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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 - PowerPoint PPT Presentation
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Alternative Measures of Urban Form in U.S. Metropolitan Areas
Stephen MalpezziWen-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.
Tract TractN Density Population1 10 302 9 303 8 104 7 105 6 106 5 57 4 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
Density of Development
$
Maximum feasible density, under current rules and practices
Costs per housing unit fall with density
Figure 1
Costs and Benefits of Sprawl: The Cost-Benefit View
Density of Development
$
Maximum feasible density
Costs fall with density
Willingness-to-pay first rises, thenfalls, with density
Maximize Benefit-Cost
Figure 2
Costs and Benefits of Sprawl: The Cost-Benefit View, with Externalities
Density of Development
$
Maximum feasible density
Private costs
Willingness-to-pay
Maximize PrivateBenefit-Cost
Social costs (= private costs + external cost)
Maximize SocialBenefit-Cost
Figure 3
Popu
latio
n D
ensi
ty
Distance from CenterFigure 1-A
Popu
latio
n D
ensi
ty
Distance from CenterFigure 1-B
1
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Metropolitan Area Average Density
Figure 2
10
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Den
sity
of
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d M
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n T
ract
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Density of Census Tract ContainingMedian Person
Figure 3
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First Decile of Census Tract DensitiesAnd MSA Population
Figure 4
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effi
cien
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og
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Population Density GradientOne Parameter Model
Figure 4
0.0
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R-S
quar
ed o
f Sim
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Mod
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10,000 100,000 1,000,000 10,000,000 MSA Population
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CHI
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NAU
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IND
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Fit of Population Density GradientOne Parameter Model
Figure 5
2
4
6
8
10 In
terc
ept
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990
NY
CHI LAPHL
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NAU
DETDAL
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BAL
SATIND
SF
MEM
DCMILSJSCLE
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COL
BOS
NO
SEADEN
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Intercept from Univariate ExponentialPopulation Density Gradient
Figure 6
0.00
0.10
0.20
0.30
0.40
0.50
0.60
Impr
ove
men
t in
R-S
qua
red
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990
NYCHI
LA
PHLHOUNAU
DET
DAL
SDIPHX
BAL
SAT
IND
SFMEM
DCMILSJS
CLE
JKLCOL
BOSNO
SEA
DEN
NSH
STLKCMLAK
ELP
ATL
PGHOKC
CIN
FWO
MINPOOHON
TULBUFTDO
MIA
AUS
OAK
ABQ
TUC
NWK
CTEOMHLVL
BIR
WCH SAC
TPA
NFK
ROC
AKRCPXJC
BAT
ANHRCH
FRO
COS
SHR
LEX
JMSMOB
DTN
DES
GRR
MTGKNX
ANC
LBK
FWA
INCSPKMAD
RVR
CGA
SYR
CNOLSV
SLK
WOR
FLTLRA
TAC
PRV
GNC
FTL
SMAGRY
RLG
AMR
STCHAL
SAV
RKF
PAT
HRTSMO
EVN
LANORL
NHA
PEO
ERI
TPK
BEU
MAC
YNG
CDR SBN
OXN
ANN
MOD
EUG
BAK
ALN
WAT
BDC
BOIALB
WAC
CSC
RNO
ROA
SIL
ABLCTN
TRN
LAR
ODS
SLM
GBY
SRS
TAL
GNV
SIX
AUR
SNS
VAL
KAL
KEN
SAG
BDR
WLO
WILCSC
BLG
ATC
SWVWPB
CMO
FAR APLCHM
GSC
GFM
BNH
DAB
BIN
HBG
EAU
CAS
JWI
YAX
VIS
BXI
SRA
LAC LKL
CHY
MEL
RCY
KIL
YRK
BIL
BURFTM
RLD
WAS
MCH
BRD
NLN
MSX
MONXBO
BROLHMLWL
NSH
SGM
XBN
NIA
XCH
JOL
XCN
HMO
XCL
LEO
XDLXDV XDTXHRBRCMDC
NBC
XHO
BRZ
GAL
XMMXMW
RAC
DNB
NRC
ORG
STM
XPH
VMB
XPT
BEV
XPVVAN XPR
FLR
PAW
XSF
SCZ
XSE
ALGAXL
ALT
ANI
ANS
ANA
ASN
ATH
AUGBGR
BCRBEL
BNT
BSM
BRM
BRN
BRY
BUR
CVL
CCO
CHT
CUM
DAN
DAV
DCA
DCI
DOT
DBQDUL
ELK
ELM
END
FYN
FAZFIM
FAL
FSC
FCL
FPCFSA
FWBGAD
GLN
GFK
GRY
HAG
HIK
HTL
HAW
IOW
JMIJTN JNC
JDN
JKB
JHNJOPKNK
KOK
LFL
LFI LCL
LAN
LCN
LWK
LAW
LEW
LIM
LMT
LYN
MNOMEM
MDO
MRC
MDT
MNLMUN MUSNPL
NBMOCLOLY
OWNPCFPRK
PAS
PEN
PBAPTM
PMEPDRPKEPRO
PUE
RDGRDC
RCM
STC
STJ
SNG
SBR
SFE
SWB
SHN
SBY
SDT
SXC
SCP
SWOTHA
TEX
TUS
TYLUTR
VTX
WWV
WFT
WMP
WNC
YCCYAZ
Change in R2 of Negative Exponential,From Linear to 4th Power Models
Figure 8
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Gin
i Ind
ex
10,000 100,000 1,000,000 10,000,000 100,000,000 MSA Population
NY
CHI
LA
PHL
HOU
NAU
DET
DAL
SDI
PHX
BAL
SAT
IND
SFMEM
DC
MIL
SJS
CLE
JKL COL
BOS
NO SEA
DEN
NSH
STL
KCM
LAK
ELP
ATL
PGH
OKC
CIN
FWOMIN
POO
HON
TUL
BUF
TDO
MIA
AUS
OAK
ABQ
TUC
NWK
CTE
OMH
LVL
BIR
WCHSAC
TPA
NFKROC
AKR
CPX
JC
BAT
ANH
RCH
FROCOS
SHR
LEX
JMSMOB
DTN
DES
GRR
MTG
KNX
ANCLBK
FWA
INC
SPK
MAD
RVR
CGA
SYR
CNO
LSV
SLK
WORFLT
LRA
TAC
PRV
GNCSMA
GRY
RLG
AMR
STC
HAL
SAV
RKF
HRT
SMO
EVNLAN
ORL
NHA
PEOERI
TPKBEU
MAC YNG
CDR
SBN
OXN
ANN
MODEUG
BAK
ALN
WAT
BDC
BOI
ALBWAC
CSC
RNO
ROA
SIL
ABL
CTN
TRN
LAR
ODS
SLMGBY
SRSTALGNV
SIX
AUR
SNSVAL
KALKEN
SAG
BDR
WLO
WIL
CSC
BLG
ATCSWV
WPB
CMO
FAR
APL
CHM
GSC
GFM
BNH
DABBIN
HBG
EAUCAS
JWI
YAX VIS
BXI SRALAC
LKL
CHY
MEL
RCY
KIL
YRK
BIL
BUR
FTM
RLD
WASMCH
BRD
NLN
MSX
MON
BRO LHM
LWLNSH
SGM
NIA JOLHMO
LEO
NBC
BRZGAL
RAC
DNB
ORGSTM
VMB
BEV
FLR
PAW
SCZ
ALG
AXL
ALT
ANI
ANS
ANA
ASN
ATH
AUG
BGR
BCR
BEL
BNT
BSM
BRM
BRN
BRY
BURCVL
CCO
CHT
CUMDAN
DAV
DCA
DCI
DOT
DBQDUL
ELK
ELM
END
FYNFAZFIM
FALFSC
FCL
FPC
FSA
FWB
GADGLN
GFK
GRY
HAG
HIK
HTLHAW
IOW
JMI
JTN JNC
JDNJKB
JHN
JOPKNKKOK
LFL
LFILCL
LAN
LCN
LWK
LAW
LEW
LIMLMT
LYN
MNO
MEM
MDO
MRC
MDT
MNLMUNMUS
NPL
NBM
OCL
OLYOWN
PCF
PRK
PASPEN
PBA
PTM PME
PDR
PKE
PROPUE
RDG
RDC
RCM
STC
STJ
SNG SBR
SFE
SWB
SHN
SBYSDT
SXC
SCP
SWO
THATEX
TUS
TYL
UTR
VTX
WWV
WFT
WMP
WNC
YCC
YAZ
Gini Indices of Census TractDensities and Population
Figure 9
0
10
20
30
40
50
Avg
Str
aig
ht-
Lin
e D
ist.
to
Cen
ter,
km
10,000 100,000 1,000,000 10,000,000 MSA Population
NY
CHI
LA
PHL
HOU
NAU
DET
DALSDI
PHXBAL
SAT
IND
SF
MEM
DC
MILSJS
CLEJKL COL
BOS
NO
SEA
DEN
NSH STLKCMLAK
ELP
ATL
PGHOKC
CINFWO
MIN
POOHON
TUL
BUFTDO
MIAAUS
OAK
ABQ
TUC
NWK
CTE
OMH
LVL
BIR
WCH
SAC
TPA
NFKROC
AKR
CPX
JC
BATANHRCH
FRO
COS
SHRLEXJMS
MOB DTN
DES
GRR
MTG
KNX
ANC
LBK
FWA
INC
SPKMAD
RVR
CGA
SYRCNO
LSV
SLK
WORFLT
LRA
TAC PRV
GNC
SMA
GRY
RLG
AMR
STCHALSAV
RKF
HRT
SMO
EVNLAN
ORL
NHAPEO
ERI
TPK
BEU
MAC
YNG
CDR SBN
OXN
ANNMOD
EUG
BAK
ALN
WATBDCBOI
ALB
WAC
CSC
RNO
ROASIL
ABL
CTN
TRNLARODS
SLM
GBY
SRS
TALGNV
SIX
AURSNS
VAL
KALKEN
SAG
BDRWLO
WIL
CSC
BLG
ATC
SWV
WPB
CMOFAR
APL
CHM
GSC
GFM
BNH
DAB
BIN
HBG
EAU
CAS
JWI
YAXVIS
BXI
SRA
LAC
LKL
CHY
MEL
RCY
KIL YRK
BILBUR
FTMRLDWAS
MCH
BRD
NLN MSX
MON
BRO
LHM
LWLNSH
SGM
NIAJOL
HMOLEO
NBC
BRZ
GAL
RACDNB
ORG
STM
VMB
FLR
PAWSCZ
ALG
AXL
ALTANI
ANSANA
ASNATH
AUG
BGR
BCRBEL
BNT
BSM BRM
BRN
BRYBUR
CVL
CCOCHT
CUMDAN DAVDCA
DCI
DOT
DBQ
DUL
ELK
ELMEND
FYNFAZ
FIM
FALFSC
FCL
FPC
FSAFWB
GAD
GLN
GFK
GRY
HAG
HIK
HTLHAW
IOW
JMIJTN
JNC
JDN
JKB
JHN
JOP
KNKKOKLFL
LFI
LCL
LAN
LCN
LWK
LAW
LEW
LIM
LMT
LYN
MNO
MEMMDO
MRC
MDTMNL
MUN
MUS
NPL
NBM
OCL
OLY
OWN
PCF
PRKPAS PEN
PBA
PTM
PME
PDR
PKE
PRO
PUE
RDG
RDC
RCM
STC
STJSNG
SBR
SFE
SWB
SHN
SBY
SDT
SXC
SCPSWOTHA
TEX
TUSTYL
UTR
VTX
WWV
WFTWMP
WNC
YCCYAZ
Average Distance to Center of MSA
Figure 10
0
10
20
30
40
50
60
Med
Str
aig
ht-
Lin
e D
ist.
to
Cen
ter,
km
10,000 100,000 1,000,000 10,000,000 MSA Population
NY
CHI
LA
PHLHOU
NAU
DETDALSDI
PHXBAL
SATIND
SFMEM
DC
MILSJS
CLEJKL
COL
BOS
NO
SEA
DEN
NSH STLKCM
LAK
ELP
ATL
PGHOKC
CIN
FWO MIN
POOHONTULBUF
TDO
MIAAUS
OAK
ABQTUC
NWK
CTE
OMH
LVL
BIR
WCH
SAC
TPA
NFK
ROC
AKR
CPX
JC
BATANHRCH
FROCOSSHRLEX
JMSMOB
DTN
DESGRR
MTG
KNX
ANC
LBK
FWA
INC
SPKMAD
RVR
CGASYRCNO LSV
SLK
WORFLT
LRATAC
PRV
GNC
SMA
GRY
RLG
AMRSTCHAL
SAVRKF
HRT
SMO
EVN LAN
ORL
NHA
PEO
ERITPK
BEU
MAC YNG
CDR SBN
OXN
ANNMODEUG
BAK
ALN
WAT BDCBOI
ALB
WAC
CSC
RNOROASILABL
CTN
TRNLARODS
SLMGBY
SRS
TALGNV
SIX
AURSNS
VAL
KALKEN
SAG
BDR
WLO
WIL
CSC
BLG
ATCSWV
WPB
CMO FAR
APL
CHM
GSC
GFM
BNH
DAB
BIN
HBG
EAU
CAS
JWIYAX
VISBXI
SRA
LAC
LKL
CHY
MEL
RCY
KIL YRK
BIL
BUR
FTMRLD
WAS
MCHBRD
NLN MSX
MON
BRO
LHM
LWLNSHSGM
NIA
JOL
HMOLEO
NBC
BRZ
GAL
RAC
DNB
ORG
STM
VMBFLR
PAWSCZ
ALG
AXL
ALTANI
ANS
ANAASN
ATH
AUG
BGR BCRBEL
BNT
BSMBRM
BRN
BRYBURCVL
CCOCHT
CUM
DAN
DAV
DCA
DCI
DOT
DBQ
DUL
ELKELMEND
FYNFAZ
FIM
FALFSC
FCL
FPC
FSA
FWB
GAD
GLN
GFKGRY
HAG
HIK
HTL HAW
IOW
JMIJTN JNC
JDN
JKB
JHN
JOP
KNK
KOK
LFL
LFI
LCL
LAN
LCN
LWKLAWLEW
LIM
LMT
LYNMNO
MEM
MDO
MRC
MDT
MNL
MUN
MUS
NPL
NBM
OCL
OLY
OWN
PCF
PRKPAS PEN
PBAPTM
PME
PDR
PKE
PRO
PUE
RDGRDCRCM
STC
STJSNG
SBR
SFE
SWB
SHN
SBY
SDT
SXC
SCPSWOTHA
TEXTUSTYL
UTR
VTX
WWV
WFTWMPWNCYCCYAZ
Median Distance to Center of MSA
Figure 11
1
10
100
1,000
Ind
ex V
alu
e
10,000 100,000 1,000,000 10,000,000 100,000,000 MSA Population, 1990
NY
CHILA
PHL
HOUNAU
DET
DALSDIPHX
BAL
SATIND
SF
MEM
DC
MILSJS
CLE
JKL
COL
BOSNO
SEA
DEN
NSH
STLKCM
LAK
ELP
ATL
PGH
OKCCIN
FWO
MIN
POOHON
TUL
BUF
TDO
MIA
AUS
OAK
ABQ
TUC
NWK
CTE
OMHLVL
BIRWCH
SAC TPANFKROC
AKR
CPX
JC
BAT
ANH
RCH
FROCOS
SHRLEXJMS
MOB
DTN
DESGRR
MTGKNX
ANC
LBK FWAINC
SPKMAD
RVRCGA
SYR
CNO
LSVSLK
WORFLT
LRA
TAC
PRV
GNCSMAGRYRLGAMRSTC
HAL
SAVRKF
HRT
SMOEVN
LANORL
NHA
PEOERITPK
BEU
MAC
YNG
CDR
SBNOXN
ANNMOD
EUG BAK
ALN
WAT
BDC
BOI
ALB
WAC
CSC
RNOROASILABLCTN
TRN
LARODSSLM
GBY
SRSTAL
GNV
SIXAURSNS
VALKAL
KENSAG
BDRWLO
WIL
CSC
BLG
ATC
SWV
WPB
CMO
FAR
APL
CHM
GSC
GFM
BNH
DAB
BIN
HBG
EAUCAS
JWI
YAXVISBXI SRALAC
LKL
CHY
MEL
RCYKIL
YRKBIL
BUR
FTM
RLD
WAS
MCH
BRDNLN
MSX
MON
BRO
LHMLWL
NSH
SGMNIA
JOLHMO
LEONBC
BRZ
GALRAC
DNB ORG
STM
VMB
FLR
PAW
SCZ
ALGAXL
ALTANI
ANSANA
ASNATH
AUG
BGRBCR
BEL
BNT
BSM
BRMBRN
BRY
BUR
CVL
CCOCHTCUM
DAN
DAV
DCA
DCI
DOT
DBQ DUL
ELK
ELM
END
FYN
FAZ
FIM
FALFSC
FCLFPC
FSAFWBGAD
GLN
GFKGRYHAG
HIKHTL
HAWIOW
JMI
JTNJNCJDN
JKBJHN
JOP
KNKKOK
LFL
LFI
LCL
LAN
LCNLWK
LAWLEW
LIM
LMT
LYN
MNOMEM
MDOMRC
MDT MNLMUN MUS
NPL
NBM
OCLOLYOWN
PCF
PRK
PAS
PEN
PBA
PTM
PME
PDR
PKEPRO
PUE RDG
RDCRCM STC
STJ
SNG
SBR
SFE
SWB
SHNSBY
SDT
SXC
SCPSWOTHA
TEXTUSTYL
UTR
VTX
WWVWFT
WMP
WNC
YCCYAZ
Gravity Measure (Linear)
Figure 12
0.0
0.1
1.0
10.0
100.0
Ind
ex V
alu
e
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990
NY
CHI
LAPHL
HOU
NAUDET
DALSDIPHX
BAL
SATIND
SF
MEM
DCMIL
SJSCLE
JKL
COL
BOSNO
SEA
DEN
NSH
STLKCM
LAK
ELP
ATL
PGH
OKCCIN
FWO
MINPOO
HON
TUL
BUF
TDO MIAAUS
OAK
ABQ
TUC
NWK
CTE
OMH LVL
BIR
WCHSAC
TPANFK
ROC
AKR
CPX
JC
BAT
ANH
RCHFROCOSSHR
LEX
JMS
MOBDTNDES GRR
MTG
KNX
ANCLBK FWA
INC SPKMAD
RVR
CGA
SYR
CNO
LSV SLKWORFLT
LRA
TAC
PRV
GNC
SMAGRY
RLG
AMR STC
HAL
SAVRKF
HRT
SMOEVN LAN
ORL
NHA
PEO
ERITPK
BEUMAC
YNGCDR
SBN
OXN
ANN
MODEUG BAK
ALNWAT
BDC
BOI
ALB
WAC CSCRNOROA
SILABLCTN
TRNLAR
ODS
SLM
GBY
SRS
TAL
GNV
SIX AURSNS VALKALKEN
SAGBDR
WLO
WIL
CSCBLG ATC
SWV
WPBCMO
FAR APLCHM
GSC
GFMBNH
DAB
BIN HBG
EAU
CAS JWI
YAXVIS
BXI SRA
LAC
LKLCHY
MEL
RCY
KIL
YRK
BIL
BUR
FTMRLDWAS
MCH
BRD NLN
MSX
MONBRO
LHMLWL
NSH
SGMNIA
JOLHMOLEO
NBC
BRZ
GALRAC
DNB ORG
STM
VMB
FLR PAW
SCZALGAXL
ALT
ANI
ANSANA
ASNATH AUGBGR BCR
BEL
BNTBSMBRM
BRNBRY
BUR
CVL
CCOCHT
CUMDAN
DAV
DCA
DCI
DOT
DBQ
DUL
ELK
ELM
END
FYN
FAZ
FIM
FALFSC
FCL
FPCFSAFWBGAD
GLN
GFK GRYHAG
HIK
HTL
HAW
IOW
JMIJTN
JNC
JDN
JKB
JHN
JOP
KNKKOK
LFL
LFI
LCL
LAN
LCNLWK
LAWLEW LIM
LMT
LYN
MNO
MEMMDOMRC
MDT
MNL
MUNMUS
NPL
NBM
OCLOLY
OWN
PCF
PRK
PAS PENPBAPTM
PME
PDR
PKEPRO
PUE RDG
RDC
RCMSTC
STJ
SNGSBR
SFE
SWB
SHNSBY
SDT
SXCSCP
SWO
THA
TEX TUSTYL
UTR
VTX WWVWFT
WMPWNC
YCC
YAZ
Gravity Measure (Exponential)
Figure 13
(0.5)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Coe
ffic
ient
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990
NYCHI
LAPHL
HOUNAU
DETDAL
SDI
PHX
BAL
SAT
IND
SF
MEM
DC
MILSJS
CLEJKL
COL
BOS
NO
SEA
DEN
NSH
STL
KCMLAK
ELP
ATL
PGH
OKC
CIN
FWO
MIN
POOHON
TULBUF
TDO MIA
AUS OAK
ABQ TUC
NWK
CTEOMH LVL
BIR
WCH SACTPANFK
ROC
AKR
CPX
JC
BAT
ANH
RCH
FRO
COS
SHR
LEX
JMS
MOB
DTNDES
GRRMTG
KNX
ANCLBK
FWA
INCSPKMAD RVR
CGA
SYR
CNO
LSV
SLK
WOR
FLTLRA
TAC
PRV
GNCSMA
GRY
RLG
AMR STC
HAL
SAV
RKF
HRT
SMO
EVNLAN
ORL
NHA
PEO
ERI
TPK
BEU
MAC
YNG
CDRSBN
OXN
ANN
MOD
EUG
BAK
ALN
WAT
BDC
BOI
ALB
WAC
CSCRNOROASIL
ABL
CTN
TRNLAR
ODS
SLM
GBY
SRS
TAL
GNVSIXAURSNS VAL
KAL
KENSAGBDR
WLO
WIL
CSC
BLG
ATC
SWV
WPB
CMO
FAR
APL
CHMGSC
GFM
BNH
DABBIN
HBGEAU
CAS JWI
YAXVIS
BXI
SRALAC
LKL
CHY
MEL
RCY
KIL
YRK
BIL
BUR
FTM
RLD
WAS
MCHBRD
NLN
MSXMONBRO
LHM
LWL
NSH
SGM
NIA JOL
HMO
LEONBC
BRZ
GAL
RAC
DNB
ORG
STM
VMBFLR
PAW
SCZALG
AXL
ALT
ANI
ANS
ANA
ASN
ATH
AUG
BGR
BCR
BEL
BNT
BSMBRM BRN
BRYBUR
CVL
CCO
CHT
CUM
DAN
DAV
DCA
DCI
DOT
DBQ
DUL
ELK
ELMEND FYN
FAZ
FIM
FAL
FSC
FCL
FPCFSA
FWBGAD
GLN
GFK
GRY
HAG
HIK
HTL HAW
IOW
JMIJTNJNC
JDN
JKB
JHN
JOP
KNK
KOK
LFL
LFI LCL
LAN
LCN
LWKLAW
LEW
LIM
LMT
LYN
MNO
MEM
MDOMRC
MDT
MNLMUN
MUSNPL
NBM
OCL
OLY
OWN
PCFPRK
PAS
PENPBAPTM
PME
PDR
PKE
PRO
PUE
RDG
RDC
RCMSTC
STJ
SNG
SBR
SFE
SWB
SHN
SBY
SDT
SXC
SCP
SWO
THATEXTUSTYL
UTR
VTX
WWV
WFT
WMPWNCYCC
YAZ
Moran's I Spatial CorrelationPrelim., Using Quadratic Approx. to C
Figure 14 (Preliminary)
100
1,000
10,000
100,000
Pri
ncip
al C
om
pon
ent
Sco
re
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990
NY
CHI LAPHL
HOU
NAU DET
DAL
SDI
PHX
BAL
SATIND
SF
MEM
DCMILSJSCLE
JKL
COL
BOSNO
SEADEN
NSH
STL
KCM
ELP
ATL
PGHCIN
FWO
MINPOO
HON
TUL
BUF
TDO
MIA
AUS
OAK
ABQTUC
NWK
CTE
OMH LVL
BIRWCH
SACTPA
NFKROCAKRCPX
JC
BAT
ANH
RCH
FRO
COSSHR
LEX
JMSMOB
DTNDES GRR
MTG
KNX
ANCLBKFWA
INC
SPKMAD
RVRCGA
SYR
CNO
LSV SLKWOR
FLT
LRA
TAC
PRV
GNC
SMA
GRY
RLG
AMR
STC
HAL
SAVRKF
HRT
SMO
EVN LAN ORL
NHA
PEO
ERI
TPKBEU
MAC
YNGCDRSBN
OXNANN
MOD
EUG
BAKALN
WAT
BDC
BOIALB
WACCSC
RNO
ROASIL
ABL CTN
TRN
ODSSLM
GBYSRSTAL
GNV
SNSVAL
KAL
SAG
BDR
WLO
WIL
CSCATC
SWV
WPBFARAPL
CHM
GSC
BNHDAB
HBG
EAU
JWI YAX VISBXI
SRA
LKL
MEL
KILYRKFTMRLD
MCH
NLN
MSX
MONBRO LHM
LWL
NSHHMO
BRZ
GALRAC
DNB
SCZ
ALGAXL
ALT
ASNATH
AUGBNT
BRN
CVL
CCO
CHTCUM
DAV
DCA
DCI
DOT
DULELK FYN
FCL
FPCFSA
FWBGLN
GRY
HIK
HTLHAW
JMIJDN
JKB
JHNJOP
LFLLCL
LAN
LIM
LMTLYN
MEMMNL
NBM
OCL
PRKPEN
PME
PDR
PROPUE
RDG
STC
SBR
SWB
SHNSWO
THA
TYL
UTR
WWVWFT
1st Principal Component of "Sprawl"Unadjusted Data
Figure 15
(5,000)
0
5,000
10,000
15,000
20,000
25,000
Pri
ncip
al C
om
pon
ent
Sco
re
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990
New York
ChicagoLos AngelesPhiladelphia
HoustonNassau
DetroitDallasSan Diego
Phoenix
Baltimore
San AntonioIndianapolis
San Francisco
Memphis WashingtonMilwaukeeSan Jose
ClevelandJacksonville FLColumbusOH
BostonNew Orleans
SeattleDenverNashville Saint LouisKansas City MO
El Paso
Atlanta
PittsburghCincinnatiFort Worth MinneapolisPortland OR
Honolulu
Tulsa
BuffaloToledo
Miami
Austin
OaklandAlbuquerqueTucson
Newark
Charlotte
Omaha LouisvilleBirmingham
Wichita SacramentoTampa
NorfolkRochester NYAkronCorpus Christi
Jersey City
Baton Rouge
Anaheim
RichmondFresnoColorado SpringsShreveportLexington
Jackson MSMobile DaytonDes MoinesGrand RapidsMontgomery
Knoxville
AnchorageLubbockFort Wayne
LincolnSpokaneMadison
Riverside
ColumbusGA SyracuseChattanooga
Las VegasSalt Lake City
WorcesterFlintLittle RockTacoma
Providence
Greensboro
Springfield MAGary
Raleigh
Amarillo StocktonHuntsvilleSavannahRockford HartfordSpringfield MOEvansville Lansing
Orlando
New HavenPeoria
ErieTopekaBeaumontMacon Youngstown
Cedar RapidsSouth Bend Oxnard
Ann Arbor
ModestoEugene BakersfieldAllentown
Waterbury BridgeportBoise
Albany NYWaco
Columbia SC
RenoRoanokeSpringfield ILAbilene
Canton
TrentonOdessa
Salem ORGreen Bay
Santa RosaTallahasseeGainesvilleSalinas
VallejoKalamazooSaginaw
BoulderWaterlooWilmington DE
Charleston SCAtlantic CityCharleston WVA
West Palm Beach
FargoAppleton
Champaign
Greenville
BinghamtonDaytona BeachHarrisburg
Eau ClaireJanesvilleYakimaVisaliaBiloxi Sarasota
LakelandMelbourneKilleen YorkFort MyersRichland
Manchester
New London MiddlesexMonmouth
BrocktonLawrence MALowell
Nashua HamiltonBrazoria
GalvestonRacineDanbury
Santa CruzAlbany GAAlexandria LAAltoona
AshevilleAthensAugusta
Benton HarborBrownsvilleCharlottesville
ChicoClarksvilleCumberland DavenportDecatur ALDecatur IL
Dothan DuluthElkhartFayetteville NC
Fort CollinsFort PierceFort SmithFort Walton BeachGlens FallsGreeley
HickoryHouma Huntington
Jackson MIJamestown
Johnson CityJohnstownJoplin Lafayette LALake Charles LancasterLimaLongviewLynchburg
Mc AllenMonroe
New Bedford
OcalaParkersburg
PensacolaPortland MEPortsmouth NH
ProvoPueblo ReadingSt. Cloud
Santa Barbara
ScrantonSharonSteubenvilleTerre HauteTyler UticaWheelingWichita Falls
1st Principal Component of "Sprawl"Population Adjusted Data
Figure 16
0.0
1.0
2.0
3.0
4.0
5.0
Thei
l Ind
ex
10,000 100,000 1,000,000 10,000,000 100,000,000 MSA Population
NY
CHI
LA
PHL
HOU
NAU
DET
DAL
SDI
PHX
BAL
SAT
IND
SF
MEM DC
MIL
SJS
CLEJKL COL
BOS
NO
SEA
DEN
NSH
STLKCM
LAK
ELP
ATL
PGH
OKC
CIN
FWO MIN
POO
HON
TUL
BUFTDO
MIA
AUS
OAK
ABQTUC
NWK
CTE
OMH
LVL
BIR
WCH
SAC
TPANFK
ROC
AKR
CPX
JCBAT
ANHRCH
FROCOS
SHRLEX
JMSMOB
DTN
DES
GRR
MTG
KNX
ANC
LBK
FWA
INCSPK
MAD
RVR
CGA
SYRCNO
LSV
SLK
WORFLT
LRA
TAC
PRV
GNCSMAGRY
RLG
AMR
STC
HAL
SAVRKF
HRT
SMOEVNLAN
ORL
NHA
PEOERITPK
BEU
MAC
YNG
CDRSBN
OXN
ANN
MOD
EUGBAK
ALNWAT BDC
BOI
ALBWACCSC
RNO
ROA
SIL
ABL
CTN
TRN
LAR
ODS
SLM
GBYSRS
TALGNV
SIX
AUR
SNSVAL
KALKENSAG
BDR
WLOWIL
CSC
BLG
ATCSWV
WPB
CMO
FAR
APL
CHM
GSC
GFM
BNH
DAB
BINHBGEAU
CAS
JWI
YAX
VIS
BXI
SRA
LAC
LKL
CHY
MEL
RCY
KIL
YRK
BIL
BUR
FTM
RLD
WAS MCH
BRD
NLN
MSXMON
BRO LHMLWLNSH
SGM
NIAJOL
HMOLEO
NBC
BRZGALRAC
DNBORGSTM
VMBBEV
FLR
PAW
SCZALG
AXLALTANI
ANSANA ASN
ATH
AUGBGR
BCR
BEL
BNT
BSM
BRM
BRN
BRY
BURCVL
CCO
CHTCUMDAN
DAV
DCA
DCI
DOT
DBQ
DUL
ELK
ELM
END
FYNFAZ
FIMFALFSC
FCL
FPC
FSA
FWB
GADGLN
GFK
GRY
HAGHIK
HTLHAW
IOW
JMI
JTN JNCJDN JKBJHN
JOPKNKKOK
LFLLFI LCL
LAN
LCNLWK
LAW
LEWLIMLMT
LYN
MNO
MEMMDO
MRCMDT
MNLMUNMUS
NPL
NBMOCL
OLYOWN
PCF
PRK
PAS
PENPBA
PTM PMEPDR
PKE
PRO
PUE
RDG
RDC
RCM
STC
STJSNG
SBR
SFE
SWB
SHNSBY
SDT
SXC
SCP
SWO
THATEX
TUS
TYL
UTRVTX
WWV
WFT
WMP
WNC
YCC
YAZ
Theil Indices of Census TractDensities and Population
1
10
100
1,000
10,000
Fir
st D
ecil
e o
f T
ract
Den
siti
es
10000 20000 30000 40000 50000 60000 Median HH Income
NY
CHILA
PHLHOU
NAU
DETDAL
SDIPHX
BALSAT
IND
SF
MEM
DCMIL
SJS
CLE
JKLCOL
BOSNO
SEADEN
NSH
STLKCM
LAKELP
ATLPGH
OKC
CINFWO
MINPOO
HON
TUL
BUF
TDO
MIA
AUS
OAK
ABQ
TUC
NWK
CTEOMH
LVL
BIR WCH
SAC
TPANFK
ROC
AKRCPX
JC
BAT
ANH
RCHFRO COSSHR LEXJMSMOB
DTN
DESGRR
MTG
KNX ANC
LBK FWAINC
SPKMAD
RVR
CGA SYRCNO
LSVSLK
WORFLT
LRA
TACPRV
GNC
SMAGRY
RLGAMR
STC
HALSAV RKF
HRT
SMO EVNLAN
ORLNHA
PEOERI
TPKBEU MACYNG
CDR
SBN OXN
ANNMOD
EUG BAK
ALN
WAT
BDC
BOIALB
WACCSC
RNOROA
SILABL
CTN
TRNLAR
ODS
SLMGBY
SRS
TALGNV
SIX
AUR
SNS
VALKALKEN
SAG
BDR
WLO
WIL
CSC
BLG
ATCSWV
WPB
CMO
FAR
APLCHM
GSC
GFM
BNH
DAB
BIN HBG
EAU
CAS
JWIYAXVIS
BXI
SRA
LACLKL
CHY MEL
RCY
KILYRK
BIL
BURFTM
RLDWAS
MCH
BRD
NLN
MSXMONBRO
LHMLWL
NSHSGM
NIA JOLHMO LEO
NBC
BRZ
GALRAC
DNB
ORG
STM
VMBBEVFLR
PAW
SCZ
ALG
AXL
ALT ANIANSANAASN
ATHAUGBGR
BCR
BELBNT
BSM
BRM
BRNBRY BUR
CVLCCOCHTCUM
DAN
DAVDCADCIDOT
DBQ
DUL
ELKELM
END
FYN
FAZ
FIM
FALFSCFCL
FPC
FSAFWB
GAD
GLN
GFK
GRY
HAGHIK
HTL
HAWIOW
JMIJTNJNCJDNJKB
JHN
JOPKNK KOK
LFL
LFILCL
LAN
LCN
LWKLAW
LEW
LIMLMTLYN
MNOMEM
MDOMRC
MDT
MNLMUNMUS NPL
NBM
OCL
OLYOWNPCFPRKPAS
PEN
PBA
PTMPMEPDRPKE
PRO
PUE
RDG
RDC
RCMSTCSTJ
SNG
SBR
SFE
SWBSHN
SBYSDT
SXC
SCP
SWOTHA
TEX
TUSTYLUTR
VTXWWV
WFT
WMP
WNC
YCCYAZ
First Decile of Census Tract DensitiesAnd Income
1
10
100
1,000
10,000
Fir
st D
ecil
e o
f T
ract
Den
siti
es
1940 1950 1960 1970 1980 Mean Year Built, 1990 Census
NY
CHILA
PHLHOUDET DAL
SDI PHX
BALSAT
IND
SF
MEM
DC
MIL
SJS
CLE
JKLCOL
BOS
NOSEA
DEN
NSH
STLKCM
ELP
ATLPGH
OKC
CINMINPOO
HON
TUL
BUF
TDO
MIA
AUS
ABQ
TUC
NWK
CTE
OMHLVL
BIRW CH
SAC
TPANFK
ROC
CPX
BAT
RCHFRO COSSHR
LEX JMSMOB
DTN
DES
GRR
MTG
KNXANC
LBKFWA
INC
SPKMADCGASYR
CNO
LSVSLK
W OR FLT
LRA
PRV
GNC
SMA
RLGAMR
STC
HALSAVRKF
HRT
SMOEVN
LAN
ORLNHA
PEO
ERI
TPK
BEUMAC
YNG
CDR
SBN
MOD
EUG BAK
ALN
W AT
BOI
ALB
W AC
CSCRNO
ROASIL ABL
CTN
LAR
ODS
SLMGBY
TALGNV
SIXSNS
KAL
SAG
W LOCSC
BLG
ATC
SWV
W PB
CMO
FAR
APL
CHM
GSC
GFM
BNH
DAB
BINHBG
EAU
CAS
JW IYAX VIS
BXI
SRA
LACLKL
CHY MEL
RCY
KIL
YRK
BIL
BURFTM
RLDW AS
MCH
BRD
NLNSCZ
ALG
AXL
ALT ANI ANSANA
ASNATH
AUGBGR
BCR
BELBNT
BSM
BRM
BRN
BRYBUR
CVLCCOCHT
CUM
DAN
DAVDCADCI
DOT
DBQ
DUL
ELKELM
END
FYN
FAZ
FIM
FAL FSCFCL
FPC
FSAFWB
GAD
GLN
GFK
GRY
HAG HIK
HTL
HAWIOW
JMIJTN JNC
JDN JKBJHN
JOPKNKKOK
LFL
LFI LCL
LAN
LCN
LW K
LAW
LEW
LIMLMTLYN
MNO
MEM
MDOMRC
MDT
MNLMUNMUS NPL
NBM
OCL
OLYOW N
PCFPRK PAS
PEN
PBA
PTM PME PDR
PKE
PRO
PUE
RDG
RDC
RCMSTC
STJ
SNG
SBR
SFE
SWB
SHNSBY
SDT
SXC
SCP
SWO
THA
TEX
TUSTYL
UTR
VTX
W WV
W FT
W MP
W NC
YCCYAZ
First Decile of Census Tract DensitiesAnd Age of Housing Stock
1
10
100
1,000
10,000
Per
sons
per
Sq
uare
Kil
omet
er
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990
New York
Chicago
Los Angeles
Philadelphia
Houston
Nassau
Detroit
DallasSan Diego
Phoenix
Baltimore
San AntonioIndianapolis
San Francisco
Memphis
WashingtonMilwaukeeSan JoseCleveland
Jacksonville FLColumbusOH
Boston
New Orleans SeattleDenver
Nashville
Saint LouisKansas City MO
El Paso AtlantaPittsburgh
Oklahoma City
CincinnatiFort Worth Minneapolis
Portland OR
Honolulu
Tulsa
Buffalo
Toledo
Miami
Austin
Oakland
Albuquerque
Tucson
Newark
CharlotteOmahaLouisville
BirminghamWichita
Sacramento
TampaNorfolk
Rochester NY
Akron
Corpus Christi
Jersey City
Baton Rouge
Anaheim
Richmond
Fresno
Colorado SpringsShreveportLexington
Jackson MSMobile
Dayton
Des Moines
Grand Rapids
Montgomery
Knoxville
Anchorage
Lubbock Fort WayneLincolnSpokane
Madison
Riverside
ColumbusGASyracuse
Chattanooga
Las Vegas
Salt Lake City
Worcester
Flint
Little Rock
Tacoma
Providence
Greensboro
Fort Lauderdale
Springfield MAGary
Raleigh
Amarillo
StocktonHuntsvilleSavannahRockford
Paterson
Hartford
Springfield MOEvansvilleLansing
Orlando
New Haven
Peoria
ErieTopeka
BeaumontMacon
Youngstown
Cedar Rapids
South Bend
OxnardAnn Arbor
Modesto
Eugene Bakersfield
Allentown
WaterburyBridgeport
BoiseAlbany NY
Waco
Columbia SC
Reno
Roanoke
Springfield ILAbilene
Canton
Trenton
Laredo
Odessa Salem OR
Green Bay
Santa RosaTallahassee
Gainesville
Sioux FallsSalinas
VallejoKalamazoo
Kenosha
SaginawBoulder
Waterloo
Wilmington DE
Charleston SC
Billings
Atlantic City
Charleston WVA
West Palm Beach
Columbia MO
Fargo
AppletonChampaign
Greenville
Great Falls
Binghamton
Daytona BeachBloomington IN Harrisburg
Eau Claire
Casper
Janesville
Yakima
Visalia
Biloxi
Sarasota
La Crosse Lakeland
Cheyenne
Melbourne
Rapid City
Killeen
York
Bloomington IL
Burlington VT
Fort Myers
Richland
Wausau
Manchester
New London
MiddlesexMonmouthBrocktonLawrence MALowell
NashuaHamilton
Brazoria
GalvestonRacineDanbury
Vineland
Santa Cruz
Albany GA
Alexandria LA
AltoonaAnniston
Asheville
Athens
AugustaBangor
Bellingham
Benton Harbor
Bismarck
BrownsvilleBryanBurlington NC
CharlottesvilleChicoClarksvilleCumberland
Dansville
Davenport
Decatur AL
Decatur IL
DothanDubuque
Duluth
ElkhartElmira
Enid
Fayetteville NC
Fayetteville AR
Fitchburg
Florence ALFlorence SC
Fort Collins
Fort Pierce
Fort Smith
Fort Walton BeachGadsden
Glens Falls
Grand Fork
Greeley
Hagerstown
Hickory
Houma
HuntingtonIowa CityJackson MI
Jackson TNJacksonville NC
Jamestown Johnson CityJohnstownJoplin
KankakeeKokomo
Lafayette LA
Lake Charles
Lancaster
Las Cruces
Lawrence KS
Lawton
Lewiston
Lima
LongviewLynchburg
Mansfield
Mc Allen
Medford
Merced
MonroeMuncie
Naples
New Bedford
Ocala
OwensboroPanama CityParkersburgPensacola
Pine Bluff
Pittsfield Portland MEPortsmouth NH
Provo
Pueblo
Reading
Redding
Rochester MN
St. CloudSt. Joseph
San Angelo
Santa Barbara
Santa Fe
Scranton
SharonSheboygan
ShermanSioux CityState College
Steubenville
Terre Haute
TexarkanaTuscaloosa
TylerUtica
Victoria
Wheeling
Wichita FallsWilliamsport
Wilmington NCYuba City
Yuma
Metropolitan Area Average Density
1
10
100
1,000
10,000
Per
son
s P
er S
q.
KM
10,000 100,000 1,000,000 10,000,000 MSA Population, 1990 (Log Scale)
NY
CHILA
PHLHOU
NAU
DETDAL
SDIPHX
BALSAT
IND
SF
MEM
DC
MIL
SJS
CLE
JKL COL
BOS
NOSEA
DEN
NSH
STLKCM
LAKELP
ATLPGH
OKC
CINFWO
MINPOO
HON
TUL
BUF
TDO
MIA
AUS
OAK
ABQ
TUC
NWK
CTE
OMHLVL
BIRWCH
SAC
TPANFK
ROC
AKRCPX
JC
BAT
ANH
RCHFROCOSSHRLEXJMSMOB
DTN
DES
GRR
MTG
KNXANC
LBKFWA
INC
SPKMAD
RVR
CGA SYRCNO
LSVSLK
WORFLT
LRA
TAC
PRV
GNC
SMAGRY
RLGAMR
STC
HALSAVRKF
HRT
SMOEVN
LAN
ORLNHA
PEO
ERI
TPK
BEUMAC
YNG
CDR
SBN OXN
ANNMOD
EUG BAK
ALN
WAT
BDC
BOI
ALB
WAC
CSCRNO
ROASILABL
CTN
TRN
LAR
ODS
SLMGBY
SRS
TALGNV
SIX
AUR
SNS
VALKALKEN
SAG
BDR
WLO
WIL
CSC
BLG
ATC
SWV
WPB
CMO
FAR
APL
CHM
GSC
GFM
BNH
DAB
BINHBG
EAU
CAS
JWI
YAX VISBXI
SRA
LACLKL
CHYMEL
RCY
KIL
YRK
BIL
BURFTM
RLDWAS
MCH
BRD
NLN
MSXMONBRO
LHMLWL
NSHSGM
NIA JOL
HMOLEO
NBC
BRZ
GAL
RAC
DNB
ORG
STM
VMBBEV
FLR
PAW
SCZ
ALG
AXL
ALTANIANSANA
ASN
ATHAUGBGR
BCR
BEL BNT
BSM
BRM
BRN
BRYBUR
CVL CCOCHT
CUM
DAN
DAV
DCADCIDOT
DBQ
DUL
ELKELM
END
FYN
FAZ
FIM
FALFSCFCL
FPC
FSAFWB
GAD
GLN
GFK
GRY
HAG HIK
HTL
HAWIOW
JMIJTN JNC
JDN JKBJHN
JOPKNKKOK
LFL
LFI LCL
LAN
LCN
LWK
LAW
LEW
LIMLMTLYN
MNO
MEM
MDOMRC
MDT
MNLMUN MUSNPL
NBM
OCL
OLYOWN
PCFPRKPAS
PEN
PBA
PTM PMEPDR
PKE
PRO
PUE
RDG
RDC
RCMSTC
STJ
SNG
SBR
SFE
SWB
SHNSBY
SDT
SXC
SCP
SWO
THA
TEX
TUS
TYLUTR
VTX
WWV
WFT
WMP
WNC
YCCYAZ
Density of Tract Containing Median HHMSA Tracts Sorted by Density
0.00
0.20
0.40
0.60
0.80
1.00
Un
dad
just
ed R
2,
4th
Po
wer
Mod
el
0.00 0.20 0.40 0.60 0.80 1.00 Undadjusted R2, Linear Model
NY
CHI
LA
PHL
HOU
NAU
DET
DAL
SDIPHX
BAL
SATIND
SF
MEM
DC
MIL
SJS
CLEJKL
COL
BOS
NO
SEA
DENNSH
STL
KCM
LAK
ELP
ATL
PGH
OKC
CIN
FWO
MINPOO
HON
TUL
BUFTDO
MIA
AUS
OAK
ABQ
TUC
NWK
CTE
OMH
LVLBIRWCH
SAC
TPA
NFK
ROC
AKR
CPX
JC
BAT
ANH
RCH
FRO
COSSHR
LEX
JMSMOB
DTN
DES
GRR
MTG
KNX
ANC
LBK
FWA
INC
SPK
MAD
RVR
CGA
SYR
CNO
LSV
SLK
WORFLT
LRA
TAC
PRV
GNC
SMA
GRY
RLG
AMR
STC
HALSAVRKF
HRT
SMO
EVN
LAN
ORL
NHA
PEOERI
TPK
BEU
MACYNG
CDRSBN
OXN
ANNMOD
EUG
BAK
ALN
WATBDC BOI
ALB
WAC
CSC
RNO
ROA
SIL
ABL
CTN
TRN
LAR
ODSSLM
GBY
SRS
TALGNV
SIX
AUR
SNS
VAL
KAL
KEN
SAG
BDR
WLO
WIL
CSC
BLG
ATC
SWV
WPB
CMO
FAR
APL
CHM
GSC
GFM
BNH
DAB
BIN
HBG
EAU
CAS
JWI
YAX
VIS
BXISRA
LAC
LKL
CHY
MEL
RCY
KIL
YRK
BILBUR
FTM
RLD
WAS
MCH
BRD
NLN
MSXMON
BRO
LHM
LWL
NSH
SGMNIA
JOLHMO
LEO
NBC
BRZ
GAL
RAC
DNB
ORG
STM
VMBBEV
FLR
PAW
SCZ
ALGAXL
ALT
ANI
ANS ANA
ASN
ATH
AUG
BGR
BCR
BEL
BNT
BSM
BRM
BRN
BRY
BUR
CVL
CCOCHT
CUM
DAN
DAV
DCA
DCI
DOT
DBQ
DUL
ELK
ELM
END
FYN
FAZ
FIM
FAL
FSC
FCL
FPC
FSA
FWB
GADGLN
GFK
GRYHAG
HIK
HTL
HAW
IOW
JMI
JTN
JNC
JDN
JKB
JHN
JOP
KNKKOKLFL
LFI
LCL
LAN
LCN LWKLAW
LEWLIM
LMT
LYN
MNO
MEM MDOMRC
MDT
MNL
MUN
MUS
NPLNBM
OCLOLY
OWN
PCF
PRK
PAS
PENPBA
PTM
PME
PDR
PKEPRO
PUE
RDG
RDC
RCM
STC
STJ
SNG
SBR
SFE
SWB
SHN
SBY
SDT
SXC
SCP
SWO
THA
TEXTUSTYL
UTR
VTX
WWVWFT
WMP
WNC YCC
YAZ
Fit of Linear, Fourth Power SUE Models
Figure 6
100
1,000
10,000
100,000
Den
sity
of
Wei
ghte
d M
edia
n T
ract
0 10 20 30 40 50 60 70 Galster et al. Total Sprawl Index
NY
CHILA
PHL
HOU
DETDAL
SF
DCBOSDEN
ATL
MIA
Compare Galster et al. Results to DensOf Tract Containing Median Person
Figure 7
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
0% 20% 40% 60% 80% 100%
Population
Manufacturing
Wholesale Trade
Retail Trade
Select Services
19801948
Source: O’Sullivan, Kain, Census
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
0.005
0.05
0.1
1
1.3
1.7
2.9
21.9
0 5 10 15 20 25
Mongolia
USSR
USA
Germany
Japan
Korea
Bangladesh
Hong Kong
Population Per Acre
U.S. Population if settled at other countries’ densities
0.01
0.12
0.265
2.3
3.1
4
6.7
51
0 20 40 60
Mongolia
USSR
USA
Germany
Japan
Korea
Bangladesh
Hong KongU
.S. a
t Sam
e D
ensi
ty A
s:
Projected Population, Billions
How U.S. Urban Land is Used, 1980
Transitional5.0%
Mixed Urban9.0%
Utilities11.0% Commercial
16.0%
Residential59.0%
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
0
100
200
300
400
500
1958 1968 1978 1988
Mill
ion
Acr
es
CroplandUrban
USDA, CensusFigure 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 cost-benefit 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
