expected (72 cases versus 58.3expected) were found in the DAs with negative value of spatialfilter (lower than estimated shares of walking).

The attribute of segment intersections was audited as six differenttypes of intersection (e.g. 3-way, 4-way, or dead-ends.). This attributewas then reclassified for analysis as 4-way and other types ofintersection. Segments with 4-way intersection act as a proxy forthe grid pattern of development which is more likely to be walkablethan other types of intersection as they provide direct routes todestinations (Ewing, 1996; Frank and Engelke, 2001; Frank et al.,2003; Handy et al., 2002; Southworth and Owens, 1993). Table 5shows the results of the independence test. Four-way intersectionsare observed in the DAs with positive filters significantly morefrequently than expected, whereas DAs with negative filters havepredominantly other types of intersections.

Sidewalk completeness and connectivity with other sidewalksare important to facilitate pedestrian traffic. Both of theseattributes are significant in the independence test. Table 6 showsthe results of the test. DAs with positive values of the filter exceedtheir expected number of completed sidewalks with 3 or more

connections, whereas DAs with negative filter have a significantlygreater number of incomplete/missing sidewalks with less than3 connections.

Presence of traffic control devices (TCD), crossing aids, andbicycle facilities are important road characteristics for activecommuters using sidewalks along the roadway. These attributeswere audited with a checklist and reclassified during analysis asnone (none of the options from their respective checklists isfound) and present (one or more options are found). All theseattributes are significant in the independence test. The contin-gency table for these three items is shown in Table 7. From thistable it can be seen that presence of TCD, crossing aids, andbicycle facilities is prevalent in DAs with positive values of thespatial filter, but they are more commonly absent in DAs withnegative values of the spatial filter.

Two indicators from the walking environment category ofPEDS are amenities and number of trees shading the walkingarea. The item amenities had a checklist of five options duringauditing (e.g. public garbage cans, benches, or water fountain)and it was recorded as present (presence of one or moreamenities) and none (no amenities). On the other hand, the itemnumber of trees was audited as none or very few (less than 25%covered), some (25–75% covered), and dense (more than 75%covered). Due to the low frequency of segments with many trees,

Table 7TCD, Crossing aids and Bicycle facilities: contingency table.

TCD Crossing aids Bicycle facilities

None Present None Present None Present Total

Positive spatial filter (Quantile 3) Count 17 158 48 127 148 27 175n3

Expected 24.5 150.5 62.2 112.8 159.2 15.8

Negative spatial filter (Quantile 1) Count 31 137 74 94 164 4 168n3

Expected 23.5 144.5 59.8 108.2 152.8 15.2

Total 48 295 122 221 312 31 343n3

Table 8Amenities and Trees shedding walking area: contingency table.

Amenities Trees shedding walking area

None Present None or very few Some or dense Total

Positive spatial filter (Quantile 3) Count 132 31 78 85 163n2

Expected 139.1 23.9 96.6 66.4

Negative spatial filter (Quantile 1) Count 136 15 108 43 151n2

Expected 128.9 22.1 89.4 61.6

Total 268 46 186 128 314n2

Table 9Building setbacks and Building height: contingency table.

Building setbacks Building height

At edge/ within 20’ More than 20’ Short Medium/ tall Total


Expected 105.1 69.9 46.4 128.6


Expected 100.9 67.1 44.6 123.4

Total 206 137 91 252 343n2

Table 6Sidewalk completeness and Sidewalk connectivity: contingency table.

Sidewalk completeness Sidewalk connectivity

Incomplete or missing Complete Less than 3 3 or more Total


Expected 22.4 152.6 119.4 55.6


Expected 21.6 146.4 114.6 53.4

Total 44 299 234 109 343n2


this item was recoded into two classes: none or very few, andsome or dense. The results of the analysis are shown in theTable 8. As seen there, areas where shares of walking are over-predicted by the model tend to have more segments with absenceof amenities and none or very few trees shedding walking area,whereas presence of amenities and some or dense trees sheddingwalking area are significantly more common sight in the DAswhere shares of walking are higher than predicted by the model.

Two items, size of setbacks and building height, are indicatorsof compact development. A setback is the separation between thebuilding and the sidewalk. After auditing in three categories(i.e., at edge, within 200, and more than 200 from sidewalk), theitem was reclassified as at edge/ within 200, and more than 200.Similarly, building height was recoded as short, and medium/tallfrom original classes i.e. short (1–2 storied), medium (3–5), andtall (6 or more). The contingency tables for these two items areshown in Table 9. As seen in the tables, DAs with positive spatialfilters tend to be more compact, both in terms of the frequency

of segments with smaller setbacks and medium/tall buildings.DAs with negative filters (i.e. lower than expected use of transit)display the opposite tendency, towards segments with shortbuildings and larger setbacks.

The attributes concerning land uses, compactness, segmentintersections, sidewalk completeness and connectivity, TCD andcrossing aids, and overall cleanliness and building maintenanceare illustrated in Figs. 7 and 8. Fig. 7 displays two examples ofsegments with mixed residential and commercial land uses,buildings that in general have smaller setbacks and medium risestructures, with highly connected sidewalk, presence of TCD,crossing aids in the roadway. According to the analysis of auditdata, these are all attributes associated with higher walk sharesthan predicted by the model. Fig. 8 in contrast shows twoexamples of segments characterized by a single land use withless compactness. In addition to the monotonous streetscape interms of uses, these segments have much larger setbacks and theyare consistently low-rise along the segment with missing or less

Fig. 7. Examples of street segments with mixed land uses (residential and commercial), small setbacks and medium rise buildings, high sidewalk connectivity, presence

of TCD, and crossing aids.

Fig. 8. Examples of street segments with single land use, large setbacks and low-rise buildings, missing sidewalk or no connectivity of sidewalk with no crossing aids.


connected sidewalks and absence of crossing aids. These are,according to the analysis of the contingency tables, attributesobserved with significantly higher frequency in DAs with lowershares of walking compared to the shares predicted by the model.

While all the previous items align well with our prior expecta-tions about walkable environments, one significant attribute(overall cleanliness and building maintenance) provides a some-what counter-intuitive result. Overall cleanliness and buildingmaintenance were coded as poor/fair (much or some litter/graffiti/broken facilities), and good (no litter/graffiti/broken facil-ities). Contrary to our expectation that good cleanliness ratingmight increase the attractiveness of a street segment for walking,there were significantly more segments with good cleanlinessrating in DAs where the shares of walking are lower thanexpected, and vice-versa.

In summary, the weight of the evidence tends to favor our initialconjecture, namely, that micro-scale factors in areas where walkingis chosen as a mode of transportation more commonly thanpredicted by the model, would be more supportive of active travel.The overall picture that emerges of an area where walking is morefrequent than expected, corresponds to a pedestrian environmentwith high volume streets, mixed land uses, more compact devel-opment (smaller setbacks and medium/tall buildings), flat streetsegments with 4-way intersections, presence of TCD, crossing aidsand bicycle facilities in the roadway, and complete and connectedsidewalks with amenities and medium/dense trees shedding thewalking area. This is all consistent with previous research indicatingthat mixed land uses and pedestrian-oriented designs reduce cartrips and encourage people to use active mode of transportations,and transit (Cervero and Kockelman, 1997; Cervero et al., 2009;Estupinan and Rodriguez, 2008; Ewing, 1995; Frank and Pivo, 1995;Kitamura et al., 1997; Loutzenheiser, 1997). In contrast, we findthat areas where walking is chosen as a mode less frequentlythan expected, tend to be low volume streets, more segmentscharacterized by single uses, less compact development (greatersetbacks and low-rise buildings), slight/steep hill street segmentswith other type of intersections, no TCD, crossing aids and bicycle

facilities in the roadway, and missing or incomplete and lessconnected sidewalks with no amenities and none or very few treesshedding the walking area.

The results with respect to overall cleanliness (Table 10), initiallyseem counter-intuitive, but ultimately, we would argue, are notunreasonable due to possible correlations with other factors. Forinstance, segments with mixed uses, which according to ouranalysis and previous research are more supportive of walking,tend to attract more people and other forms of traffic as well, suchas commercial vehicles loading or unloading goods. This mayplausibly reduce the overall cleanliness of the street segmentsand/or their sidewalks. Single land use segments, in particularresidential, may be pristine, but as suggested by the analysis, alsoless conducive for walking. Interestingly, Leyden et al. (2011) findthat people in urban settings are willing to trade off some untidi-ness for other benefits of good urban living (p. 880). Thus, a finalcaveat is in order. Correlation between attributes (negative in thiscase) is more appropriately seen as indicative of preferable attri-butes (mixed land uses over cleanliness), than a path to explaincausality (cleaner streets leading to less walking).

6. Summary and conclusions

In this paper we proposed a novel approach to systematicallyinvestigate the micro-scale factors that can influence walking.Our approach is based on the conjecture that the unexplainedportion of a meso-scale model of travel behavior can be partiallyattributed to missing attributes at the micro-scale level. Exam-ination of the spatial pattern of the unexplained component of themodel can assist in the selection of sites for conducting walk-ability audits.

A case study was presented to provide a proof of principle ofthe proposed approach. Based on a model of walk shares at the DAlevel in the City of Hamilton, twenty DAs (out of a total of549 DAs) were selected, based on the examination of the spatialfilter retrieved for the model. Walkability audits of these DAs

Table 10Overall cleanliness and building maintenance: contingency table.

Overall cleanliness and building maintenance

Poor or fair Good Total


Expected 115.3 59.7


Expected 110.7 57.3

Total 226 117 343


were conducted using PEDS, covering a total of 343 segments.Audit data were summarized in contingency tables and analyzedusing chi-squared of independence. Our conjecture is that thepatterns of over- and under-estimation of model predictions canbe related to micro-scale environments is supported by theanalysis of walk shares and the attributes of the pedestrianenvironment in neighborhoods across the city of Hamilton.

Substantively, we found that DAs where walking shares werehigher than predicted tend to have more active, diverse, andcompact street segments. This helps to identify pedestrian-friendly attributes of the environment. These attributes seem tobe accompanied by complete and highly connected sidewalks withcrossing aids, presence of medium/dense trees with amenities alongthe sidewalks, flat street segments with 4-way intersections, pre-sence of traffic control devices and bicycle facilities in the roadway,and lower overall cleanliness and building maintenance ratings. Incontrast, DAs with lower than expected shares of walk tend to bemore homogeneous and less compact with less pedestrian-friendlyattributes such as, incomplete and less connected sidewalks withno crossing aids, none or fewer number of trees with no amenitiesalong the sidewalks, slight/steep hill segments with other than4-way intersections, absence of traffic control devices and bicyclefacilities in the roadway, and higher overall cleanliness and buildingmaintenance ratings.

The method proposed in this paper has a number of attrac-tive features. First, the approach is broadly applicable in manycontexts. Modal split models can be estimated using widely avail-able data, such as journey-to-work data from the Census of Canadaand the Census Transportation Planning Package in the US, andtravel surveys that exist in many cities worldwide.

Secondly, our approach allows for sites to be selected aftercontrolling for an arbitrary number of confounding factors in themodel. The unexplained part can then reasonably be assumed tobe independent of any variables included in the model. The use ofa spatial filtering approach, moreover, means that the pattern isundisturbed by genuinely random variation.

Finally, the case study provides persuasive evidence that theproposed model-based selection strategy can be used to bettertarget limited resources for field-based work, and that it is capableof producing valuable insights into the micro-scale factors thataffect travel behavior. As the case study shows, the method allowedus to identify 343 street segments for field work. This is but afraction of the more than 15,000 street segments in the city. At avery modest cost of approximately CAD 3,000, our approach wasable to generate an intuitive and statistically supported picture ofthe factors that associate with more or less walking in Hamilton.

It is important to note at least one possible limitation of ourapproach. The meso-scale model used in our case study was ofwalking shares for the journey to work, and therefore was specificto this mode of transportation and population segment. Thespecific origin of the spatial filter used to select walkability auditsites could well limit the generality of the findings. It is reason-able to think that some of the pedestrian scale built environmentattributes that an individual may consider important might be

different for someone walking towards his/her working place,compared to someone walking to a bus stop, a person walking forleisure, or a household considering whether to buy a vehicle. Anintriguing avenue for extending this line of inquiry therefore is touse models for different travel behaviors (e.g. walking for leisure,auto ownership) complemented by field research (e.g. environ-mental audits), in order to assess whether some features of themicro-scale built environment influence specifics forms of mobi-lity, or on the contrary, whether they affect travel behavior moregenerally.

There are at least two other ways in which this research couldbe expanded. The method was implemented using a model ofmodal shares. In many instances, it is possible to analyze insteadindividual-level outcomes (e.g. mode choice). This is usually donebased on the implementation of discrete choice models, which donot lend themselves well to analysis of residuals as discussed in thispaper. A possibility, on the other hand, would be to apply the samelogic regarding over- and under-estimation, in the implementationof methods used in the comparison of thematic maps (Ruiz et al.,2010; Ruiz et al., 2012). Finally, the methodology proposed herecould also be applied to different contexts, for instance other cities,or to specific population segments (e.g. children or seniors). This is amatter for future research.

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A model-based approach to select case sites for walkability audits