Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Fig. 4. The landscape metric also captures &#8220leapfrog” development and development at further
distances—note that the scenarios presented here have the same areal proportions as Fig. 3 (and therefore,
the same number of housing units). Compared with a fairly compact pattern (top, and also center Fig. 3),
the effect of disjunct development is an increase from 338.9 (top) to 373.9 (center). Moving development
yet further afield, the score increases to 399.3 (bottom).
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Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Fig. 5. The Landscape Sprawl (LS) metric also can be used to capture patterns of development over time.
The scenarios here reflect a 10% increase in the original number of housing units (Fig. 3, top). Expanding
the area occupied by urban housing density to accommodate the additional housing units increases LS only
slightly (274.7, top, compared with 267.8 in the original). The LS metric increases to 286.7 (center) when
the suburban class is expanded. LS increases to 403.1 (bottom) when the additional units are developed at
exurban densities.
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Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Table  1. The extent of development for the coterminous U.S., grouped by housing density class.
“Developable” land includes private lands that do not have some protected designation. “Undevelopable”
includes public (e.g., Forest Service, parks, etc.) and other protected lands (derived from DellaSala et al.
2001).
Density class
Extent (km
2
)
Percentage of developable land
1980
2000
2020
1980
2000
2020
Urban/suburban (>0.69 ha/
unit)
95 635
125 729
174 226
1.7%
2.2%
3.1%
Exurban
(0.69–16.18 ha/unit)
693 591
917 090
1 116 046
12.2%
16.1%
19.6%
Rural
(>16.18 ha/unit)
4 891 988
4 638 395
4 275 543
86.1%
81.6%
75.2%
RESULTS
Status and Trends in Developed Lands
In 2000, there were 125 729 km
2
in urban/suburban
residential housing density nationwide, excluding
commercial  and  industrial  lands  typically
associated with urban  areas (Table 1).  There are
slightly  over  seven  times  the  additional  area  in
exurban  housing  density  (917  090  km
2
).  About
1.6% of land nationwide (coterminous U.S.) was in
urban/suburban residential density, whereas 11.8%
was  in  exurban  in  2000.  The  urban/suburban/
exurban development footprint has increased from
10.1% to 13.4% (1980 to 2000), roughly at a rate of
1.60%  per  year.  This  rate  of  land  development
outstrips by 25% the rate of population growth from
the  same  time  period  of  1.18%  per  year—a
conservative estimate because rural lands were not
included in the computation, but rural population
was included. There were 2 107 894 km
(27%) in
non-developable (i.e., public  or  private-protected
lands).  The  distribution  of  these  development
patterns can be seen in Fig. 6 (low resolution), Fig.
7 (high resolution), and App. 2 (Portable Document
File).
To facilitate easy examination of this database, I
provide  a  spreadsheet  containing  summaries  of
these data by county (see App. 3). Also, animations
of the development patterns from 1980 to 2020 on
a  decadal  basis  allow  visualization  of  spatio-
temporal patterns for the nation (Fig. 8) and western
(Fig. 9), central (Fig. 10), and eastern U.S. (Fig. 11).
Other analytical units, such as hydrologic unit codes
(HUC 8-digit codes) or ecoregions, could be used
to summarize the housing density data as well.
Another  way  to  examine  urban  and  exurban
development  patterns  is  to  determine  what
proportion remains rural: that is, either rural housing
density  (private  lands) or public/protected  lands.
Ruralness  is defined  here  as the  proportion  of  a
county (or state) in  rural housing density,  or the
proportion of the developable area in the county (or
state)  in  rural  housing  density.  The  amount
developed  (urban,  suburban,  and  exurban)  is
roughly the opposite of the rural landscape (Figs.
12, 13, and 14). Notably, some of the “New West”
states (Arizona, Colorado, Idaho, Utah) appear less
rural than many of the northern Great Plains states
(Iowa,  Kansas, Montana, Nevada, North Dakota,
South Dakota), because much of the open space is
provided by public lands that are “undevelopable.”
Model Forecasts
The  SERGoM  forecast  model  performed
reasonably well (Table 2), resulting in high accuracy
overall for 1990 (urban = 93.0%, exurban = 91.2%,
and rural = 99.0%) and reasonably high accuracy
for 2000 (urban  = 84.2%, exurban = 79.4%, and
rural  =  99.1%).  With  coarser  resolutions,  the
accuracy increased minimally for the 1990 pattern
and  slightly  for  2000  (exurban  increased  from
79.4% to 82.3%.
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Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Fig. 6. A low-resolution map showing housing density classes for 2000.
Status and Trends of Landscape Sprawl Metric
Values of the LS metric ranged from 0.06 to 330.53
throughout the U.S.. LS values increased from 1980
(mean = 232.17, SD = 258.40) to 2000 (mean =
248.06, SD = 258.55) to 2020 (mean = 263.47, SD
= 257.11), indicating that exurban development and
sprawl have increased throughout the U.S. There is
substantial spatial variation of LS between counties,
however, and Fig. 15 shows the LS metric averaged
by counties for 2000 (and App. 4). Moreover, there
is significant local spatial variation within a county
(Fig. 16). Surprising spatio-temporal patterns arise
in LS because urban core areas may emerge over
time, causing a phase change in the LS metric values
in a region as municipal goods and services move
into new regions (see Fig. 17).
DISCUSSION
Understanding  the  patterns  and  trends  of urban 
sprawl is important, but there are important land-
use  patterns and  dynamics  occurring  beyond the
urban  fringe.  Not  only  is  the  extent  of  exurban
housing density 7–10 times that of urban areas, but
per  capita  land  consumption  in exurban  areas is
much greater than in urban locations.
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Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Fig. 7. A high-resolution map showing housing density classes for 2000.
Fig.  8. An animation of national development
patterns from 1980 to 2020.
View animated Figure
Fig. 9. An animation of western U.S. development
patterns from 1980 to 2020.
View animated Figure
Fig. 10. An animation of central U.S. development
patterns from 1980 to 2020.
View animated Figure
Fig. 11. An animation of eastern U.S. development
patterns from 1980 to 2020.
View animated Figure
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Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Fig. 12. A map showing the proportion of a county in rural housing density for 1980.
The housing density database produced in this study
is intended to complement other existing land-use/
land-cover  databases.  Compared  with  the  NRI
(NRCS  2001)  database,  it  provides  a  detailed
coverage (based  on a  census, not a  sample)  that
allows spatially explicit patterns to be examined for
potential fragmentation effects. Compared with the
U.S. Geological Survey/Environmental Protection
Agency NLCD (Vogelmann et al. 2001), it provides
insight  beyond  urban  and  built-up  areas  into
exurban areas. However, as noted earlier, intense
urban land uses, such as commercial and industrial,
are not captured well in the census data used here,
but are readily identified in the NLCD data. Future
work will attempt to integrate these two data sets to
a greater degree.
The LS metric quantified patterns and locations of
urban  and  exurban  sprawl,  but  requires  careful
interpretation. Because LS should be summarized
by some analytical unit (e.g., a watershed, a county,
an MSA), comparisons between different regions
must  be  normalized. LS  exhibits  a  non-linear
response to different development patterns that are
controlled by two critical parameters. First, at the
critical threshold specified by the assumption of the
Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Fig. 13. A map showing the proportion of a county in rural housing density for 2000.
size and contiguity of an urban core, LS adjusts to
the emergence of a new urban core area. A once-
exurban or rural area that results in large LS values
because of long distances to the nearest urban core
can rapidly have much reduced LS values as a new
urban  core  emerges.  Second,  as  a  rural  area  is
developed  and  converted  to  exurban or  possibly
urban/suburban  land  use,  natural  resource  or
ecological  values  rapidly  diminish.  This  critical
threshold exists roughly at the low-density end of
the  exurban  housing  density  class.  In  particular,
development edge is defined to occur at the interface
between exurban and rural and protected lands, but
not  at the urban/suburban interface with exurban
lands. Of course there are a variety of micro- or site-
scale conditions (siting of buildings, landscaping,
fencing,  allowed  or  prohibited  human  activities,
etc.)  that  can  also  have  a  strong  influence  on
potential ecological effects that are not accounted
for in LS. Although these non-linearities and phase
changes are more difficult to interpret, this mirrors
real-world phenomena that are intrinsic to land-use
dynamics (Batty 1997). To progress beyond simple
measures of  sprawl  based  on population  density
changes,  these  situations  need  to  be  explicitly
measured.
Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Fig. 14. Proportion of a county in rural housing density for 2020.
An  important  challenge  for  ecologists  is  to
contribute to better understanding of these critical
thresholds, their regional variation, and the potential
for  micro-  or  site-level  measures  to  mitigate
possible broader-scale effects. In general, an initial
analysis of development patterns using LS suggests
that  development  patterns  that  were  more
contiguous, higher density, and more compact (not
dispersed) had reduced overall  effects on  natural
resources because they resulted in smaller footprints
or  “disturbance  zones,”  lower  percentage  of
impervious surface, and reduced pollution because
fewer vehicle miles were generated. Moreover, the
practical  complications  of  natural  resource
management are much reduced with more compact
patterns of development. LS is a preliminary metric
to  quantify  the  general  effects  (or  impacts)  of
development patterns on ecological systems. Much
work remains to develop causal relationships and
more conclusive results, but quantifying development
patterns in a logical and consistent manner is an
important first step. To complement current work
that  seeks  to  understand  the  ecological
consequences of urban sprawl (e.g., Blair 2004), the
LS metric needs to be tested by empirical, field-
Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Table 2. The results of the test of the SERGoM model comparing estimated areas
for urban/suburban, exurban, and rural housing density classes for 1990 and
2000 against forecasted patterns.
Resolution 1 ha, Year 1990
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
10,081,521
703,879
58,201 Urban
93.0%
6.5%
0.5%
Exurban
813,885 71,055,499
6,050,269 Exurban
1.0%
91.2%
7.8%
Rural
8,205
4,723,352 473,180,337 Rural
0.0%
1.0%
99.0%
Resolution 1 ha, Year 2000
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
10,518,408
1,717,056
260,980 Urban
84.2%
13.7%
2.1%
Exurban
876,047 72,573,279 17,994,389 Exurban
1.0%
79.4%
19.7%
Rural
21,392
4,209,015 458,504,582 Rural
0.0%
0.9%
99.1%
Resolution 4 ha, Year 1990
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
11,068,972
785,308
49,400 Urban
93.0%
6.6%
0.4%
Exurban
809,680 74,892,988
6,119,500 Exurban
1.0%
91.5%
7.5%
Rural
3,504
4,833,044 478,906,016 Rural
0.0%
1.0%
99.0%
Resolution 4 ha, Year 2000
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
11,550,736
1,928,984
243,824 Urban
84.2%
14.1%
1.8%
Exurban
857,476 76,444,180 18,312,696 Exurban
0.9%
80.0%
19.2%
Rural
15,352
4,318,952 463,796,212 Rural
0.0%
0.9%
99.1%
(con'd)
Ecology and Society 10(1): 32
http://www.ecologyandsociety.org/vol10/iss1/art32/
Resolution 16 ha, Year 1990
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
12,110,992
874,304
36,560 Urban
93.0%
6.7%
0.3%
Exurban
829,216 80,066,768
6,181,248 Exurban
1.0%
91.9%
7.1%
Rural
1,552
5,034,896 488,480,752 Rural
0.0%
1.0%
99.0%
Resolution 16 ha, Year 2000
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
12,663,840
2,178,672
203,248 Urban
84.2%
14.5%
1.4%
Exurban
855,936 81,695,040 18,712,096 Exurban
0.8%
80.7%
18.5%
Rural
12,304
4,487,488 472,807,664 Rural
0.0%
0.9%
99.1%
Resolution 64 ha, Year 1990
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
13,213,056
944,640
22,144 Urban
93.2%
6.7%
0.2%
Exurban
941,440 87,619,200
6,249,408 Exurban
1.0%
92.4%
6.6%
Rural
1,536
5,609,664 502,895,232 Rural
0.0%
1.1%
98.9%
Resolution 64 ha, Year 2000
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
13,864,576
2,466,880
145,536 Urban
84.1%
15.0%
0.9%
Exurban
943,488 89,389,248 19,349,632 Exurban
0.9%
81.5%
17.6%
Rural
10,944
4,985,664 486,340,352 Rural
0.0%
1.0%
99.0%
Resolution 256 ha, Year 1990
Hectares
Forecasted
% correct
Forecasted
Estimated Urban
Exurban
Rural
Estimated Urban
Exurban Rural
Urban
14,220,288
990,976
9,728 Urban
93.4%
6.5%
0.1%
(con'd)
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