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https://doi.org/10.1177/0739456X16688767
Journal of Planning Education and Research
1 –16
© The Author(s) 2017
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DOI: 10.1177/0739456X16688767
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Research-Based Article
Introduction
Peter Newman and Jeffrey Kenworthy, in their classic book
Cities and Automobile Dependence and in subsequent publi-
cations, popularized the idea that per capita automobile
usage drops off exponentially with rising population density.
Their original curve, showing gasoline use per capita versus
gross population density (GPD), is one of the iconic images
of the urban planning field. At one extreme is Houston; at the
other is Hong Kong. Data points lie so close to a negative
exponential curve that it seems to represent a universal truth
about cities.
Newman and Kenworthy’s work has been widely adopted,
with thousands of citations in professional and academic
reports. A recent Google search on the terms “newman ken-
worthy density” turned up nearly thirty thousand hits, with
references to Newman and Kenworthy’s density curve
appearing in books, planning policy guidelines, and other
practice-oriented publications. For example, in their book
The Ecology of Place, Beatley and Manning present popula-
tion density as an important factor determining the sustain-
ability of urban areas (Beatley and Manning 1997). On the
policy front, the UK Commission for Integrated Transport’s
report Planning for Sustainable Travel, which updates UK
policy makers on planning research, includes a lengthy sec-
tion on the relationship between population density and auto-
mobile travel and refers directly to Newman and Kenworthy’s
“pioneering” studies (Hickman et al. 2009). Similarly, a
United Nations Environmental Programme guide to carbon
neutrality reproduces Newman and Kenworthy’s population
density/energy consumption graph directly in its report
(Kirby 2008).
This study tests four hypotheses related to the work of
Newman and Kenworthy using multivariate analysis and
data for 157 large US urbanized areas. First, based on our
sample, we find that there is much more scatter around a
best-fit curve than their original work suggests, and that den-
sity explains only a small fraction of the variation in per
capita vehicle miles traveled (VMT). Second, we find that
density continues to be significant when control variables
such as per capita income and average fuel price are added to
a multiple regression model, but the significance and effect
size of density drop sharply. The addition of control variables
greatly improves the explanatory power of the model. Third,
we find that a more complete metric than density, a compact-
ness/sprawl index widely used in the planning literature and
measured by four factors—density, mixed use, degree of
centering, and street connectivity—has a stronger relation-
ship to per capita VMT than GPD alone. However, the differ-
ence is not great, and it is the density component of this
more complete metric that accounts almost entirely for the
688767JPEXXX10.1177/0739456X16688767Journal of Planning Education and ResearchEwing et al.
research-article2017
Initial submission, February 2016; revised submissions, August 2016,
November 2016; final acceptance, November 2016
1City and Metropolitan Planning, University of Utah, Salt Lake City, UT,
USA
2Institute of Urban Studies, University of Texas, Arlington, TX, USA
3Department of Design and Planning in Complex Environments, University
Iuav, Venice, Veneto, Italy
Corresponding Author:
Reid Ewing, City and Metropolitan Planning, University of Utah, 375 South
1530 East, Salt Lake City, UT 84112, USA.
Email: ewing@arch.utah.edu
Testing Newman and Kenworthy’s
Theory of Density and Automobile
Dependence
Reid Ewing1, Shima Hamidi2, Guang Tian1, David Proffitt1,
Stefania Tonin3, and Laura Fregolent3
Abstract
This study tests four hypotheses related to the much-cited work on density and automobile dependence by Newman
and Kenworthy, using multivariate analysis and data for 157 large US urbanized areas. We find that density alone explains
only a small fraction of the variation in vehicle miles traveled (VMT), and many confounders account for the differences in
automobile dependence. We also find that it is not the localized density of individual neighborhoods that causes VMT to be
lower in compact urbanized areas but rather the relative accessibility of neighborhoods to the rest of the region.
Keywords
auto dependence, VMT, density, sprawl index
2 Journal of Planning Education and Research
relationship to per capita VMT. Finally, we find that relation-
ships between the built environment and VMT are different
in aggregate (metropolitan-level) studies such as this one, as
compared to disaggregate (household-level) studies that
mostly populate the literature. In particular, the importance
of density, as a built environmental measure, differs. We dis-
cuss why this may be the case.
Literature Review
There are rich literatures relating VMT to density, the built
environment generally, highway capacity, the real price of
fuel, and transit service. Peter Newman and Jeffrey
Kenworthy were the first to explore the relationship between
VMT (and proxies for it) and density. We will review their
work. The literatures on the second through fourth topics
(built environment, highway capacity, and fuel price) are so
extensive we will limit this review to meta-analyses. Unlike
traditional research methods, meta-analyses use summary
statistics from individual primary studies as the data points in
a new analysis. The last topic, the relationship between tran-
sit service and VMT, is the most recent addition to the litera-
ture, and that too will be explored.
Density and VMT
Newman and Kenworthy’s Cities and Automobile
Dependence (1989a) is one of the most influential planning
books of all time. In it and related papers (Newman and
Kenworthy 1989b; Newman and Kenworthy 2006; Newman
and Kenworthy 2011a, 2011b; Newman 2014), the authors
suggest that in world cities (actually metropolitan areas), per
capita fuel use is inversely related to GPD (see Figure 1).
The relationship follows an exponential function.
More recently, Kenworthy et al. (1999) and Newman
(2014) reproduced this graph for a greatly expanded set of
world cities (see Figure 2). Data points again lie very close to
a best-fit curve.
Newman and Kenworthy’s work has been criticized for
stating the obvious (that car use per capita and density will
always be hyperbolic since population is in the denominator
of one and numerator of the other) and for ignoring other
variables that affect fuel use (population size and income, for
example) (Dujardin et al. 2012; Gordon and Richardson
1989; Perumal and Timmons 2015). Their analyses were
bivariate rather than multivariate (Dujardin et al. 2012).
Other criticisms include the possible incomparability of the
different countries studied. Perumal and Timmons (2015)
argue that compared to the US cities, Hong Kong has very
high population density and very low automobile usage, yet
the differences between Hong Kong and Houston likely go
far beyond density.
They (actually Kenworthy and Laube 1999) have subse-
quently shown that car use itself (in per capita vehicle kilo-
meters traveled) is inversely related to density (in persons
per hectare). In the same article, they also looked at other
simple correlations (see Figures 3 through 5).
In their most recent work, The End of Automobile
Dependence: How Cities Are Moving beyond Car-Based
Planning (Newman and Kenworthy 2015), Newman and
Kenworthy retreat ever so slightly from their previous focus
on density as the solution to automobile dependence. They
have a section titled “Is Increasing Density Enough to End
Automobile Dependence?” which hints at a broader perspec-
tive. However, this section adds only one variable to the sus-
tainability equation: transit service, and even this variable is
tied to density. To quote, “In response to the question of
whether increased density alone is enough, we say that pub-
lic transit improvements are also needed–but the two go
together, they are totally intertwined” (174). They then pro-
ceed to “debunk” ten supposed “myths” about density, com-
pleting their case for density as a “sustainability multiplier”
(174–87). In terms of statistics, they assert that for their sam-
ple of fifty-eight cities (actually metropolitan areas), urban
density alone accounts for 84 percent of the variance in car
use per person.
Built Environment and VMT
In travel research, urban development patterns have come to
be characterized by “D” variables. The original “three Ds,”
coined by Cervero and Kockelman (1997), are density, diver-
sity, and design. The Ds have multiplied since then, with the
addition of destination accessibility and distance to transit
(Ewing and Cervero 2001, 2010). While not part of the envi-
ronment, demographics are another D in travel studies, con-
trolled as confounding influences.
A recent meta-analysis uncovered more than two hundred
studies of the built environment and travel (Ewing and
Cervero 2010). Of these, sixty-two studies yielded usable
outcome measures from which to compute weighted-average
elasticities. An elasticity is a measure of effect size equal to
the percentage change in an outcome variable (such as VMT
per capita) with respect to a 1 percent increase in an explana-
tory variable (such as density). In this analysis, the D vari-
able that is most strongly associated with VMT is destination
accessibility. In fact, the -0.19 VMT elasticity is nearly as
large as the elasticities of the first three D variables—density,
diversity, and design—combined.
The variables next-most strongly associated with VMT
are design metrics expressed in terms of intersection density
or street connectivity. The elasticities of these two street-
network variables are fairly similar. Both short blocks and
frequent intersections shorten travel distances, apparently to
about the same extent. Surprisingly, population density is
weakly associated with travel behavior once these other vari-
ables are controlled. In an effort to explain the much higher
elasticities reported in the literature, the article notes: “The
relatively weak relationships between density and travel
likely indicate that density is an intermediate variable that is
Ewing et al. 3
often expressed by the other Ds (i.e., dense settings com-
monly have mixed uses, short blocks, and central locations,
all of which shorten trips and encourage walking)” (12).
The studies referenced above use disaggregate data
(household-level data) to explore relationships between the
built environment around households (the Ds) and household
travel outcomes. There is a whole different literature that
tests for relationships using aggregate data. The two litera-
tures have developed somewhat independently. These aggre-
gate studies posit relationships between urban form, often
measured by “sprawl indices,” and average travel outcomes
for large areas such as counties, metropolitan areas, or urban-
ized areas. Because of omitted variables, aggregation bias,
the ecological fallacy, and geographic scale, these studies
could logically provide different results.
The built environment in the urban form studies is also
represented by D variables, but with different names given
to the different Ds. Development density remains as den-
sity, but land use diversity is described as land use mix, and
street network design is described as street connectivity.
The other two Ds, most importantly, destination accessibil-
ity, do not factor into sprawl indices. And, of course, the
Figure 1. Gasoline use per capita versus population density, 1980.
Source: Newman and Kenworthy (1989a), 128.
4 Journal of Planning Education and Research
geographic scale is all different. In the disaggregate studies,
it is the neighborhood built environment that is represented
by the Ds. In the aggregate studies, it is the extent of sprawl
in the county, metropolitan area, or urbanized area as a
whole that is measured.
Early attempts to measure the extent of urban sprawl
focused on density (Pendall 1999; Fulton et al. 2001; Lopez
and Hynes 2003; Anthony 2004; Lang 2003; Pendall and
Carruthers 2003). Density was the primary indicator of
sprawl in the early studies likely because it is easy to mea-
sure, and captures one important dimension of sprawl. The
most notable feature of early studies was the failure to define
sprawl in all its complexity.
Most scholars now agree that sprawl is a multidimensional
phenomenon that is best quantified by a combination of mea-
sures (Galster et al. 2001; Ewing, Pendall, and Chen 2002;
Cutsinger et al. 2005; Frenkel and Ashkenazi 2008; Jaeger
et al. 2010; Mubareka et al. 2011; Torrens 2008). The most
widely used compactness/sprawl metrics are those of Ewing,
Pendall, and Chen (2002), updated in Ewing and Hamidi
(2014a). Compactness indices have now been developed for
metropolitan areas (Hamidi et al. 2015), census urbanized
areas (Hamidi and Ewing 2014), and metropolitan counties
(Ewing, Hamidi, and Grace 2016; Ewing et al. 2014b).
The approach used in these studies is the same. First, using
principal components analysis (PCA), they estimate factor
Figure 2. Per capita private passenger transport energy use and urban density in global cities.
Source: Newman (2014).
Figure 3. Urban density versus car use in developed and
developing cities, 1990.
Source: Kenworthy and Laube (1999).
Figure 4. Gross regional product per capita versus car use per
capita in developed cities, 1990.
Source: Kenworthy and Laube (1999).
Ewing et al. 5
scores for four dimensions of urban form: development den-
sity, land use mix, activity centering, and street connectivity.
They then sum the four scores, regress the result on the natu-
ral logarithm of population, and use the standardized residu-
als to compute overall compactness/sprawl indices for the
areas in their sample. The indices are standardized with a
mean of 100 and a standard deviation of 25. The resulting
indices are independent of population. Thus, the degree of
sprawl for any given metropolitan or urbanized area is judged
relative of other areas of the same size. It makes little sense to
compare the degree of sprawl in the New York City urbanized
area, with a population of more than eighteen million, to
places such as the Ithaca, NY, urbanized area, with a popula-
tion of just over fifty thousand.
Both the individual factors and overall index are then
validated against transportation outcomes and other qual-
ity-of-life measures. These compactness/sprawl indices
have been widely used in health and other research. The
indices have been related to traffic fatalities (Ewing,
Pendall, and Chen 2003; Ewing and Hamidi 2015; Ewing,
Hamidi, and Grace 2016a), physical inactivity, obesity,
heart disease, cancer prevalence (Berrigan et al. 2014;
Cho et al. 2006; Doyle et al. 2006; Ewing, Pendall, and
Chen 2003; Ewing et al. 2014b; Fan and Song 2009;
Griffin et al. 2012; Joshu et al. 2008; Kelly-Schwartz
et al. 2004; Kim et al. 2006; Kostova 2011; Lee, Ewing,
and Sesso 2009; Plantinga and Bernell 2007), air pollution
(Ewing, Pendall, and Chen 2002; Schweitzer and Zhou
2010; Stone 2008), extreme heat events (Stone, Hess, and
Frumkin 2010), residential energy use (Ewing and Rong
2008), social capital (Nguyen 2010), emergency response
times (Trowbridge, Gurka, and O’Connor 2009), teenage
driving (Trowbridge and McDonald 2008), private-vehi-
cle commute distances and times (Ewing, Pendall, and
Chen 2003; Hamidi et al. 2015; Hamidi and Ewing 2014;
Holcombe and Williams 2012; Zolnik 2011), housing
plus transportation costs (Hamidi and Ewing 2015),
and economic and social upward mobility (Ewing et al.
2016). While most studies have linked sprawl to negative
outcomes, there have been exceptions (see, in particular,
Holcombe and Williams 2012).
In a recent study using updated indices, the elasticity of
VMT with respect of a county compactness index was esti-
mated to be −0.78 (Ewing, Hamidi, and Grace 2016). This
elasticity is considerably higher (in absolute magnitude) than
the elasticity of VMT with respect to density alone.
Highway Capacity and VMT
Based on a meta-analysis of the VMT inducing effects of
highway expansion, Cervero (2002) concludes, “the prepon-
derance of research suggests that induced-demand effects are
significant, with an appreciable share of added capacity
being absorbed by increases in traffic, with a few notable
exceptions.”
In the short run, a variety of sources contribute to
increased traffic without any highway-induced develop-
ment. These include changes in route, mode, time of travel,
and destination. In addition, there is the possibility of new
trips that would not have occurred without the new infra-
structure capacity. In the long run, increases in highway
capacity may improve accessibility to developable lands
and lower travel times to the point where residences and
businesses are drawn to locate near the expanded highway
capacity (Ewing 2008). Cervero (2002) computes a long-
run elasticity of VMT with respect to highway capacity of
between 0.63 and 0.73.
Fuel Prices and VMT
The meta-analytical literature on VMT growth with respect to
the real price of fuel is sparse. The primary work in the area is
Graham and Glaister’s (2004) review of more than fifty stud-
ies measuring the fuel price elasticities for car trips and car
kilometers within European Union countries. Looking at both
short-term (less than 1 year) and long-term effects, the
researchers found that the unweighted mean short-run elas-
ticities for trips and kilometers across the studies were roughly
equivalent at −0.16. Over time, however, the two measures
diverged, with trips decreasing only slightly to −0.19, but
kilometers dipping substantially to −0.31. A parallel study by
Goodwin, Dargay, and Hanly (2004) summarizing sixty-nine
studies from Europe and North America came to similar con-
clusions, with a mean short-term vehicle-kilometer elasticity
of −0.10 and a long-term elasticity of −0.29.
Meta-analysis studies of gasoline demand versus price
are more numerous, and given that gasoline demand is a
rough proxy for VMT, particularly in the short run, this lit-
erature sheds light on the fuel price–VMT relationship. One
meta-analytic study derived a long-run mean price elastic-
ity of gasoline demand of −0.53 (Brons et al. 2006). Another
meta-analysis of gasoline price elasticities based on hun-
dreds of studies across the globe found a mean short-run
elasticity of −0.23 and a mean long-run elasticity of −0.58
(Espey 1998). The second study concludes with
Figure 5. Cost of cars versus car use in developed cities, 1990.
Source: Kenworthy and Laube (1999).
6 Journal of Planning Education and Research
this relevant thought: “The finding of different elasticity
estimates using data prior to 1974 and data after 1974 sug-
gests the need for updated studies and for care to be taken
in extrapolating into the future using elasticity estimates
from the 1970s or even the 1980s.”
In an oft-cited recent study, which overcomes some of the
methodological limitations of earlier studies, Small and Van
Dender (2007) observed a low (under −0.10) short-run price
elasticity of gasoline demand. But importantly, they found
gasoline’s long-run price elasticity to be much higher,
approximately −0.43. In addition, they found that the elastic-
ity of VMT with respect to fuel cost per mile (controlling for
increased vehicle fuel efficiency) was roughly half the price
elasticity of gasoline demand.
Transit Service and VMT
Historically, research examining the role of public transit in
reducing VMT has focused directly on mode shifts from
driving to transit occurring as a result of transit investments.
Such research typically shows only modest reductions in
vehicle travel. However, a growing body of research sug-
gests that cities with comprehensive transit facilities achieve
more efficient use of their transportation systems that is not
fully captured by mode shifts from driving to transit. This
concept, commonly referred to as transit leverage, or the land
use multiplier effect, states that one mile traveled on transit
corresponds to a disproportionately higher reduction in auto-
mobile travel. The multiplier is typically expressed as VMT
reduced per passenger-mile of transit or as a multiplier of the
mode shift effects of transit.
In other words, the influences of transit—including
more compact and mixed land uses in station areas, a
higher propensity by users to chain trips, reduced traffic
congestion, and a significantly higher rate of related non-
motorized travel (walk and bike trips)—converge to reduce
automobile travel to a greater degree than simply the dis-
tance traveled via transit. Even those who live near transit
but do not utilize it may drive less because of the compact,
mixed-use neighborhoods and opportunities to walk and
bike fostered by transit.
The mechanism by which transit leverages larger
reductions in VMT is straightforward: Transit creates
opportunities for transit-oriented development (TOD),
“compact, mixed-use development near transit facilities
with high-quality walking environments” (TCRP Report
102), which by definition combines all of the D variables.
However, researchers have yet to reach a consensus on the
magnitude of the land-use multiplier effect. Studies,
which draw on data from different cities and use different
methods, have produced estimates for the land use multi-
plier ranging from 1.29 to 9. Estimates of the land use
multiplier can even vary widely within a given study. A
recent study pegged the multiplier at about 3.0 (Ewing
and Hamidi 2014b).
Multivariate Analyses
Unlike the studies described above, which focus on one cor-
relate of VMT at a time, another class of studies seeks to
estimate elasticities of VMT with respect to relevant vari-
ables in a multivariate context. This article does as well.
The book Growing Cooler (Ewing et al. 2008) asked and
attempted to answer the question: How does compact develop-
ment affect VMT and associated greenhouse gas emissions
that contribute to global warming? Using structural equation
modeling and both cross-sectional and longitudinal data for
eighty-four large US urbanized areas, chapter 8 estimated
elasticities of VMT with respect to population, real per capita
income, population density, highway lane miles, transit reve-
nue miles, transit passenger miles, and the real price of fuel
(see Table 1). Table 1 suggests, for example, that a 1 percent
increase in density will bring about a 0.3 percent drop in VMT.
More recently, Cervero and Murakami (2010) similarly
used structural equation modeling, plus cross-sectional data
from 370 US urbanized areas, to estimate elasticities of per
capita VMT with respect to household income, population
density, road density, rail density, and other land use vari-
ables related to density and accessibility. Their results are
presented in Table 2. They are generally consistent with
the results of Ewing et al. (2008), though the elasticity of
roadway density is smaller and the elasticity of population
Table 1. Elasticities of Vehicle Miles Traveled with Respect to
Urban Variables.
Cross-Sectional
Analysis
Longitudinal
Analysis
Best
Estimate
Population 0.97 0.874 0.95
Real per capita income 0.531 0.538 0.54
Population density −0.213 −0.152 −0.30
Highway lane miles 0.463 0.684 0.55
Transit revenue miles −0.075 −0.023 −0.06
Transit passenger miles −0.068 −0.03 −0.06
Heavy-rail miles −0.013 −0.021 −0.01
Light-rail miles −0.003 −0.002 NA
Real fuel price NA −0.171 −0.17
Source: Ewing et al. (2008).
Table 2. Elasticities of Per Capita Vehicle Miles Traveled with
Respect to Urban Variables.
Estimate
Household income 0.21
Population density −0.38
Roadway density 0.42
Rail density −0.003
Urbanized area 0.02
% commuting by auto 0.60
Source: Cervero and Murakami (2010).
Ewing et al. 7
density is larger. A 1 percent increase in density would be
expected to bring about a 0.38 reduction in per capita VMT.
Most recently, Ewing et al. (2014a) used structural equa-
tion modeling and cross-sectional data for 315 urbanized
areas to estimate refined elasticities of per capita VMT with
respect to population, household income, population density,
freeway and arterial lane miles per 1,000 population, transit
passenger miles per capita, average fuel price, and other vari-
ables. Their results are presented in Table 3. Their results are
generally consistent with the earlier estimates. A 1 percent
increase in density would be expected to bring about a −0.238
percent decline in per capita VMT.
Hypotheses
This study reanalyzes Newman and Kenworthy’s view of the
relationship between the built environment and VMT using
the data of Ewing et al. (2014a). We will test four hypotheses
based on Newman and Kenworthy’s work:
Hypothesis 1: That GPD (density in persons per square mile)
bears a simple, smooth inverse relationship to per capita
VMT for urbanized areas in the United States. The alternate
hypothesis is that the relationship is not nearly so tightly fit
when these two variables are measured independently and,
in fact, has a high degree of scatter around a best-fit curve.
Hypothesis 2: That, when confounding variables are con-
trolled, the relationship between GPD and per capita VMT
continues to be strong and negative. The alternate hypothesis
is that the relationship between GPD and per capita VMT is
weakened to the point where it is no longer statistically sig-
nificant when confounding variables are controlled.
Hypothesis 3: That a more complete measure of urban
compactness/sprawl than GPD bears a similar inverse
relationship to per capita VMT. The alternate hypothesis
is that a more complete measure of compactness/sprawl,
which accounts for more aspects of land use and street
design, actually has a stronger relationship to per capita
VMT than does GPD.
Hypothesis 4: That the relationship between density and
per capita VMT is the same for urban form studies using
aggregate (metropolitan level) data, such as Newman and
Kenworthy’s, and the more numerous travel behavior
studies using disaggregate (household level) data. The
alternate hypothesis is that density takes on dispropor-
tionate importance in aggregate studies that fail to account
for all D variables and measure the built environment at
the large scale of the metropolitan area rather than the
small scale of the neighborhood.
Methodology
Research Design
In this study, cross-sectional models for built environment and
VMT were estimated to capture the long-run relationships
between transportation and land use at a point in time, 2010.
Each urbanized area has had decades to arrive at quasi-equilib-
rium among land use patterns, road capacity, transit capacity,
and VMT.
Method of Analysis
Unlike the earlier study by Ewing et al. (2014a), which used
structural equation modeling to explain the relationship
between the built environment and VMT, this study uses
ordinary least squares (OLS) regression, which is consistent
with Newman and Kenworthy’s approach. Density and
(later) compactness are treated as exogenous influences on
per capita VMT. In this manner, we are able to tease out the
relative influence of density and compactness on per capita
VMT, controlling for other correlates of VMT.
We also used PCA to create compactness indices for the
157 large Federal Highway Administration (FHWA) urban-
ized areas in our sample. We followed the same procedures
as Hamidi and Ewing (2014) but applied them to FHWA-
approved urbanized areas rather than census-designated
urbanized areas.
Data
We gathered data from several primary sources for our cross-
sectional analysis. For the sake of consistency, the boundar-
ies used to compute explanatory variables had to be the same
as the boundaries used to estimate our dependent variable,
per capita VMT from FHWA’s Highway Statistics.
The Highway Statistics definition of urbanized area is dif-
ferent from the census definition. According to the FHWA,
“the boundaries of the area shall encompass the entire urban-
ized area as designated by the U.S. Bureau of the Census plus
that adjacent geographical area as agreed upon by local offi-
cials in cooperation with the State.” Cervero and Murakami
Table 3. Direct, Indirect, and Total Effects of Variables on Per
Capita Vehicle Miles Traveled in the Cross-Sectional Model for
2010.
Direct Indirect Total
pop 0.078 −0.025 0.052
inc 0.304 −0.015 0.289
fuel −0.448 −0.175 −0.623
hrt 0 −0.021 −0.021
lrt 0 −0.03 −0.03
flm 0.133 0.026 0.159
olm 0.04 0.131 0.172
popden −0.238 0 −0.238
rtden 0 −0.06 −0.06
tfreq 0 −0.057 −0.057
tpm −0.016 0 −0.016
Source: Ewing et al. (2014a).
8 Journal of Planning Education and Research
(2010) used the census boundaries for their analysis and
deleted urbanized areas from the sample if the census and
FHWA boundaries were hugely different. We chose not to
make such approximations or lose many cases, and therefore
set out to find FHWA-adjusted boundaries for urbanized
areas in a geospatial shapefile format, which we could then
use to conduct spatial analyses in GIS (see Figure 6).
We obtained shapefiles for all fifty states and 443 urban-
ized areas and then combined the individual state files into
one national shapefile by using the “merge” function in GIS.
Many of the urbanized areas cross state boundaries, and in
this case we had more than one polygon for each urbanized
area. So, we used the “dissolve” function in GIS to integrate
those polygons into one for each urbanized area.
After cleaning the data, we did several spatial joins in GIS
to capture data from other sources. For example, we used the
“centroid” function to join 2010 census tracts to FHWA-
adjusted urbanized areas. We then aggregated values of per
capita income for census tracts to obtain urbanized area
weighted averages (weighted by population).
Consistent with Hamidi and Ewing (2014), we limited our
sample to large urbanized areas with populations of two hun-
dred thousand or more for which all variables in Table 4
could be estimated. Of the 173 urbanized areas with popula-
tions of two hundred thousand or more, some cases were lost
for lack of compactness metrics, others for lack of transit
data, and still others for lack of fuel price data. The rationale
for limiting our sample to larger urban areas is that small
areas are different qualitatively than large areas. We wanted
a more homogenous sample. In small areas, land uses are
necessarily reasonably proximate to each other. Hence good
accessibility, which defines compactness, is guaranteed. It is
spurious to compare congestion in a large area like Los
Angeles (population 12.6 million, where trips are long and
congestion is intolerable) to congestion in a small area like
Porterville, CA (population seventy-nine thousand, where
trips are necessarily short and congestion is nonexistent).
Small urbanized areas would be expected to have signifi-
cantly lower per capita VMT than larger urbanized areas.
The Newman and Kenworthy samples consist of the largest
world cities, and we are testing to see if the same dynamics
apply to a set of more typical cities. Our final sample consists
of 157 urbanized areas.
Variables
The variables in our models are defined in Table 4. They are
as follows:
Our dependent variable: per capita VMT;
Our independent variables: The independent variables
of primary interest are GPD and the aforementioned
compactness index. Control variables include popula-
tion size, per capita income, and metropolitan average
fuel price. Variables representing highway capacity
and transit capacity were also treated as exogenous, as
they are the result of long-lived policy decisions to
invest in highways or transit.
All variables were transformed by taking natural logarithms.
The use of logarithms has two advantages. First, it makes
relationships among our variables more nearly linear and
reduces the influence of outliers (such as New York and Los
Angeles). Second, it allows us to interpret parameter esti-
mates as elasticities, which summarize relationships in an
understandable and transferable form.
Results
Compactness Indices
The factor loadings from the PCA are shown in Table 5. Five
variables load on a density factor, two variables on a mixed
use factor, four variables on a centering factor, and four vari-
ables on a street factor. Using the factor loadings, factor
scores were computed for each of the 157 urbanized areas in
our sample. Factor scores were then standardized on scale
with a mean of 100 and a standard deviation of 25 (see
Hamidi and Ewing 2014). This produced a single density
factor, mixed use factor, centering factor, and street factor for
each of the urbanized areas. The four were summed and
Figure 6. Year 2000 census and Federal Highway Administration
(FHWA)-adjusted urbanized areas boundaries for Atlanta (one of
the most sprawling urbanized area in the United States).
Ewing et al. 9
regressed on the natural logarithm of population, and the
resulting standardized residuals were converted into an index
with a mean of 100 and standard deviation of 25. All urban-
ized areas fall on a continuum with compactness at one end
and sprawl at the other. High values (over 100) correspond to
compact urbanized areas, and low values (under 100) to
sprawling areas. The ten most compact areas and ten most
sprawling urbanized areas are shown in Table 6. These rank-
ings are generally consistent with the rankings for census
urbanized areas, and generally consistent with expectations,
thus achieving face validity.
Hypothesis 1
The alternate hypothesis is supported by this analysis. The
alternate hypothesis is that when vehicle use and density are
measured independently, the relationship is not nearly so
neat as in the curves of Newman and Kenworthy.
Figure 7 is a scatterplot of per capita VMT versus popula-
tion density in persons per square mile for 157 urbanized
areas. While the general pattern of data points looks
exponential, per Newman and Kenworthy, the dominant
impression is of wide variance around the best-fit curve.
The pattern of the data in Figure 7 is nonlinear. If a
power function applies, it should be possible to produce a
linear plot by taking the natural logarithm of each variable
and plotting them against each other. This is done in Figure
8. The plot is now approximately linear. However, there is
still tremendous scatter around the best-fit line. Regressing
the natural log of per capita VMT on the natural log of pop-
ulation density yields the result in Table 7. The R2 is 0.189,
which means that the log of density explains only 19 per-
cent of the variance in the logarithm of per capita VMT.
The coefficient of density in this equation is −0.237. The
coefficient in a log–log regression is just the arc elasticity
of per capita VMT with respect to density. A doubling of
density is associated with approximately a one-quarter
decrease in per capita VMT. Not a huge effect compared
with Newman and Kenworthy’s, but a significant one.
Results are similar when the analysis is limited to the 30
largest urbanized areas, a sample more equivalent to
Newman and Kenworthy’s. In a regression of the natural
log of VMT per capita on the natural log of population den-
sity, the R2 is higher, 0.267, but the elasticity of VMT per
capita with respect to population density is lower, −0.181.
Hypothesis 2
The alternate hypothesis is confirmed by a multivariate
analysis. When confounding variables are controlled, the
relationship between density and per capita VMT remains
significant and negative, but the significance level and
Table 4. Variables Included in the Urbanized Area Model.
Variable Definition Source Mean Standard Deviation
Dependent variable
vmt Natural log of daily VMT per capita FHWA Highway Statistics 3.15 0.23
Independent variables
pop Natural log of population (in thousands) US Census 6.40 0.96
inc Natural log of income per capita (in thousands) American Community
Survey
3.27 0.19
fuel Natural log of average fuel price metropolitan average
fuel price
Oil Price Information
Service
1.02 0.06
flm Natural log of freeway lane miles per 1,000 population FHWA Highway Statistics −0.49 0.42
olm Natural log of other lane miles per 1,000 population FHWA Highway Statistics
NAVTEQ
0.85 0.28
rtden Natural log of transit route density per square mile National Transit Database 0.60 0.75
tfreq Natural log of transit service frequency National Transit Database 8.68 0.55
popden Natural log of gross population density US Census 7.44 0.43
compact Natural log of compactness index Multiple sources—see
Hamidi and Ewing (2014)
4.57 0.25
denfac Natural log of density factor Multiple sources—see
Hamidi and Ewing (2014)
4.58 0.23
mixfac Natural log of mix factor Multiple sources—see
Hamidi and Ewing (2014)
4.57 0.28
cenfac Natural log of centering factor Multiple sources—see
Hamidi and Ewing (2014)
4.58 0.25
strfac Natural log of street factor Multiple sources—see
Hamidi and Ewing (2014)
4.57 0.27
Note: FHWA = Federal Highway Administration; VMT = vehicle miles traveled.
10 Journal of Planning Education and Research
effect size drop. The addition of other relevant variables
boosts the explanatory power of the model from an R2 of
0.189 to an R2 of 0.450 (see Table 8). At the same time, the
effect size of the density variable, measured by the elastic-
ity of VMT per capita with respect to density, drops from
−0.237 to −0.164.
Three of the other variables in the model are highly sig-
nificant: the natural logarithms of urbanized area population,
representing area size; freeway lane miles per 1,000 popula-
tion, representing freeway capacity; and per capita income,
representing area affluence. The average real price of fuel
(gasoline), representing the cost of auto use, has the expected
sign but is only significant at the 0.10 level. Per capita VMT
increases with area size, freeway capacity, and income, and
declines slightly with fuel price, all of which are expected.
Interestingly, the other roadway-supply variable, nonfree-
way lane miles per 1,000 population, and the transit variables
are not significant. Lower-order roads, such as collectors and
local streets, do not appear to induce additional traffic.
Transit supply does not appear to dampen VMT, perhaps
because transit mode shares are small in most urbanized
areas.
Parenthetically, multicollinearity may be an issue in this
regression. The largest variance inflation factor (VIF) is
5.77 for the variable GPD. VIFs between 5.0 and 10.0 are
suspect, and those over 10.0 are generally indicative of mul-
ticollinearity. This is the reason why Ewing et al. (2014a)
used structural equation modeling in their earlier analysis.
VIFs for all other variables are much smaller.
Hypothesis 3
The null hypothesis is that a more complete measure of urban
compactness/sprawl than GPD bears a similar inverse rela-
tionship to per capita VMT. The alternate hypothesis is that a
more complete measure of compactness/sprawl, which
accounts for more aspects of land use and street design, actu-
ally has a stronger relationship to per capita VMT than does
density. On this score, the evidence is mixed.
Table 9 presents the results of a regression of per capita
VMT on the same set of variables as in Table 8, but substi-
tutes the compactness index for GPD. The compactness
index in Table 9 is more significant than gross density in
Table 8, but the difference is not material. Likewise, the
explanatory power of the model in Table 9 (represented by
the R2) is slightly greater than that of the model in Table 8.
Again, the difference is not material. The main advantage
of the new model over the old is in the area of multicol-
linearity. Because the compactness index is independent
of population, as explained above, the largest VIF is now
2.209.
We wondered which of the dimensions of the compact-
ness index accounts for its relationship to per capita VMT.
So we regressed per capita VMT on each of the four com-
pactness factors—density, mixed use, centering, and street
factors—plus control variables. The results are presented in
Table 10. To our surprise, the multivariate density factor (a
more complete measure of density than simple gross den-
sity) is far more significant than Newman and Kenworthy’s
gross density measure, and the more complete measure of
density alone, of the four factors, is statistically significant.
The other factors have the expected signs but do not
approach significance. The elasticity of per capita VMT
with respect to the more complete measure of density is
−0.612. This result can be taken as confirmation of Newman
and Kenworthy’s basic theory, that density, properly mea-
sured, is strongly related to vehicle use, at least at the large
scale of urbanized areas.
Table 5. Factor Loadings on Principal Components That
Comprise the Compactness Index.
Component Matrix
2010 Factor
Loadings
Density factor
popden: gross population density of
urban and suburban census tracts
0.964
empden: gross employment density of
urban and suburban census tracts
0.895
lt1500: percentage of the population
living at low suburban densities
−0.818
gt12500: percentage of the population
living at medium to high urban densities
0.775
Urbden: net population density of urban
lands
0.938
Eigenvalue 3.88
Explained variance 77.6%
Mix use factor
jobpop: job–population balance 0.833
jobmix: degree of job mixing (entropy) 0.833
Eigenvalue 1.39
Explained variance 69.5%
Centering factor
popcen: percentage of urbanized area
population in CBD and/or subcenters
0.780
empcen: percentage of urbanized area
employment in CBD and/or subcenters
0.787
varpop: coefficient of variation in census
block group population densities
0.666
varemp: coefficient of variation in census
block group employment densities
0.668
Eigenvalue 2.12
Explained variance 52.9%
Street factor
smlblk: percentage of small urban blocks
of less than 1/100th of a square mile
0.818
avgblk: average block size −0.930
intden: intersection density 0.793
4way: percentage of 4-or-more-way
intersections
0.703
Eigenvalue 2.66
Explained variance 66.4%
Ewing et al. 11
Hypothesis 4
The alternate hypothesis is confirmed. However it is mea-
sured, density takes on disproportionate significance in
aggregate studies that fail to account for all D variables. In
the meta-analysis of Ewing and Cervero (2010), the
weighted average elasticity of VMT per capita with respect
to population density is only −0.04. At the same time, the
weighted average elasticity of VMT per capita with respect
of regional destination accessibility is −0.19. A recent
update places the elasticity of VMT per capita with respect
to density at −0.15 (Ewing and Cervero, forthcoming).
Table 6. Compactness/Sprawl Scores for 10 Most Compact and 10 Most Sprawling Urbanized Areas in 2010.
Rank
Compactness
Index
Density
Factor
Mix
Factor
Centering
Factor
Street
Factor
Ten most compact urbanized areas
1 San Francisco–Oakland, CA 175.50 190.14 88.90 169.16 148.36
2 Reading, PA 162.19 120.74 128.44 126.47 138.92
3 Eugene, OR 155.08 118.34 128.22 123.68 127.25
4 Madison, WI 154.73 118.70 88.50 186.95 111.97
5 Salem, OR 153.88 123.04 135.33 112.19 123.12
6 Lexington–Fayette, KY 152.04 134.48 123.02 124.22 112.03
7 Huntington, WV-KY-OH 146.87 83.29 129.11 148.69 126.96
8 New York–Newark, NY-NJ-CT 146.62 186.88 75.10 185.54 124.87
9 York, PA 146.17 98.46 138.95 126.74 113.29
10 Allentown, PA-NJ 145.91 108.68 134.48 105.34 149.70
Ten most sprawling urbanized areas
148 Nashville-Davidson, TN 66.05 94.10 64.31 97.93 79.97
149 Cleveland, OH 64.29 99.21 88.55 95.75 64.26
150 Lancaster-Palmdale, CA 63.88 98.34 97.30 54.81 61.05
151 Winston-Salem, NC 63.27 70.82 89.69 89.15 61.51
152 Fayetteville, NC 62.90 80.58 89.21 67.29 69.36
153 Chattanooga, TN-GA 61.63 70.13 67.38 100.48 71.59
154 Atlanta, GA 58.34 87.47 113.62 104.91 49.05
155 Baton Rouge, LA 57.67 74.57 107.36 71.05 57.73
156 Jackson, MS 55.90 63.24 94.84 104.76 36.48
157 Shreveport, LA 45.80 66.36 71.04 68.36 66.43
Figure 7. Daily per capita vehicle miles traveled versus
population density of 157 US urbanized areas (variables not
logged).
Figure 8. Daily per capita vehicle miles traveled versus
population density of 157 US urbanized areas (logged variables).
12 Journal of Planning Education and Research
In the aggregate analysis in Table 8, the elasticity of VMT
per capita with respect to population density is −0.164. It is
even higher, −0.612, using the more complete measure of
density in Table 10. The aggregate analyses, representing
regional urban form strictly in terms of density, fail to
account for the confounding influence of destination acces-
sibility. Other reasons for this important difference are dis-
cussed below.
Discussion and Conclusion
The contribution of Newman and Kenworthy to the planning
field is undeniable. They were among the first to study the
relationship between the built environment and transportation
outcomes. Their work in the late 1980s, and that of Robert
Cervero at about the same time, spurred a whole new area of
academic inquiry. The relationship between the built environ-
ment and travel has become perhaps the most heavily
researched topic in urban planning (Ewing and Cervero
2010). Newman and Kenworthy’s iconic image of private
transport energy use versus density, shown in Figure 1, has
been reproduced in countless scholarly articles and govern-
ment reports. While others had previously written about the
interaction of land use and transportation, their work made
the bidirectional relationship more tangible and quantitative.
Yet, given its importance, their basic theory that density
(and the transit service it supports) almost uniquely deter-
mine automobile dependence has been subject to surpris-
ingly little scrutiny. This study demonstrates that while
density is correlated with per capita VMT, it accounts for
relatively little of the variance in per capita VMT across US
urbanized areas. Other variables such as personal income
and freeway capacity are more significant and have greater
elasticities.
As important, Newman and Kenworthy’s measure of den-
sity, GPD, has not nearly the explanatory power of a more
refined multivariate measure of density that captures the dis-
tribution of density across the urbanized area. The more
complete density factor score is much more significant and
has a much larger elasticity than GPD. We suspect the reason
is that the density factor score as shown in Table 5 includes
information beyond simple GPD. Lt1500 (% population liv-
ing at low suburban densities) and gt12500 (% population
Table 8. Regression of Per Capita Vehicle Miles Traveled on
Gross Population Density and Control Variables (Log–Log Form).
Model
Unstandardized
Coefficients
tSignificanceBStandard Error
(Constant) 3.870 0.608 6.370 .000
popden −0.164 0.080 −2.062 .041
pop 0.055 0.021 2.621 .010
flm 0.167 0.039 4.332 .000
olm 0.051 0.081 0.635 .526
fuel −0.561 0.332 −1.689 .093
inc 0.299 0.087 3.455 .001
rtden −0.020 0.032 −0.613 .541
tfreq −0.024 0.035 −0.678 .499
R20.450
Table 9. Regression of Per Capita Vehicle Miles Traveled on
Compactness Index and Control Variables (Log–Log Form).
Model
Unstandardized Coefficients
tSignificanceBStandard Error
(Constant) 3.838 0.524 7.325 .000
compact −0.203 0.071 −2.845 .005
pop 0.022 0.022 1.014 .312
flm 0.182 0.037 4.964 .000
olm 0.071 0.073 0.971 .333
fuel −0.692 0.328 −2.112 .036
inc 0.351 0.088 4.002 .000
rtden −0.037 0.026 −1.403 .163
tfreq −0.034 0.033 −1.033 .303
R20.464
Table 10. Regression of Per Capita Vehicle Miles Traveled
on Four Compactness Factors and Control Variables (Log–Log
Form).
Model
Unstandardized Coefficients
tSignificanceBStandard Error
(Constant) 5.342 0.741 7.205 .000
denfac −0.612 0.156 −3.919 .000
mixfac −0.017 0.055 −0.312 .756
cenfac −0.058 0.065 −0.898 .371
strfac −0.016 0.069 −0.233 .816
pop 0.072 0.022 3.329 .001
flm 0.156 0.037 4.213 .000
olm −0.029 0.078 −0.367 .714
fuel −0.567 0.330 −1.719 .088
inc 0.338 0.087 3.879 .000
rtden 0.024 0.030 0.791 .430
tfreq 0.015 0.035 0.426 .670
R20.508
Table 7. Simple Regression of Per Capita Vehicle Miles Traveled
on Gross Population Density (Log–Log Form).
Model
Unstandardized Coefficients
tSignificanceBStandard Error
constant 4.908 0.293 16.740 .000
popden −0.237 0.039 −6.015 .000
R20.189
Ewing et al. 13
living at medium to high urban densities) are more about the
distribution of population than about simple density. A simi-
lar urbanized area-level study using population-weighted
density by Lee and Lee (2014) reports an elasticity of trans-
portation-related CO2 per household with respect to density
of −0.48, similar to our elasticity of VMT per capita with
respect to the multivariate density factor. VMT is a good
proxy for transportation-related CO2. Thus, the distribution
of population and employment might be more important than
overall density at the regional level.
There are two troubling things about our results when
compared to Newman and Kenworthy’s and the literature
generally. Perhaps the most troubling is the much higher
elasticity of VMT per capita with respect to density in the
aggregate studies, and additionally, the failure of other
dimensions of compactness beyond density, namely, land use
mix, population and employment centering, and street con-
nectivity, to significantly relate to per capita VMT in the
aggregate studies. These other dimensions are actually more
important than density in disaggregate studies of the built
environment and travel behavior (Ewing and Cervero 2010;
Ewing et al. 2014c). There are several possible reasons for
the difference.
One is aggregation bias, and the ecological fallacy that
plagues aggregate studies like Newman and Kenworthy’s.
This is the reason so little of the built environment–travel
literature has used aggregate data since the mid-1990s.
The second is that at the highly aggregate scale of the
urbanized or metropolitan area, the variable density picks up
the effects of other D variables (and other variables gener-
ally, such as parking availability). This is an omitted variable
problem with the aggregate studies. We suspect, in particu-
lar, that at a highly aggregate scale, density and destination
accessibility (one of the Ds) become interchangeable. For
any given population size, a low-density area will have much
greater extent than a high-density area. This will cause auto-
mobile trips, on average, to be longer irrespective of mode
shifts to transit and walking (Downs 1992, 181). In our
aggregate study above, we do not explicitly model destina-
tion accessibility because of lack of data for 157 urbanized
areas. It would be a herculean task to acquire socioeconomic
and travel time data, and to derive destination accessibility
metrics, for such a large sample.
Finally, we suspect that the two types of studies provide
different results because they are asking different ques-
tions. The question in disaggregate studies is, What is the
travel-behavior impact of a change in one’s immediate
environment, holding metropolitan characteristics con-
stant? These are focused on the impact of marginal change
in a region. For example, What is the impact of living in a
walkable neighborhood versus an auto-oriented neighbor-
hood in sprawling Atlanta? This is a good framework if you
are, for example, the EPA and you are trying to figure out
how much emissions-reduction credit to allow this year for
transit-oriented development. But since a larger portion
(probably a majority) of one’s travel is conditioned by met-
ropolitan-level than neighborhood characteristics, the pic-
ture offered by the disaggregate framework is only partial
(Jonathan Levine, personal communication).
The aggregate studies are effectively asking the question,
What is the effect of changing metropolitan-level character-
istics? So rather than asking about the effect of dropping a
new urbanist neighborhood into metro Atlanta, they ask what
would travel behavior look like if metro Atlanta looked more
like metro Boston. It stands to reason that the travel-behavior
impact on a resident of a new urbanist neighborhood in
Atlanta would be a whole lot greater if the rest of Atlanta had
grown more like Boston than if Atlanta remained Atlanta. So
the greater impact shown by the aggregate studies is, in large
part, due to a change in scale (Jonathan Levine, personal
communication). It is not that one type of study is inherently
more accurate or relevant than the other, but that they ask and
answer different questions.
The other thing that is troublesome about our findings
relative to Newman and Kenworthy’s has to do with variance
within our samples. It would appear, on its face, to account
for some of the difference in results. By limiting our sample
to a small slice of their sample, those urbanized areas that fall
within the density range characteristic of the United States,
we have less variance in both the dependent variable, per
capita VMT, and the independent variable, GPD. Any given
scatter around the best-fit curve is accentuated when such a
small slice of the VMT/density curve is considered (as shown
in Figure 9, a slice of Figure 2).
Using per capita VMT and gross density data from
Kenworthy and Laube’s original data set (1989–1991), we
Figure 9. Per capita private passenger transport energy use and
urban density in typical US cities (from Figure 2).
14 Journal of Planning Education and Research
get an R2 of 0.72 when running a regression for the entire
data set, but only 0.096 when we run a regression with only
US cases. Out of curiosity, we also ran a regression for the
entire data set adding a single fixed effect variable for US
cases, and got the results in Table 11. The log of population
density is highly significant, but so is the fixed-effect vari-
able for the US cases (with a positive sign). This suggests an
apples and oranges problem in the data sets of Newman et al.
Houston and Hong Kong differ in many ways other than den-
sity alone, or even density and transit service availability.
They differ in terms of per capita income, fuel price, high-
way capacity per capita, and myriad other factors, including
culture. This may be most serious limitation of Newman and
Kenworthy’s original analysis.
Returning to the question of scale, ultimately, we think
that most planners aspire to systemwide change, not merely
scattered islands of urbanism in a sea of sprawl. While the
disaggregate framework is better fitted to “this-year-to-the-
next” policy impacts, the aggregate framework better fits our
long-run aspirations. This is the strongest argument for
aggregate studies like Newman and Kenworthy’s, and this
one as well (Jonathan Levine, personal communication).
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: Data collec-
tion for this research was funded by an HUD Sustainable Communities
Grant and by the Transit Cooperative Research Program.
References
Anthony, J. 2004. “Do State Growth Management Regulations
Reduce Sprawl?” Urban Affairs Review 39 (3): 376–97.
Beatley, T., and K. Manning. 1997. The Ecology of Place: Planning
for Environment, Economy, and Community. Washington, DC:
Island Press.
Berrigan, D., Z. Tatalovich, L. W. Pickle, R. Ewing, and R.
Ballard-Barbash. 2014. “Urban Sprawl, Obesity, and Cancer
Mortality in the United States: Cross-sectional Analysis and
Methodological Challenges.” International Journal of Health
Geographics 13 (1): 3.
Brons, M., P. Nijkamp, E. Pels, and P. Rietveld. 2006. A Meta-
analysis of the Price Elasticity of Gasoline Demand. A System
of Equations Approach. Amsterdam: Tinbergen Institute
Discussion Paper, No. 06-106/3. http://www.econstor.eu/han-
dle/10419/86304.
Cervero, R. 2002. “Induced Travel Demand: Research Design,
Empirical Evidence, and Normative Policies.” Journal of
Planning Literature 17 (1): 3–20.
Cervero, R., and K. Kockelman. 1997. “Travel Demand and the
3Ds: Density, Diversity, and Design.” Transportation Research
D 2 (3): 199–219.
Cervero, R., and J. Murakami. 2010. “Effects of Built Environments
on Vehicle Miles Traveled: Evidence from 370 US Urbanized
Areas.” Environment and Planning A 42 (2): 400–18.
Cho, S. H., Z. Chen, S. T. Yen, and D. B. Eastwood. 2006. “The
Effects of Urban Sprawl on Body Mass Index: Where People
Live Does Matter?” Consumer Interests Annual 52:159–69.
Cutsinger, J., G. Galster, H. Wolman, R. Hanson, and D. Towns.
2005. “Verifying the Multi-dimensional Nature of Metropolitan
Land Use: Advancing the Understanding and Measurement of
Sprawl.” Journal of Urban Affairs 27 (3): 235–59.
Downs, A. 2000. Stuck in Traffic: Coping with Peak-Hour Traffic
Congestion. Washington, DC: Brookings Institution Press.
Doyle, S., A. Kelly-Schwartz, M. Schlossberg, and J. Stockard. 2006.
“Active Community Environments and Health: The Relationship
of Walkable and Safe Communities to Individual Health.”
Journal of the American Planning Association 72 (1): 19–31.
Dujardin, S., F. Pirart, F. Brevers, A. F. Marique, and J. Teller. 2012.
“Home-to-Work Commuting, Urban Form and Potential Energy
Savings: A Local Scale Approach to Regional Statistics.”
Transportation Research Part A: Policy and Practice 46 (7):
1054–65.
Espey, M. 1998. “Gasoline Demand Revisited: An International
Meta-analysis of Elasticities.” Energy Economics 20 (3): 273–95.
Ewing, R. 2008. “Highway Induced Development: Results for
Metropolitan Areas.” Transportation Research Record
2067:101–9.
Ewing, R., K. Bartholomew, S. Winkelman, J. Walters, and D. Chen.
2008. Growing Cooler: The Evidence on Urban Development
and Climate Change. Washington, DC: Urban Land Institute.
Ewing, R., and R. Cervero. 2001. “Travel and the Built Environment:
A Synthesis.” Transportation Research Record 1780:87–114.
Ewing, R., and R. Cervero. 2010. “Travel and the Built
Environment: A Meta-analysis.” Journal of the American
Planning Association 76 (3): 265–94.
Ewing, R., and R. Cervero Forthcoming. “Does Compact
Development Make People Drive Less? The Answer Is ‘Yes.’”
Journal of the American Planning Association.
Ewing, R., and S. Hamidi. 2014a. Measuring Urban Sprawl and
Validating Sprawl Measures. Washington, DC: National
Institutes of Health and Smart Growth America.
Ewing, R., and S. Hamidi. 2014b. “Longitudinal Analysis of
Transit’s Land Use Multiplier in Portland (OR).” Journal of
the American Planning Association 80 (2): 123–37.
Ewing, R., and S. Hamidi. 2015. “Urban Sprawl as a Risk Factor in
Motor Vehicle Occupant and Pedestrian Fatalities—Update and
Refinement.” Transportation Research Record 25(13):40–47.
Table 11. Regression of Per Capita Vehicle Miles Traveled on
the Gross Density and a US Dummy Variable (Log–Log Form).
Model
Unstandardized
Coefficients
Standardized
Coefficients
tSignificanceB
Standard
Error Beta
constant 12.8 0.585 21.896 .000
popden −0.513 0.062 −0.705 −8.247 .000
us dummy 0.418 0.129 0.277 3.246 .002
R20.776
Ewing et al. 15
Ewing, R., S. Hamidi, F. Gallivan, A. C. Nelson, and J. B. Grace.
2014a. “Structural Equation Models of VMT Growth in US
Urbanised Areas.” Urban Studies 51 (14): 3079–96.
Ewing, R., S. Hamidi, and J. B. Grace. 2016. “Urban Sprawl as a
Risk Factor in Motor Vehicle Crashes.” Urban Studies 53 (2):
247-66.
Ewing, R., S. Hamidi, J. B. Grace, and Y. D. Wei. 2016. “Does
Urban Sprawl Hold Down Upward Mobility?” Landscape and
Urban Planning 148:80–88.
Ewing, R., G. Meakins, S. Hamidi, and A. C. Nelson. 2014b.
“Relationship between Urban Sprawl and Physical Activity,
Obesity, and Morbidity—Update and Refinement.” Health &
Place 26:118–26.
Ewing, R., R. Pendall, and D. Chen. 2002. Measuring Sprawl and
Its Impacts. Washington, DC: Smart Growth America.
Ewing, R., R. Pendall, and D. Chen. 2003. “Measuring Sprawl and
Its Transportation Impacts.” Transportation Research Record
1831 (1): 175–83.
Ewing, R., and F. Rong. 2008. “The Impact of Urban Form on US
Residential Energy Use.” Housing Policy Debate, 19 (1): 1–30.
Ewing, R., G. Tian, J. P. Goates, M. Zhang, M. J. Greenwald, A.
Joyce, and W. Greene. 2014c. “Varying Influences of the Built
Environment on Household Travel in 15 Diverse Regions of
the United States.” Urban Studies 52 (13): 2330–48.
Fan, Y., and Y. Song. 2009. “Is Sprawl Associated with a Widening
Urban–Suburban Mortality Gap?” Journal of Urban Health 86
(5): 708–28.
Frenkel, A., and M. Ashkenazi. 2008. “Measuring Urban Sprawl:
How Can We Deal with It?” Environment and Planning B 35
(1): 56–79.
Fulton, W. B., R. Pendall, M. Nguyen, and A. Harrison. 2001.
Who Sprawls Most? How Growth Patterns Differ across the
US? Washington, DC: Brookings Institution, Center on Urban
and Metropolitan Policy. http://www.brookings.edu/~/media/
research/files/reports/2001/7/metropolitanpolicy%20fulton/
fultoncasestudies.pdf.
Galster, G., R. Hanson, M. R. Ratcliffe, H. Wolman, S. Coleman,
and J. Freihage. 2001. “Wrestling Sprawl to the Ground:
Defining and Measuring an Elusive Concept.” Housing Policy
Debate 12 (4): 681–717.
Goodwin, P., J. Dargay, and M. Hanly. 2004. “Elasticities of Road
Traffic and Fuel Consumption with Respect to Price and
Income: A Review.” Transport Reviews 24 (3): 275–92.
Gordon, P., and H. W. Richardson. 1989. “Gasoline Consumption
and Cities: A Reply.” Journal of the American Planning
Association 55 (3): 342–46.
Graham, D. J., and S. Glaister. 2004. “Road Traffic Demand Elasticity
Estimates: A Review.” Transport Reviews 24 (3): 261–74.
Griffin, A. B., C. Eibner, C. E. Bird, A. Jewell, K. Margolis, R. Shih,
M. E. Slaughter, E. A. Whitsel, M. Allison, and J. J. Escarce.
2012. “The Relationship between Urban Sprawl and Coronary
Heart Disease in Women.” Health & Place 20:51–61.
Hamidi, S., and R. Ewing. 2014. “A Longitudinal Study of Changes
in Urban Sprawl between 2000 and 2010 in the United States.”
Landscape and Urban Planning 128:72–82.
Hamidi, S., and R. Ewing. 2015. “Is Sprawl Affordable for
Americans? Exploring the Association between Sprawl and
Housing + Transportation Affordability.” Transportation
Research Record 2500:75–79.
Hamidi, S., R. Ewing, I. Preuss, and A. Dodds. 2015. “Measuring
Sprawl and Its Impacts: An Update.” Journal of Planning
Education and Research 35 (1): 35–50.
Hickman, R., C. Seaborn, P. Headicar, and D. Banister. 2009.
Planning for Sustainable Travel. Summary Guide. London:
Halcrow and CfIT.
Holcombe, R. G., and D. W. Williams. 2012. “Urban Sprawl and
Transportation Externalities.” Review of Regional Studies 40
(3): 257–72.
Jaeger, J. A., R. Bertiller, C. Schwick, and F. Kienast. 2010.
“Suitability Criteria for Measures of Urban Sprawl.” Ecological
Indicators 10 (2): 397–406.
Joshu, C. E., T. K. Boehmer, R. C. Brownson, and R. Ewing. 2008.
“Personal, Neighborhood and Urban Factors Associated with
Obesity in the United States.” Journal of Epidemiology and
Community Health 62 (3): 202–8.
Kelly-Schwartz, A. C., J. Stockard, S. Doyle, and M. Schlossberg.
2004. Is Sprawl Unhealthy? A Multilevel Analysis of the
Relationship of Metropolitan Sprawl to the Health of Individuals.”
Journal of Planning Education and Research 24 (2): 184–96.
Kenworthy, J. R., and F. B. Laube. 1999. “Patterns of Automobile
Dependence in Cities: An International Overview of Key
Physical and Economic Dimensions with Some Implications for
Urban Policy.” Transportation Research Part A 33 (7): 691–723.
Kenworthy, J. R., F. B. Laube, P. Newman, P. Barter, T. Raad, C.
Poboon, and B. Guia, Jr. 1999. An International Sourcebook
of Automobile Dependence in Cities 1960–1990. Boulder, CO:
University Press of Colorado.
Kim, D., S. V. Subramanian, S. L. Gortmaker, and I. Kawachi.
2006. “U.S State- and County-Level Social Capital in Relation
to Obesity and Physical Inactivity: A Multilevel, Multivariable
Analysis.” Social Science & Medicine 63 (4): 1045–59.
Kirby, A. 2008. Kick the Habit: A UN Guide to Climate Neutrality.
Nairobi: UNEP.
Kostova, D. 2011. “Can the Built Environment Reduce Obesity?
The Impact of Residential Sprawl and Neighborhood Parks on
Obesity and Physical Activity.” Eastern Economic Journal 37
(3): 390–402.
Lang, R. 2003. Edgeless Cities: Exploring the Elusive Metropolis.
Washington, DC: Brookings Institution.
Lee, I., R. Ewing, and H. D. Sesso. 2009. “The Built Environment and
Physical Activity Levels: The Harvard Alumni Health Study.”
American Journal of Preventive Medicine 37 (4): 293–98.
Lee, S., and B. Lee. 2014. “The Influence of Urban Form on GHG
Emissions in the U.S. Household Sector.” Energy Policy
68:534–49.
Lopez, R., and H. P. Hynes. 2003. “Sprawl in the 1990s:
Measurement, Distribution, and Trends.” Urban Affairs Review
38 (3): 325–55.
Mubareka, S., E. Koomen, C. Estreguil, and C. Lavalle. 2011.
“Development of a Composite Index of Urban Compactness
for Land Use Modelling Applications.” Landscape and Urban
Planning 103 (3): 303–17.
Newman, P. 2014. “Density, the Sustainability Multiplier: Some
Myths and Truths with Application to Perth, Australia.”
Sustainability 6 (9): 6467–87.
Newman, P., and J. R. Kenworthy. 1989a. Cities and Automobile
Dependence: An International Sourcebook. Aldershot, UK:
Gower Technical.
16 Journal of Planning Education and Research
Newman, P., and J. R. Kenworthy. 1989b. “Gasoline
Consumption and Cities: A Comparison of US Cities
with a Global Survey.” Journal of the American Planning
Association 55 (1): 24–37.
Newman, P., and J. Kenworthy. 2006. “Urban Design to Reduce
Automobile Dependence.” Opolis 2 (1): 35–52.
Newman, P., and J. Kenworthy. 2011a. “Peak Car Use:
Understanding the Demise of Automobile Dependence.” World
Transport Policy and Practice 17 (2): 31–42. http://www.eco-
logica.co.uk/pdf/wtpp17.2.pdf.
Newman, P., and J. Kenworthy. 2011b. “The Density Multiplier:
A Response to Mees.” World Transport Policy and Practice
17 (3).
Newman, P., and J. Kenworthy. 2015. The End of Automobile
Dependence: How Cities Are Moving beyond Car-Based
Planning. Washington, DC: Island Press.
Nguyen, D. 2010. “Evidence of the Impacts of Urban Sprawl
on Social Capital.” Environment and Planning B 37 (4):
610–27.
Pendall, R. 1999. “Do Land-Use Controls Cause Sprawl?”
Environment and Planning B 26 (4): 555–71.
Pendall, R., and J. I. Carruthers. 2003. “Does Density Exacerbate
Income Segregation? Evidence from US Metropolitan Areas,
1980 to 2000.” Housing Policy Debate 14 (4): 541–89.
Perumal, A., and D. Timmons. 2015. “Contextual Density and
US Automotive CO2 Emissions across the Rural-Urban
Continuum.” International Regional Science Review. Published
online November 18. doi: 10.1177/0160017615614897.
Plantinga, A. J., and S. Bernell. 2007. “The Association between
Urban Sprawl and Obesity: Is It a Two-Way Street?” Journal
of Regional Science 47 (5): 857–79.
Schweitzer, L., and J. Zhou. 2010. “Neighborhood Air Quality,
Respiratory Health, and Vulnerable Populations in Compact
and Sprawled Regions.” Journal of the American Planning
Association 76 (3): 363–71.
Small, K. A., and K. Van Dender. 2007. “Fuel Efficiency and Motor
Vehicle Travel: The Declining Rebound Effect.” International
Association for Energy Economics 28 (1): 25–51.
Stone, B. 2008. “Urban Sprawl and Air Quality in Large US Cities.”
Journal of Environmental Management 86 (4): 688–98.
Stone, B., J. J. Hess, and H. Frumkin. 2010. “Urban Form and
Extreme Heat Events: Are Sprawling Cities More Vulnerable
to Climate Change Than Compact Cities?” Environmental
Health Perspectives 118 (10): 1425–28.
Torrens, P. M. 2008. “A Toolkit for Measuring Sprawl.” Applied
Spatial Analysis and Policy 1(1): 5–36.
Trowbridge, M. J., M. J. Gurka, and R. E. O’Connor. 2009. “Urban
Sprawl and Delayed Ambulance Arrival in the US.” American
Journal of Preventive Medicine 37 (5): 428–32.
Trowbridge, M. J., and N. C. McDonald. 2008. “Urban Sprawl
and Miles Driven Daily by Teenagers in the United States.”
American Journal of Preventive Medicine 34 (3): 202–6.
Zolnik, E. J. 2011. “The Effect of Sprawl on Private-Vehicle
Commuting Outcomes.” Environment and Planning–Part A 43
(8): 1875–93.
Author Biographies
Reid Ewing is a professor and chair of City and Metropolitan
Planning at the University of Utah. He also directs the Metropolitan
Research Center. His research interests focus on the interactions
between land use and transportation.
Shima Hamidi is an assistant professor and Director of the Institute
of Urban Studies at University of Texas Arlington. Her research
interests include transportation equity and affordability, urban
sprawl and its quality of life impacts.
Guang Tian is a recent graduate of the Doctoral Program in
Metropolitan Planning, Policy, and Design at the University of
Utah. His research interests include travel behavior and built envi-
ronment, active transportation, and GIS applications in planning.
David Proffitt is a doctoral student at the University of Utah. He
studies the relationships between transportation planning, the built
environment, and climate change.
Stefania Tonin is an associate professor in the Department of
Design and Planning in Complex Environments at the University
Iuav, Venice. Her research interests include economics of sustain-
ability, land use and urban sprawl, and economic valuation.
Laura Fregolent is an associate professor in the Department of
Design and Planning in Complex Environments at the University
Iuav, Venice. Her research interests include territorial transforma-
tion and planning tools, land use and urban sprawl.
