Alternative Measures of Urban Form in
U.S. Metropolitan Areas
Stephen Malpezzi
Wen-Kai Guo
University of Wisconsin-Madison
What is sprawl?


Most writers and activists fail to define sprawl. Some
elements of a definition might include:
– Low density
– Discontiguous (“leapfrog”) development
– Lack of public open space
Other outcomes that may or may not be associated with
sprawl include:
– High auto use, low transit use
– Differences in the cost of public services
– Excessive loss of farmland
Overall Plan for Malpezzi and Guo




Estimate a number of candidate measures of urban form
– MSA specific indexes, based on Census tract data
Which incorporate the ‘most information’ about form?
– Regress each index against other indexes, examine fit
and t-statistics
Which are reasonably related to determinants?
– Regress each index against a reasonable set of
determinants
Link to second paper: take the best index, and run with it.
Candidate Indexes







Average MSA density
Sort tracts by their density. Pick density of tract containing the
“median person.”
– Many variations on this theme.
Estimate exponential density models
– Univariate: intercept as well as delta, compare to flexible forms.
Incorporate measures of fit.
Measures of dispersion
– Gini, Theil indexes
Weighted average distances
– to center; to all tracts
Gravity measures
Spatial autocorrelation
Selected Previous Research



A number of ‘sprawl’ papers examine average metropolitan
density (Brueckner and Fansler, Peiser)
Many papers examine population density gradients, and
related measures (Mills, Muth, etc., see McDonald review)
Compare and evaluate alternative measures
– A fair number evaluate, e.g., power terms, test SUE
model against a flexible alternative (e.g. Kau and Lee)
– Only a few examine a fair range of alternatives (e.g.
Song)
Sprawl, Related Issues


Bertaud and Malpezzi demonstrate that, in fact, cities like
Paris and Los Angeles have much more efficient form than
Seoul or Moscow, or Johannesburg.
What are the specific costs of sprawl which give rise to this
concern? Are there benefits to “sprawl?” What are the
most efficient policy responses?
– E. Mills and B. Song, Urbanization and Urban Problems.
Harvard, 1979.
– G. Ingram, Land in Perspective. In Cullen and Woolrey, World
Congress on Land Policy, DC Heath, 1982
– A. Bertaud and S. Malpezzi, The Spatial Distribution of Population
in 35 World Cities
Measuring Sprawl








Since sprawl is hard to define, it’s not surprising few
papers have tried to measure it.
Many papers rely on average population density in the
metro area.
Our usual density gradients
– including power terms, R-squared
Moments of tract density
Gini coefficients, Theil information measures
Distance/gravity measures
Techniques of measuring spatial autocorrelation
Data reduction (principal components?)
Measuring Sprawl



Our initial measure will rely on tract densities within
MSAs.
Sort each MSA’s census tracts by density, lowest to
highest. Use the density of the tract containing the 10th
percentile of MSA population, when tracts are so ordered.
– Can use other percentiles (median, quartiles, etc.)
– A better measure of density at the fringe.
– Pros and cons?
Under development: average lot size for a “new” single
family house, from AHS
Example of a measure based on order statistics: the average
density of the tract containing the median of the MA
population, when tracts are ranked by density.
N
1
2
3
4
5
6
7
Tract
Density
10
9
8
7
6
5
4
Tract
Population
30
30
10
10
10
5
5
Our MA has 7 tracts, total pop. is 100. Where is person 50?
The measure we focus on today.



The average density of the tract containing the 10th
percentile of the metropolitan area’s population, when
tracts are ranked by density. Say it 10 times, fast.
Pros:
– Distinguishes between MAs with a lot of open space,
and those without.
– Gets at density on “the margin” without a particular
assumption about monocentricity.
Cons:
– There’s no guarantee that this “fringe” tract is really on
the fringe.
– The usual issues with using “gross” tract densities.
Costs and Benefits of Sprawl: The “Pure
Cost” View
$
Costs per housing unit fall with density
Maximum feasible density, under
current rules and practices
Density of Development
Figure 1
Costs and Benefits of Sprawl: The CostBenefit View
$
Costs fall with density
Willingness-to-pay first rises, then
falls, with density
Maximum feasible density
Maximize
Benefit-Cost
Figure 2
Density of Development
Costs and Benefits of Sprawl: The CostBenefit View, with Externalities
$
Social costs (= private costs + external cost)
Private costs
Willingness-to-pay
Maximum feasible density
Figure 3
Maximize
Private
Benefit-Cost
Maximize
Social
Benefit-Cost
Density of Development
Population Density
Figure 1-A
Distance from Center
Population Density
Figure 1-B
Distance from Center
P e rs o n s p e r S q u a re K ilo m e te r
Metropolitan Area Average Density
10,000
JC
NY
PAT
1,000
100
10
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NAU
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1
10,000
100,000
1,000,000
MSA Population, 1990
Figure 2
10,000,000
D e n s ity o f W e ig h te d M e d ia n T ra c t
Density of Census Tract Containing
Median Person
100,000
NY
JC
10,000
SF
1,000
100
10
10,000
100,000
1,000,000
MSA Population
Figure 3
LA
CHI
SJS MIA ANH
HON
NO
OAK
NWK
SDI
PHL
LAR
STC ELP LSV BUF
PHXNAU
DET
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10,000,000
F ir s t D e c ile of T r a c t D e n s itie s
First Decile of Census Tract Densities
And MSA Population
10,000
JC
1,000
100
10
NY
LA
ANH
M IA
CHI
SF
SJS
NAU
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BSM
1
10,000
100,000
1,000,000
MSA Population
Figure 4
10,000,000
100,000,000
C o e ffi c i e n t o f L o g D e n s i t y
Population Density Gradient
One Parameter Model
0.2
ANC
0.1
O RG
0.0
-0.1
-0.2
-0.3
BRZ
NAU
JC
SBR
W PB
RVR
HMOAUR
SLK
JDN
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N
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BIN
DCI
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LF I
LEW
END
LA
NY
-0.4
10,000
100,000
1,000,000
MSA Population
Figure 4
10,000,000
Fit of Population Density Gradient
One Parameter Model
1.0
R-Squared of Simple Model
OWN
VTX
0.8
0.6
0.4
0.2
0.0
10,000
LAR
DCI JMI
BUR
SNGMUN
GFM
SMO
LWKGAD SIX
JTN
RCY
PUE
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TYL
TUS
ROA
CAS
MDT
PCF
ASN
BUR
LAW
TPK
LCN
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ALG
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GBY
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THA
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CDR BRD
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SRS
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ANN
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MAC
WOR SYR
PHL
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TRN
YRK
MOD
CSC
SAT DEN PGH DAL
ANI
DAV
UTR
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NONFK
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DAB
ELP
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FIM
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JKL
CHI
SWO
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BGR
NLN
ALN DTN
VIS
HIK
WFT
HBG
FTM
YAX
ALB
SAG
HTL
BAK
LEO
NAU
SHN
ANC
JOL
CPX
GNC
SDI
GRRHON
BCR
SGMAPL JKB
PHX
STC
CHT PDRKIL
FWO
VAL
OAK
MON
SMAGSC
BRN PAW
RLG MSX
VMB
SF TPA
SJS
JWI
BDR ATC
SWB
BEV
PAS BNT SCZ
FPC
NHA
BEU LAK
GAL
SLK
MEL
OXN
ANH
JDN
AUR
ORG
STM
HMO SBR
WPB
BRZ
JC
END
100,000
1,000,000
MSA Population
Figure 5
NY
LA
10,000,000
10
Intercept from Univariate Exponential
Population Density Gradient
NY
NO
JC
SF
BOS
CHI
NWK
MIA
BAL
PHL
LWL
DEN
ABQ
MIL
HON
OAK
DC
BUF
LEW
SJS SEA MIN
MAD
SDI
ELP LSV
TRN
LFI MCHINC
LAR
DET
CLE ANH
COS
BDC OMH
POO
LVLPRV
PRO RDG
CIN
STL
NBC GBY
NFK
PHX
CGA
SBN
FIM
SNS
DAL HOU
IOWDCI
PME
MEM
BRO
LBK
GRY
TAC
PGH
TPK
RKF
WIL
ANN
HRT
BOI
ELM
IND
BRY
WAT
MSX SAT
BIN
TUS BRD
MUN
MDT
LWK
SNG
BUR
LHM
COL
ODS
SAV
SGMPAW
LEX
FLT
TDO
SPK
VAL
ROC
AKR
OWN
WOR
STC
ATL
SIX
MOD
PUE
ORL
TPA
KEN
SMA
HALERI
JMI SILNIA
SMO
LAN
KAL
LAN
KCM
WCH
AUS
LAWBILRLD
VTX LAC
DES
LEO
PEO
FRO
RCH
SHR
EVNSTM
CSC
NSH
FCL
FYN
CSC
ALN
AMR
DTN
SAC
CPX
NHA
FWO
WAC
ALT
CTN
YNG
ALB
ABL
CHM
FWA
GFKSTJ RCM
CDR
CVL
SLK
MNL
MON
BAT
TUL OKC
RAC
BXI SRA
HBG
OXN
PEN
SRS
SYR
APL
PCF
END
FAR
MTG
JMSMOB
BIR
BNH
TUC
BEVROA
RVR
LCN
MUS
DAB
BRM
BDR
UTR
PKE
TYL
DBQ
BLG
SCP
THA
MEM
SWB JKL
JTN
GFM
SBY
SCZ
WNC
RNO
DAV
CNOLAK GRR
GAL
WMP
BRN
MNO
GAD
SWV
ASN
FWB
ALG
JNC
WWV
KNK
KOK
AUG
LYN
NSH
LKL
NLN
SXC
CMO
DNB
BGR
SWO
LRA
FAZ
ATC
YRK
HAW
FAL
HAG
TAL
VMB
FTM
EUG
BUR
PRK
SLM
JWI
AUR
JOL
BAK
GRY
OLY
PTM
MDO
ANIELK
SAG
WPB
KNXRLG
PBA WFT
MAC
LCL
SBR
MEL
WLO
GNCCTE
AXL
CCO JHN
VISBEU
HMO
RCY
YCC NPL
BSM
SHN
ATH
LIMHTL
YAZ
LFL KIL
CASCHY
SDT
GNV
YAX
EAU
SFE
TEX
DCA
RDCMRC
WAS
GSC
FSA DUL
JOP
FPC
GLN
BEL
PAS
OCL
ANA
PDR
CUM
STC
BCR
LMT
JKB
BNT
ANS
DANDOT
CHT
NAU
FSC JDN
HIK
FLR
NBM
In te rc e p t
8
6
LA
ORG
BRZ
4
2
10,000
ANC
100,000
1,000,000
MSA Population, 1990
Figure 6
10,000,000
Im p ro v e m e n t in R -S q u a re d
0.60
Change in R2 of Negative Exponential,
From Linear to 4th Power Models
JDN
0.50
0.40
0.30
0.20
0.10
0.00
10,000
GRR
FSC
SBR
LIM
BDR
STC
NHA
DOT
BRN
GNV
DAN
RAC
YAX
BRZ
DUL
WFT
TUC
SFE
WNC
DBQ
JWI
ATC
HMO
ANI CHT
DAV
BNT
BEL
HAG
KEN
GSC
NBC
WAS ATH
CMO
BSMCUM
YRK
EUG
BCR
RNO
TRN
PAW
SHN
SCP
TAL
WOR SYR
PAS
CHY
BRY MRC SGM BEUBDC
BAK
LFL
YCCANS
AUR
LEWYAZ
WLO
BNH RDG WCH
FSA FPC
SAT
TEX
RDC
ALT
SAC
HIK
GLN
SBY
LAN
ANC
KOK
SCZ
PBA
BRO
MAC
PTM ANASWO
FLR
SRA
NSH
KIL
DNB
ROC
SHR
FWA
RLG GNC
BIL CCOGAL
THA
LHM
MEMABQ
JHNLWL
VIS
FAR
KNK
COL
BIN
CHM
SIL CGA
LCN
APL DES
MNO
ELK
VAL
GRY
JOP
SAG
EAU
NLN
JKL
SLM
SXC
SAV
FYN
MDT
WAT
CVL
ORG
LMT
BLG TYL
GFM RCM
SRS
ERIFTM
DTN NO DEN
GFK
FRO
PDR
ELP
LAW
BUR
PRO
PME
RCYIOW
ROA
AXL AMR
PKE
ALG
OXN
HRT SF
HBG
BEV
JKBBAT
RVR
JOL
AUSBIRSLK
MCH
SMO
DCA
LYN
LBK
PCF
UTR
TDO
PRK
WAC
ABL
RKF
ALN
STM
DAB
BUFCTE CIN
ODS
JMS
BAL BOS
FWB
CDR
END VTX
HAW
MEM
SBN
PRV
NPL
LFI
TUS
CTN
OWN
LAN
FLT
PUE
TUL
STJ
KNX
WMP
TPA
LCL
NIA
PEN
OMH
NSH
GAD
LWK
MUS
FAL
CAS
PGHSDI
BXI
MOB GRY WPB
MSX MIL
MOD
VMB
LA
DET
INC MTGSNS
RLD
OCL
CNO
MUN
CSC
MNL
SIX
OLY
BRM
LRA
BUR
SEA
FCL
RCH
MDO
JMI
DCI
ORLFWO CLE
WIL
OKC
GBYHAL
PHX
JTNBGR
LEO
LVL
FIM
AUGSTC
CPX
MIA
SMA
EVN
LAR
TPK
JNC
NBM
NWK
AKR
LSV
SWV PEO
MAD
CSC
ANH
MONPOO
SDT
SPK
YNG
LAK
HTLKAL
DC PHL
INDSJS
DAL
NAU
KCM OAK
SNG
LACFAZ WWV
ASN
JC SWBALB
COS
ELM
BRD ANN LEX
CHI
STL
MEL
NFK
NY
LKL
HOU
MIN
TAC HON XHR
ATL
BOIVAN
BRC
MDC NRC
XPR
XBN
FTL
PATXPV
XMW
XCN
XDVXPT
XSE
XCL
XMM
XHO
XDL
XBO
XDT XPH
XSF XCH
100,000
1,000,000
MSA Population, 1990
Figure 8
10,000,000
Gini Indices of Census Tract
Densities and Population
1.0
0.9
0.8
Gini Index
0.7
0.6
0.5
0.4
LAR
RNO
LSV
PHX
YAZ
PUE
TUC
RCY
AMR PRO
RVR
GFM BLG
ODS
ANC SBR
GFK SNG
INC
DEN
CPX
NPLFCL
MDT RLD
FRO
MIA
BAK
BOI
LBK
BRD
MOD
SDI
BSM
FAR
SAC
STJ ABL
COS
ABQ
RDC
FWB
SEA
STC
POO
OMH
SIX
SPKVAL ELP WPB NO
BRY
END
SXC
BEL
EUGSNS
SJS
OXN
WCH
YAX
VIS
SATSF
GRY
MDO
PCF
CHM
DES
OKC
MEM
ORL
LWK WFT
DUL
BDR
LA
LFI
TAC
NY
TUL SLK KCM
LEX
BIL
DBQ YCC
CGA
AUS
DALHOU
MIN
FPC
DAV
CDRROA
PEN
RCH
SRA
CSC
TDO
LVL
MEL
JKL
MTG
ANN
VTXIOW
DCI LCLBXI EVN
LAC
SFE
RKFBEU
COL
STL
FWO
LAW
TUSWAC
MAD
JMS
GBY ERI AUR
IND
NFK OAK
PASWLO
MRC
DC
DET
TPK
CCO
PEO
SHR
APL
DAB
BUF
ALG
PBARCM
LAN
MOB
SAV
ROC
SYRALB
BRN
LRA
BINLCN SILTAL
SLM
FWA
CMO
BAL
SMO
SBNUTR
MEM WIL HON
MILNWK
TPA
WMP
LYN
ELM
PGH
FLR
PHL
EAU
GNV
AUG
KIL SRS
JNC
SCZ
LKL
CASJTN
RAC
OWN
PRV CINCLE
KEN
AXL
KAL
CSC
HAL
LFL
BIR
DTN
NSH
FTM
CHT
MNL
SCP
BAT
MAC
YNG
ANI
GRY
TEX
NIA
JOL
CTN
FYN
BCR
JOP
LEO
CNO
GRR
ALN
NBM
FAZ
KOK
HTL
FIM
SWB
OLY
MUN
LMT
JWI
MUS
MSX
HMO SAG HBG
KNK THA
CVL
FSA BNH
LIM
RLG
MCH
HAW
WOR
ATC
PRK
VMB
ATL
SBY
SMA
BGR
JC
ALT
WWV
GAL
SWV
DAN
KNX
BUR
CHI
DOT
HRT
JDN
CUM
WAS
AKR MON
TRN
SDT
MNO
RDGFLT
ANHBOS
ELK
BRZ
TYL
WAT
CTE
JKB
ANA
JMI
GNC
FAL
GSC
BEV
SGM
BNT
HAG
STC
PKE
BUR
BRM
OCL
SWO
ORG
PTMFSC
NAU
DCA
PME
LEW
BDC
NHA
GAD
SHNATH JHN
NLNSTMLAN
GLN
WNC
PAWYRK
NBC
ASN LWL
NSH PDR
HIK
LHM LAK
ANSDNB
BRO
0.3
CHY
0.2
10,000
100,000
1,000,000
MSA Population
Figure 9
10,000,000
100,000,000
A v g S tra ig h t-L in e D is t. to C e n te r, k m
Average Distance to Center of MSA
50
NAU
SBR
40
DUL
RVR
ANC
BRZ
30
20
10
0
10,000
BAK
SWB GNC
MON
JKB
ATL
TPA
CTE
DET
NO
GSC
NSH
SAC
MEL
VAL
ATC
ALB
STL HOUDC
GAL
LAK OXN WPB
KCM SEA
PGHSDI
STC JHN
RLG BIRSLK
DAL
JDN HTL
VIS
FPCHAW
SF
HBG
OAK
CSC
PHL CHI
LKL
MIN BOS
KNX
BXI
CSC
DAB
NFK
ROC
OKC
BEU
LRA
YAX
KIL
CCO
JKL
BRN
SAG
CPX
YRK
SNS
ALN
LCN CHT PDR
HIK
COL NWK
PHX
FWO
BAL
TUL
CLE
GLN BNT
AUR MOB SYR
ORG
UTR
LMT
IND DEN
DTN
JOL
RCH
DOT
MSX
POO
MRC
AUS
NPL
MAC
ANH
SHN
GRR
CNO
ORL
AUG
CIN
SRA
APL
NLN
DCA
WIL
FSA
FRO
NIA
DAV
RLD OCL
EUGPEN
PKE
SWV
RDC
WAS
MIA
FWB
SRS BAT TUCHON
DAN
PAS
GRY
MEM SAT
FSC
SFE
CUM
MIL
BEL
JOP
PMEBNH FTM
PEO
MEM
JMS
YNGGRY
LSV LVLHRT
TEX
WCH
EAU
LAN
BCR
WWV
FAL
TDO
TAC
SLM
SDT
ANS
LIMBRMSCZ
ELP
LAN
SHR
LHM
MDO
PRK
AKR
PRV
MTG
TAL
SJS
STC
FWA
ATHFCL
GNVRNO
ANA
AXL
HMOLEX
BUF
EVN
CTN
SCP
ERI
VMB
DES NHA
JWI
BSM FAZ
LYN
LCL BDR
WMP
CGA
RDG
SWO
ELK
SAV
CVL
PAW
MOD
OMH
ASN
HAL
PRO
TYL
MAD
OLY
RAC
YAZ
SPK
WOR
SMA
GFK
YCC
THA
WLO
SBY
TUSWAC
COS
LFL LEO
JNC
FYN
FLT
PCF
BLG
RCY
GAD
HAG
ANN
ANI
FAR
CHM
SGM
WFT
SIL SMO
ALT
MNL
DNB
BUR
JMI
ABQ
KNK
RKF
PBA
MUS
MNO
BIL
ROA
SXC
AMR
GFM
BUR
TRN BDC
CMO
LAW
NSH
GBY
KOK
DBQ
BRY
LAC
BRD
BGR
KEN
CDR
KAL
ALG
RCM
SBN
FLR BOI
JTN
VTX
WNC
LBK
CASCHY
OWN
MDT
ABL
WAT
DCI
LWL
TPK
IOW
PUE
SIX
FIM
PTM
BRO STM
MUN
LAR
ELM
END
SNG
ODS
LEW
LFI
JC
NBMINC
BIN
NBC
LWK
STJ
MCH
100,000
1,000,000
MSA Population
Figure 10
LA
NY
10,000,000
M e d S tra ig h t-L in e D is t. to C e n te r, k m
Median Distance to Center of MSA
60
NAU
50
40
ANC
GNC
30
BRZ
20
10
0
10,000
JKB
VAL
LAK
MON
SWB
TPA RVR
ATL
LA
OAK
RLG
OXN
BEU
HOU DET
HTLGAL
GSC WPB
JHN
NSH
SEA STL
MEL
HAW
SBR
CSC
FPC
SAG
DAL
KCM
ALB
PDR
DAB
VIS LKL
HBG
DC PHL
UTR
JDNCHT
BRN
KIL
CSC
SDI
CHI
YRK KNX
MIN
DOT CCO
SLK NFK
HIK ORG JOL
STC
BOS
BIR
DEN PGH
SAC
MSX FWO
BXI NLNATC
JKL
SHNANS
PHX
GLN
AUR
BNT
OCL
ORL
SF
SNS
SDT DCA
OKC
PKE
DUL
MIA BAL
RLD
LANLRA
SWV CPX
DTN
INDCIN CLE
LMT
RCH
FWB
CUM JWI
ROCPOO
GRY
ALNHON
SRS
CNO
COL NWK ANH
WIL
NIA MAC
AUG
MOBTAC
MEM
SYR
MEM
BRM
HMO
AUS
NPL
LSV
SAT
PME
PAS
FTM
JOP
PEN
LVL
HRT
FAL
YAX
PEO
LHM
MRC
TUL
ELP
PAW
JMSYNG
APL
GRR
WWV BDR
DAV
PRK
BAT
FAZLCN
BNH
SJS
SCZ
TUC
MIL
BAKAKR
EAU
BUF
SWOASN
CGA
LEO
SRASHR
BSMGAD
THA
FYN
FSC
FSA HAL
CTN
STC
BEL
WAS
RDC
TYL
WCH
SMA
NHA
OMH
PRV
VMB
PRO
EVN
ANN
WOR
ANA
AXL
FWA
DES
TDO
SLM
MAD
SCP
RDG
LAN
ATH
LEX
COS
FLT
BUR
LFL
FIM
JMI
LCL
DNB
ABQ
FRO
JNC
OLY
MDO
TUS
SPK
PCF
MNO
MTG
HAG
LYN
TAL
RCM
GNV
WFT
LIM
MNL
SFE
BCR
KNK
DAN
WAC
JTNBGR
ELK
GBY
EUG
WMP
SGM
ANI
WLO
ROA
SAV
MUS
BUR
MOD
ERI
AMR
SBN
FLR
RNO
BRY
TRN
KAL
STM
RCY
BOI
LAC
BDC JC
WNC
BRD
PBA
GRY
RKF
SMO
CDR
BRO
LWL
ALG
TEX
CVL
FCL
NSH
YCC
WAT
LBK
GFM
SIL
CMO
LEW
FAR
CHM
ALT
TPK
NBM
LAW
BLG
RAC
SXC
DCI
END CHY
ELM
SIX
INC
YAZ
BIL
LFI
PTM
SNG
PUE
ABL
KOK
MDT
ODS
KEN
SBY
IOW
LWK
DBQ
BIN
MUN
STJ
LAR
MCH
CAS
GFK
VTX
OWN
NBC
100,000
1,000,000
MSA Population
Figure 11
CTE
NO
NY
10,000,000
Gravity Measure (Linear)
1,000
In d e x V a lu e
NY
100
CHI
LA
PHL
DET
DC
CLE
PGH
NWK
BOS
BAL
DEN
ANH
MIL
MIN
SF
NO SJS
NAUHOU
OAKDAL
PHX
KCM
SDI
CIN
BUF COL
SEASTL
POO
MIA
NFK
OKC
LVL
HON
IND
ROC
MSX
FWO
SAC TPA ATL
SAT
HRT
RCH
MEM
TDO AUSDTN
BDC OMH
SLK
SYR
TUL
PRV
ABQWIL
WPB
ORL
AKR
MON
LSV
ALB
ALN
NHAFRO
STC
SMA
GRR
CTE
BIR
LAN
WCH
SWB
MOB
SPK
YNG
GNC
FLT
JKL
WOR
GRY
AMR SAV
ANN
RVR
SBN
TUC
FWA
NBM
RLG NSH
TAC
DAV
PAW
TRN
DES
ELP
COS
LBK
RKF
RDG
LHM
CSC
OXN
MAD
INC
BAT
CGA
ERIUTR
MOD CSCGSC
LEX
PUE
PEO
LWL
SHR
EVN
MCHWAT
STM
LYN
LAN
KNX
NBC
KAL
LEO
BEU
FLR
HBG
SMO APL
JMS
LAK
BAK
VAL
CTN
PME
SAG
ANC
CPX
CNO
HMO
BRO
AUR
EUG
MTG
LFI TPKGBY
CVL
BNH
ATC
HAL
FTM
RNO
ABL
ROA
YRKLRA
BDR
SGM
SCZ
JOL
BRN
SIL
NLN
STJ DCI
GAL
WAC
MACSNS
AUG
BRD
KEN
BOI
CDR
MUS
SIX
ALT
NIA
LAR
HAW
MUN
PRO
BIL
SBR
CHM
IOWODS
BRM
DUL
WLO
PKE
MEL
RAC
LFL
FAR
FYN PEN
FCL
TAL
LIM
MNL
WFT
LAW
ANI
DBQMDT
JKB
DAB
MEM
SWV
LKL
SXC
WWV
DNB JHN
WNC
SLM
THA
SWO
ORG SRS
PRK
LCL
ELMALG
BNT
FPC
JMI
BLG
FIM
BCR
GFM
CMO
BRY
DAN
LAC
GRY
LEW
MNO
BUR
TYL
NSH
ASN
TUS
SNG
SCP
JWI
PBA
AXL
SRA
FSA PDR
HAG
KOK
ATH
BIN
SHN
OWN
BXI KIL VIS
ELK
SFE
BGR
WMP
VMB
GFK
KNK
CHT
GAD
RLD
OCL
TEX
RCY
FWB
LMT
GNV
OLY
JTN
VTX
CUM
CASCHY
RCM
EAU
PTM
SBY
PAS
PCF
YAX
CCO
YAZ
STC
FAZ JDN
BRZ
JNCMRC
ANS
YCC
MDO
ANA
FSC
DOT
RDC
JOP
FAL
NPL
DCA
WAS
BEL
BSM BUR
LCN
LWK
GLN
HTL
HIK
SDT
END
JC
10
1
10,000
100,000
1,000,000
MSA Population, 1990
Figure 12
10,000,000
100,000,000
Gravity Measure (Exponential)
100.0
In d e x V a lu e
NY
10.0
JC
CHI
1.0
0.1
SF
NWK
BOS
PHL
MIL
NO
CLE BAL
DC
PGH
OAK
ANH
DET
BDC
HON
SJS
BUF
DEN
NBM
MIN
ROC
MCH
NAU
HRT
FLR
PAW
CIN
TRN
TDO
SYR
NBC AMR SAV
POO
COL MIA SDI
LWLRDG STC
PUE
ANN
OMH
PRV
CVL
NHA
ABQ
WIL
PHX
ERI
LVL
KCM
OKC
SBN
INC
LFI
LHM
WOR AKR
LAR
MSX
SLK NFK
DAL HOU
SAC SEA STL
STM
IOW KEN
LBK RKF
SPKLAN
MEM
UTR
RCH
WAT
LSV
SWB
ALT
DTN
AUS
IND
BIL
SMA ALN
PME
FWA
SAT
WCH
STJ
CGA
ALB
BRO
DCI
FLT
EVN DAV
DBQ MUN
SIX
ABL
DES
FRO
TPK
MOD
LEX
ELP
GRR
TUL
MOB
MAD
VAL
CHM
KAL
SIL
LAN
FWO
GBY
BRN
GRY
COS
MDT
ANC
NIA
SMO
BNH
YNG
ELMLAW
SCP
AUR
ODS
APL
TPA
LYN
FAR
RAC GAL
BDR
OXN WPB
SNS
RNO
PEO
TUC
SHR
LEW
LEO
HMO
CPX
GFK
SBR
EUG
CSCBAK
CDR
GFM
THA
LIM
GRY
ATC
ANI
SXC
BUR
MUS
PRO
BEU
TAC RLG MON
SGM
CTN
SAG
FCL
FIM
WLO
BLG
WAC
ORL
BOI
JMS
YRKCSC
BRY
HBG
WFT
BAT
BIN
LAC
ROA
MTG
BRD
HAL
OWN
CMO
SHN
NLN
ATL
WNC
BIR
JMI
MAC
DUL
JWI
TAL
GNC
KOK
SNG
BRMSCZ
WMP
CASVTX
ALG
MNO
PRK
NSHCTE
HAG
PKEHAWJOL LAK GSC JKL
VMB
MNL
PBA
PTM
SFE
TUS
WWV
SBY
SWO
BCR
KNK
CNO
JTN
RCY
BNT
LFL
TYL
JHN
NSH
DAN
LRA
RVR
ATH
BGR
YAZ
TEX
LCL
DNB SLM
AXL
KNX
BSM
RCM
AUG
CHY
YAX
JDN
PEN
ORG
FYN
DAB
SWV
FTM
SRS
FSA
FWB
PASLCN
RLD
GAD
CUM
ASN
CCO
EAU
KIL
VIS
MEL
STC
LWK
ELK
FPC
SRA MEM
LKL
MDO
CHT
BXI
BEL
GNV
WAS
PCF
LMT
PDR
END
FAZ
YCC
JKB
FSC
FAL
OLY
MRC
DCA
RDC
JNC
DOT
JOP
GLN
OCL
ANA
BUR
SDT
HTL
ANS
NPL BRZ
LA
HIK
0.0
10,000
100,000
1,000,000
MSA Population, 1990
Figure 13
10,000,000
Moran's I Spatial Correlation
3.5
Prelim., Using Quadratic Approx. to C
SBR
3.0
GLN
C o e ffic ie n t
2.5
2.0
1.5
1.0
0.5
0.0
PKE
LAN
ALN
YRK
NLN LHM
LFL
SF
ATC
DNBDUL
HRT
JHN
WIL
ATL
SCP
PME
WOR
JOP
DCA
UTR
SEA
GAL
SYR ALB
RDG
HBG
KNX
AUG
MEM
BAL
NIA LWL
JOL
ANI
CCO
CUM
HAW
WASEAU HTL SAV
ROC
SWV
MSX
FSC
BUR
FWA
MIN BOS
ELK WAT
OXN
SWO
RDC
PEO
LIM
PRV CIN
FAZ
JMS
BRO
CPX
NSH
GRR
MON
ANS
PDR
BRZ
KNK
GSC
HAG
TAL
SBY
WWV
ASN
NSH
STC
RCH
SLM
BNH
MTG
CSC SWB
NWK
BDR
YAX
JKB
PHL
CHM
PAW
FAL
OCL
FSA
BIR IND
EUG
AXLJMI
JTN
HON
FTMSAG
BSM
KIL
WMP
ERI
BDC
BEL
VIS
FLTLRA
KCM OAK
EVN
NHA
WAC
AUS
MNO
RAC
MAC
ANN
PTM
RCM
BIN
MOB
SRS
YAZ
WLO
ORG
BGR
LCN
CTN
LAK
JNC
POO
GNC
MIL
FPC
BXI
LAN
SXC
HIK
SGM
BAT
GFM CMO
KEN
LKL
BRN PEN
LEX
MEM COL
FWB
SIL
DAB
SFEJDN
NBC BRM
PBA WNC
AKR
SDI
VTX
SJS PGHSTL
CNO
LVL
CSC
YNG
BUF
MUS
FIM
RLG DTN
RVR
ATH
RKF
STM
DES
OMH
ALG
LEO
BEU
SMA
MOD
FRO
RNO
LCL
DET
SIX
LFI
TAC
FCL
CTE
APL
LEW
ROA
NPL
PRO
ANA
GNV
BAK
YCC
TDO
MAD
NO
KOK
GAD
MCH
BRD
GFKOWN
MIA
CLE
JKL
NAU
DCI
MNL
SLK
ORL
TUL
LBK
SAT
BCR
PAS
ALT
CHI
SAC
RLD
MRC
DAL
DAV
SNS
THA
AUR
SBN
STJ
SRA
SCZ
DBQBUR
WCH
TEX
VAL
CGA
HAL
DEN
SPK
JWI
MUN
ANH HOU
SHR
PRK
ANC
GRY
TYL
SMOHMO
SDT
LACBLG
NFK
IOW
CAS
TUS
FAR
MDO
SHN
CDR
TPA
VMB
OKC
GRY
LAR
BRY
OLY
NBM
PUE FLR
CHT KAL
END
ABQ ELP LSV
DAN
ELM
PCF TPKGBY
INC FYNTRNMELSTCJC TUC WPB
MDT
LAW
PHX
SNG
BIL LMT
AMR
ABL
WFT
BOI
COS
FWO
ODS
LWK
CVL
CHY
RCY
DOTBNT
LA
NY
LYN
DC
(0.5)
10,000
100,000
1,000,000
MSA Population, 1990
Figure 14 (Preliminary)
10,000,000
1st Principal Component of "Sprawl"
P rin c ip a l C o m p o n e n t S c o re
Unadjusted Data
100,000
NY
JC
10,000
HON
SF
CHI
NWK
1,000
100
10,000
LA
BOS
BAL
ANH
OAK
SJS MIA
NBM
NOMIL
BDC
DC
TRN
CHM
BUF
CLE
SDI
LWL
ANN
NAU
PRV
MCH
NHA
DET
RDG
WOR
SBR
HRT
WAT
SMA
OXN
LSV ROC
SNS STC
PGH
INC
ELP
PRO
ERI LHM
SAC
MSX
DEN
SEA
PHX
SLK
POO
MAD
CIN
ALN
TDO
RNO
NFK
LAN
FRO
STL
MIN
VAL
COL
DAL
BRO
RAC
SYR
HOU
BRN CPX
WIL
MOD ABQ
SCZ
OMH
ALTFAR GAL
PUE
BDR
SWB
ALB
LVL SAT
MEM
KAL
UTR
WPB
AKR
TPA RVR
LBK
SAV
TUC
RKF
LEX
SPKFLT BAK
GRR
HMO
DTN
GRY
MON
AMR
ANC
AUS
APL
IND
FWO
SBN
HBG
DES
KCM
GBY
DAV
PMEBNHATC
TAC
COS
BOI
ORL
FCL
SIL
LAN
WCH
DCI TPK
EVN
RCH
FWA
ODS
EUGPEO
CTN
CDR
SAG
YNG
NSH
JKL
CGA
WLO
YAX
SRS
ABL
WAC
GRY
CVL
JWI
SRA
DNB
ROA
TUL
SLM
JMS
JDN
MEL
ATL
SHR
BEU
CSC
RLG
SHN
DAB
VIS
TAL
BIR
YRK
BAT
FWB
THA
MOB
MTG
DUL
RLD
NLN
SMO
CSC
ELK
HAL
NSH
LIM
FTM
JMI
SWV
WFT
FYN
MEM
MAC
LRA
JHN
ALG
PEN
BXI
CCO
GNV
EAU
CTE
GLN
SWO
WWV
LFLFPC
KIL HAWAUG
LCL
MNL
PRK
LKL
STC
FSA
HTL
GSC GNC
BNT
AXL
TYL
JOP
LYN
CNO KNX
PDR
ATHBRZ
CHT
ASN
DCALMT
JKB
CUM DOT
OCL
HIK
100,000
1,000,000
MSA Population, 1990
Figure 15
PHL
10,000,000
1st Principal Component of "Sprawl"
P rin c ip a l C o m p o n e n t S c o re
Population Adjusted Data
25,000
New York
20,000
15,000
Jersey City
10,000
5,000
0
(5,000)
10,000
Honolulu
New Bedford
Champaign
Newark
Chicago
Manchester Ann
Trenton
Lowell
Arbor
Bridgeport
Philadelphia Los Angeles
Pueblo
Waterbury
Altoona
Lincoln
Reading
Fargo
Racine
Santa
Barbara
Provo
Boston
Decatur
IL
Erie
Brockton
Odessa
Reno
Galveston
Santa
Cruz
Worcester
NewMilwaukee
Orleans
San Jose
Abilene
Salinas
New Haven
Baltimore
Boulder
Brownsville
Lawrence
MA
Sharon
Amarillo
Miami
Charlottesville
Greeley
Kalamazoo
Buffalo
Janesville
Waterloo
Lubbock
Topeka
Oakland
Anaheim
Albany
Terre
GA
Haute
Green
Bay
Madison
Jamestown
Cedar
Rapids
Savannah
Stockton
Wichita
Falls
Springfield
Nashua
Fort
Collins
IL
Anchorage
Glens
Fort
Walton
Falls
Portland
Beach
ME
Springfield
MAVegas
Boise
Lancaster
Richland
Rockford
Yakima
South
Corpus
Bend
Modesto
Christi
Eau
Jackson
Claire
Elkhart
Lima
Waco
MI
Hamilton
Utica
Cumberland
Steubenville
Danbury
Monroe
Vallejo
Alexandria
LA
Joplin
Binghamton
ElToledo
Paso
Oxnard
Parkersburg
Wheeling
Lexington
Albuquerque
Appleton
Lynchburg
ColumbusGA
Roanoke
Spokane
Lake
Tyler
Charles
Las
Decatur
AL
Chico
Evansville
Providence
Dothan
Benton
Harbor
Eugene
Atlantic
City
Tallahassee
Athens
Fort
Smith
Biloxi
Davenport
Cleveland
Gainesville
Wilmington
DE
St.
Houma
Cloud
Duluth
Peoria
Springfield
Sarasota
MO
Flint
Allentown
Fresno
Lafayette
Huntsville
Salem
LA
OR
Des
Moines
Hartford
Syracuse
Longview
Bakersfield
Clarksville
Charleston
Johnstown
New
London
Colorado
WVA
Springs
Asheville
Fort
Wayne
Omaha
Brazoria
Visalia
Fort
Montgomery
Pierce
Canton
Fayetteville
Killeen
Shreveport
NC
Lansing
Macon
Saginaw
Rochester
NY
Portsmouth
NH
San
Diego
Beaumont
Santa
Rosa
Middlesex
Daytona
Beach
Wichita
Akron
Scranton
Ocala
Jackson
MS
Gary
Fort
Myers
Tucson
Huntington
Melbourne
Harrisburg
Salt
Lake
City
Grand
Rapids
Washington
Pensacola
Youngstown
Tacoma
York
Hickory
Albany
NY
Nassau
Mc
Allen
Portland
OR Pittsburgh
Charleston
SC
Palm
Louisville
Beach
Columbia
Mobile
SCWest
Austin
Sacramento
Augusta
Lakeland
Baton
Rouge
Memphis
Cincinnati
Norfolk
ColumbusOH
Dayton
San
Antonio
Denver
Little
Rock
Monmouth
Chattanooga
Tulsa
Richmond
Seattle
Raleigh
Johnson
City
Phoenix
Jacksonville
FL
Orlando
Indianapolis
Greenville
Birmingham
Fort
Worth
Knoxville
Saint
Louis
Detroit
Minneapolis
Dallas
Kansas
City
MO
Nashville
Tampa
Greensboro
Charlotte
Houston
Riverside
Atlanta
100,000
1,000,000
MSA Population, 1990
Figure 16
San Francisco
10,000,000
Theil Indices of Census Tract
Densities and Population
5.0
WPB
Theil Index
4.0
3.0
2.0
1.0
0.0
10,000
LAR
PUE
PRO
RNO
LSV
OXN
SJS PHX
MIA
RVR
MEL
SBR
SEA
TUC
DEN
SDI
LA
ABQ
ANC
ODSNPLAMR
SF
TAC
FRO
ORL
FCL
SAC
SNS
GRY BOI
COS
RCY BEL
CPX
NO
VAL
BRD VIS
MOD
POO
GFM WFT
INC
SRA MEM BAK
BDR
YAZ
NY
OMH
LBK
SPK
SNG
ABLRLDYAX
SLK
BLG
SAT OAK HOU
BSM
STCELP
EUG
GFK
MDO
WCH
FWB
STJ
FAR DUL
FPC BEU
MDT
TUL OKC
DAL
DES
PEN
SXC
SIX
RDC ROA
KCM
PCF
CGA
END LWK BRY
SLM
MEMFWO
NFK
LFL
AUS
ANH
MIN
DC
MRC
DAV
CSC
STL
FTM
PBA ALG
DAB
TDO RCH
JKL
UTR
LVL
LFITPK
SFE
LEX
CDR
CHM
SRS
DET
SCZ
WMP
RKF
BUF COLNWK
PHL
MOB
MTG
KIL
AUR
VTX
SAV
YCC
CCO
HON
LCL
LRA
LAC
LAW
TPA
LCN
BXI
MAD
DBQ
WLO
SHR
PAS
JC
CAS
BRN
CHI
ANN
JMS
BIL
IND
TUS
BAL
IOW
GBY
MAC
SBN
EVN
RCM
SILSMO
ERIPEO
ALBPRV MIL CLE
DCI WAC
PGH
SYR
WIL
CIN
BAT
APL
JOL
ROC
FWA
CTN
DTN
BRZ
FYN
KEN
BIN
LAN
GRY
LYN
CHT
LKL
ELM
CMO
TAL
AXL
GAL
KAL
LEO AUG
NSH
JWI
SCP
FLR
BIR
CSC
HTL
CNO
EAU
NIA
FAZ
TEX
RAC
HAL
GNV
JTN
YNG
MUS
HBG
ALN
SWB
THA
HMO
MNL
GRR
LMT
ATL
ANI
RLG MON
KOK
ATCSAG
OWN
BCR
JOP
NBM
MSX
BNH
JNC
KNX
OLY
MUN
SBY
CVL
HAW
FSA
CUM
FIM
LIM
KNK
PRK
VMB
TRN
NAU
SMA
BOS
WWV
WOR
AKR
ALT
CTE
DAN
FAL
GSC
HRT
WAS
JKB
GLN
MCH
BRM
JDN
DOT
TYL
STC
OCL
RDG
GNC
SWV
BGR
MNO
BEV
FLT
DCA
WAT
JHN
SDT
BUR
BDC
LEW
PKE
NHA
SGM
HAG
SWO
ELK
PAW
JMI
BNT PME
SHN
BUR
ATH
PTM
ORG
ANA
STM
NLN
FSC
GAD
LWL
LAN
YRK
LAK
WNC
ASNHIK
NBC
NSH
DNB
ANS
PDR
BRO LHM
CHY
100,000
1,000,000
MSA Population
10,000,000
100,000,000
F i rs t D e c i l e o f T ra c t D e n s i t i e s
First Decile of Census Tract Densities
And Income
10,000
JC
NY
1,000
100
10
MIA
LA
CHI
ANH
SF
SJS NAU
OAK
PHX
BDC
LAR
NWK
DEN SDINBC
WPB
BOS
CLE
BRD
BRO
SLK
PHL
NFK
HON
ELP
LAK
TRN
NHA
SEA
DC
PAWDET
LWL
MEL
LSV
BUFSRA
HOU
SGM
PRV
ABQ
DAL
CHY
MON
PRO
MSX
NOTPA
NSH
ORL
LHM
NBM
BAL
SMA
AKR
FWO
WAT
MIL
FLR
SBN
GAL
DNBOXN
CPX
MCH
LEW
ODS
TAC
SCZ
GRY
HRT
DAB
PGH
CIN
FLT
ATL
OMH
HMO
LEO
WOR
SAT
KEN
BDR
POO
BRM
DTN
SAC
CAS
FTM
LAN
WIL
FYN
FIM
STL
RVR
WNC
VALAUR
MIN
RAC
BEV
NLN
MEM
YNG
TPK
PDR
STC
BAT
RDG
CTN
KAL
GSC
BOI
ALN
LVL
LFL
MEM
SRS
PME
VMB
TDO
GRR
ORGJOL ANC
FPC
RNO
WFTMUS
PTM
RLG
IND
BNT
BEL
BUR
CTE
NPL
NIA
ATC
COL
KCM
YRK
MAC
INC
JKL
PEN
ALT
ASN
GNC
BRN
ANN
BGR
TUC
ELK
HBG
ROCSBR
GBY
ROA
MUN
HAG
ANI
PKE
SWO
BEU
AUS
CSC
KNX
SWB
AUG
SAV
HIK
MNO
SYR
OLY
RKF
ALB
BIN
AMR
LAC
COS
ANS
ALG
HAL
FRO
ERI
MAD
ELM
RCH
CGA
MOD
OKC
FCL
SAG
DES
NSH
DAV
BIR
JMI
BUR
SBY
FWA
ABL
TYL
APL
MNL
PUE
BNH
LAN
KIL
LKL
CNO
JWI
GNV
ANA
SLM
BRZ
ATH
CSC
GAD
WCH
SIL
SWV
JHN
CUM
JNC
LRA
BRY
SPK
TAL
THA
BXI
OWN
SHR
LBK
BCR
SHN
FSC
LIM KOK
SMO
WWV
LFI
HAW
JTN
CDR
DOT
WAC
MOB
TUL
LCL
JDN
FAL
JMS
PCF
UTR
JKB
LYN
KNK
DCA
LEX
PBA
WLO
PAS
EVN
TUS
FWB
SDT
PEO SNS
YAX
VIS
LWK
CMO
FAZ
LMT
PRK
DCI
CHT
RCM
CHM
MTG
STJ
OCL
IOW
YAZ
SCP
WMP
GLN
DBQ
CCO
CVL
JOPDAN
SIX
STC BIL
VTX
GRY
MRC
YCC
AXLFSA
LAW
TEX
SFE
EAU BAK
MDO
EUG
HTL
WAS
MDT
RLD
SXC
FAR
LCNDUL
SNG
BLG
GFK
END
GFM
RCY
RDC
BSM
STM
1
10000
20000
30000
40000
Median HH Income
50000
60000
F i rs t D e c i l e o f T ra c t D e n s i t i e s
First Decile of Census Tract Densities
And Age of Housing Stock
10,000
NY
1,000
LA
SF
100
JDN
10
MIA
CHI
SJS
PHX
LAR
SDI
NW K
DEN
W
PB
BO S
BRD
SLKELP
PHLCLE
N NFK
NHA
DCHO
SEA
SRA
MEL
LSV
BUF
HO
U
PRV DET
ABQ
DAL
PRO
T
PA
NOCHY
O RL
NBM
BAL
SMA
W
AT
MIL
SBN
MCH
LEW
O DS
SCZCPX
HRTF LT O MH
PG H W O R
CIN
ATLDAB
SAT
PO O
BRM
DTNLAN
SAC
CAS
F TM
F IM
F YN
STL
W
NC
MIN
NLN
YNG
T
PK
PDR
STC
BAT
RDG
KAL
G SC
BO
I RLG MEM F PC
ALN
LVL
LF L
MEM
PME
T DOCTN
GIND
RR
RNO
W
FT
PTM
BNT
BELCTEJKLPEN
BUR
NPL
ATC
ANC
CO
L
YRK
KCM
MAC
MUS
INC
ALT
ASN
NC
BRN
R
RO
C HAG
HBG
ELK
T UC
G
BY G
RO
A SAV
MUN
ANIMNO
SW BG
O
BEU
CSC
AUS
KNX
SW B
AUG
SBR
HIK
ALBERI
SYR
RKF PKE
O
LY
BIN
LAC
AMR
CO
S
ANS
ALG
HAL
F
RO
MAD
ELM
RCH
CG
A
MO
D
O
KC
F
CL
SAG
NSH
DAV
SBY
JMIPUE
BUR CNO
BIRANA
FDES
WA
APL
T YL
BNH
LANABL
MNL
JW
I SW
LKL
KIL
G
SLMLRA
ATH
CSC
W
CH
G
AD
VSIL
JHN
JNC
SPK
T NV
ALBRY
T HA
O WSHR
N LBK MO
BXI
BCR LIM
SHN
F
SC
SMO
KO
K
W W V CUM
I AC
JT
N
CDR HAWLFW
DO
T
B
T
UL
LCL
F
AL
JMS
UTR
PCF
KNK
LYN
JKB
LEX
DCA
W
LO
EVN
PBA
PAS
SNS
T
US
FWB
PEO
SDT
YAX
VIS
LW
K
CMO
DCI
PRK
LMT
F
AZ
CHT
RCM
CHM
G
STJ
O CL
IO W MT
YAZ
W MP
G LN
DBQ
CVL
C
CO
JO P DANSIXBILSCP
STC
F SA
VTX
G
RY
MRC
YCC
AXL
LAWT EX
SFE
EAU
EUG
MDO
W AS
HTL
MDT
RLDBAK
SXC
F
AR
LCN
DUL
G F K SNG BLG
G F M END
RCY
RDC
BSM
1
1940
1950
1960
Mean Year Built, 1990 Census
1970
1980
P e rs o n s p e r S q u a re K ilo m e te r
Metropolitan Area Average Density
10,000
Jersey City
New York
1,000
100
10
Chicago
Anaheim
Nassau
Los Angeles
San
Francisco
Newark
Trenton
Oakland BostonPhiladelphia
Honolulu
Providence
San Cleveland
Jose
Lowell
Fort
Lauderdale
Miami
Milwaukee
Detroit
Middlesex
Waterbury
Buffalo Norfolk Baltimore Washington
New Haven Monmouth
NewHouma
Bedford
Tampa
Lawrence MA
Brockton
Hartford
Akron
Bridgeport
Salt LakeCincinnati
City Pittsburgh
Hamilton Flint ElGary
Houston
San
Diego
PasoDE Dayton
Dallas
Atlanta
Galveston
South
Bend
New
Fort
Orleans
Worth Minneapolis
Racine
Nashua
Wilmington
Santa
Cruz
San
Antonio
Danbury
Fitchburg
Sarasota
Grand
Rapids
Youngstown
Kenosha
Allentown
Seattle
Springfield
MA
Saint
Louis
Toledo
Lancaster
Denver
West Palm
Memphis
Beach
Orlando
Fayetteville
NC
Louisville
Fort
Myers
Albuquerque
Indianapolis
Canton
New
London
Kalamazoo
Ann
Arbor
Reading
Melbourne
Atlantic
City
ColumbusOH
Green
Bay
Raleigh
Oxnard
Rockford
Portsmouth
NH
Pittsfield
Tacoma
Portland
ME
Erie
Stockton
Jacksonville
Rochester
Charlotte
FL
NY ORCity MO
Elkhart
Daytona
Beach
Baton
Rouge
Portland
Scranton
Omaha
Worcester
Kansas
Columbia
SC
Madison
Greenville
Muncie
Boulder
Huntsville
Harrisburg
Richmond
Topeka
York
Lewiston
Brownsville
Vallejo Syracuse
Vineland
Benton
Austin
Bloomington
IN Harbor
Greensboro
Fort
Wayne
Albany
NY Sacramento
Hagerstown
Athens
Roanoke
Savannah
Lincoln
Lansing
Altoona
Lubbock
Modesto
Santa
Rosa
Steubenville
Mc
Allen
Nashville
Macon
Cedar
Rapids
Lexington
Gainesville
Elmira
Monroe
Bangor
Phoenix
Corpus
Christi
Des
Moines
Birmingham
Appleton
Oklahoma
City
Saginaw
Fort
Pierce
ColumbusGA
Knoxville
La
Crosse
Binghamton
Lakeland
Burlington
Jackson
VT
MI
Bryan
Chattanooga
Pensacola
Davenport
Spokane
Sheboygan
Decatur
IL
Charleston
WVA
Manchester
Tallahassee
Jacksonville
Boise
NC
Charleston
SC
Janesville
Springfield
Shreveport
MO
Burlington
NC
LimaWaco Evansville
Anniston
Owensboro
Peoria Little
Gadsden
Colorado
Springs
Lawrence
KS
Sharon
Rock
Kokomo
Champaign
Beaumont
Mobile
Wheeling
Jackson
MS
Panama
City
Albany
GA
Rochester
Columbia
MN
MO
Tyler
Wichita
Augusta
Springfield
IL
Asheville
Iowa
City
Lake
Charles
Fort
Sioux
Walton
Falls
Beach
Johnson City Tulsa
Parkersburg
Waterloo
Montgomery
Salem
Huntington
OR
Florence
SC
Dubuque
Kankakee
Mansfield
Jackson
TN
Brazoria
Johnstown
Hickory
Cumberland
Clarksville
Santa
Jamestown
Anchorage
Odessa
Provo
Abilene
Terre
Haute
Ocala
Killeen
UticaBarbara Fresno
Dothan
Wilmington
NC
Tuscaloosa
State
Charlottesville
College
Chico
Biloxi
Bloomington
St.
IL
Cloud
Dansville
Salinas
Joplin
Lawton
Florence
AL
Decatur
AL
Amarillo
Sherman
Sioux
Alexandria
YubaCity
City
LASmith
St.
Joseph
Fort
Pine
Bluff
Williamsport
Riverside
Las Vegas
Merced
Longview
Victoria Wichita
Eau
Claire
Lafayette
LA
Texarkana
Lynchburg
Falls
Naples
Wausau
Tucson
Fort
Collins
Glens
Falls
Bakersfield
San
Angelo
Visalia
Fayetteville
AR
Eugene
Bellingham
Santa
Fe
Fargo
Enid
Medford
Pueblo
Richland
Yakima
Billings
Reno
Laredo
Redding
Las
Cruces
Greeley
Duluth
Rapid
City
Great
Falls
Cheyenne
Bismarck
Grand Fork Yuma
Paterson
Casper
1
10,000
100,000
1,000,000
MSA Population, 1990
10,000,000
Density of Tract Containing Median HH
MSA Tracts Sorted by Density
P e rs o n s P e r S q . K M
10,000
JC
NY
1,000
MIA
SF
SJS
LA
ANH
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OAKNAU
PHX
BDC
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STM
DEN
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BOS
BRD
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CAS
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MIN
RAC
BEV
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PDR
MEM
YNG
AUR
STC
BAT
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CTN
KAL
GSC
BOI
ALN
LFLFPC
LVL IND
MEM
SRS
VMB
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TDO
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RLG JKL
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BEL
BUR
BNT PME
NPL
CTECOL
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MAC
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BRN
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GBY
ROA
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SWO
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CSC
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KNX
SWB
AUG
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SAV
SBR
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R
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SYR
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SAG
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BIR
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ABL
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BRZ
SLM
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CSC
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SWV
JHN
CUM
JNC
LRA
BRY
TAL
SPK
THA
BXI
OWN
BCR
LBK
SHR
SHN
LIM
FSC
KOK
SMO HAW
LFI
WWV
JTN
CDR
DOT
MOB
WAC
LCL
TUL
JDN
FAL
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MS
UTR
PCF
LYN
KNK
DCA
LEX
EVN
PBA
PAS
WLO
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FWB
TUS
SDT
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YAX
VIS
LWK
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DCI
LMT
FAZ
PRK
CHT
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CHM
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STJ
OCL
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SCP
WMP
GLN
DBQ
CVL
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DAN
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SIX
STC
FSA
VTX
BIL
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YCC
AXL
LAW
TEX
SFE
EAU
EUG
MDOHTL
WAS
BAK
MDT
RLD
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FAR
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BLG
ENDGFK
GFM
RCY
RDC
BSM
NBC
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100
10
1
10,000
100,000
1,000,000
MSA Population, 1990 (Log Scale)
10,000,000
U n d a d ju s te d R 2 , 4 th P o w e r M o d e l
Fit of Linear, Fourth Power SUE Models
1.00
0.80
0.60
0.40
0.20
YAZ
GFM
BSM
CMO
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END
FSC
AT H
DCI
BUR
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JMI
LCN
MDT
TRCY
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BELT AL T EX
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LA
SWB
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VT
X
LAR
ST M ANH
MEL
WPB
JC
0.00
0.00
0.20
0.40
0.60
Undadjusted R2, Linear Model
Figure 6
0.80
1.00
D e n s ity o f W e ig h te d M e d ia n T r a c t
Compare Galster et al. Results to Dens
Of Tract Containing Median Person
100,000
NY
10,000
SF
LA
CHI
MIA
PHL
DET
DEN
DC
HOUDAL
BOS
1,000
AT L
100
0
10
20
30
40
50
Galster et al. Total Sprawl Index
Figure 7
60
70
Nine Causes of Sprawl (Richard K.
Green)
Rent gradient
 Demographics
 Growing affluence
 Transportation changes
 Government service differentials
 Racial discrimination and segregation
 Plattage and plottage
 Tax policy
 Land use regulation

More causes of sprawl
Economic structure
 The degree of monocentricity
 Opportunity cost of land in rural uses

Some Opinions








American Farmland Trust, Farming on the Edge
Bank of America et al., Beyond Sprawl
Al Gore, several recent speeches
Peter Gordon and Harry Richardson, “Are Compact Cities
a Desirable Planning Goal?”
Reid Ewing, “Is Los Angeles Style Sprawl Desirable?”
John Norquist, The Wealth of Cities
Richard Moe, Growing Smarter
Many more, type “sprawl” into your browser and stand
back.
Some Literature








Real Estate Research Corporation, The Costs of Sprawl
(1974)
Critiques of RERC by Altshuler (1977) and Windsor
(1979)
Downs, New Visions for a Metropolitan America
Helen Ladd, “Population Growth, Density, and the Costs
of Providing Public Services” (1992)
David Mills (1981)
Richard Peiser (1989)
Brueckner and Fansler (1983)
Burchell and Listokin, others at Rutgers, on “fiscal impact
analysis” (various), The Costs of Sprawl Revisited (1998)
Highly Tentative Conclusions
Transit infrastructure has little effect on density
per se.
 More mass transit is associated with longer
commutes.
 Higher densities lower commutes, ceteris paribus.
 Will these results hold up to further work?

Some Next Steps
Alternative sprawl measures (e.g. AHS new
housing density)
 Better measures of transit infrastructure
 Model other outcomes that reflect potential costs
and benefits of sprawl
– environmental outcomes
– public service costs
– racial and economic segregation
 Endogeneity, endogeneity, endogeneity

Percent of Metro Population and
Employment in Central Cities
Select Services
Retail Trade
1980
1948
Wholesale Trade
Manufacturing
Population
0%
20%
Source: O’Sullivan, Kain, Census
40%
60%
80%
100%
Why Do We Observe Decentralization?
Standard Urban Economics (SUE) model: rising
incomes, falling transport costs
 “Blight Flight” or Amenities/disamenities models
 Public policies
 Change in technology, shift to service economy,
incubator processes?

The U.S. is Well-Endowed with Land
The U.S. has 7% of the world’s land area.
 But 13% of the world’s cropland is in the U.S.
 The U.S. has roughly 10 acres of land for every
inhabitant.

Population Density, Selected Countries
Hong Kong
21.9
2.9
Bangladesh
Korea
1.7
Japan
1.3
Germany
Population Per Acre
1
USA 0.1
USSR 0.05
Mongolia
0.005
0
5
10
15
20
25
U.S. Population if settled at other
countries’ densities
51
U.S. at Same Density As:
Hong Kong
6.7
Bangladesh
Korea
4
Japan
3.1
Germany
2.3
0.265
USA
USSR 0.12
Mongolia
0.01
0
20
40
Projected Population, Billions
60
How U.S. Urban Land is Used, 1980
Utilities
11.0%
Mixed Urban
9.0%
Commercial
16.0%
Transitional
5.0%
Residential
59.0%
Source: Vesterby and Heimlich, 1991
U.S. Land Use
Urban land is 3 percent of U.S. land by area, but
the majority of land by value.
 With about 4 hectares of land per person (gross),
the U.S. is far from typical in density.
 However, even very dense countries, like Korea,
have small percentages of land in urban uses (see
below).

U.S. Cropland and Urban Land Area
500
Million Acres
400
300
Cropland
Urban
200
100
0
1958
USDA, Census
1968
1978
1988
Figure A-2
U.S. Cropland and Urban Land Area
While the share of U.S. land in urban uses has
been growing (from a low base), cropland has
been roughly constant over the last 40 years.
 When relative prices warrant it, land can readily
be converted from other uses to agriculture.

Some Big-Picture Land Use Questions
(Indicative Only, Not Exhaustive)



Can we reconcile market approaches with social and
ethical concerns?
– Why have many economists focused so much on costs
of regulation, not on benefits?
– Why have many noneconomists neglected costs?
How can we get a better handle on the real social costbenefit of different land uses?
– Just because something’s hard to measure doesn’t mean
it isn’t important
Can we focus more rigorously on distributional issues (as
well as efficiency)?
More Land Use Questions



What’s the right system of incentives (taxes, subsidies,
regulations, etc.)?
– Lower order: for market participants?
– Higher order: for planners and policymakers?
Urban decentralization (“sprawl”) is high on the public
agenda. What can we say about costs and benefits, and
appropriate responses?
Many other important land use issues, e.g.
– Brownfields
– Preservation
– Central city/suburban/rural issues
Some General Things to Look for in a
Cluster Hire in Land Use






Rigor
Open-mindedness
Some appreciation of the economics of land use (formal or
informal)
Good institutional knowledge as well as technical training
Interest in urban and rural land use issues
Understanding related markets (e.g. transportation) would
be a plus
Some objectives for a potential hire in
land economics






Put the “sprawl” debate on a more rigorous footing: better
definition and measurement, cost and benefits,
determinants, policy recommendations
Enhance understanding of interactions among land use,
general economic development, transport, real estate
What are the costs and benefits of different development
patterns, of different public interventions?
How do land uses affect income distribution, racial and
ethnic cleavages
Needs a strong economics background with demonstrated
ability to work with noneconomists; knowledge of
planning, law, institutions a plus
International as well as U.S. perspectives a plus