Thinking Spatially 121806
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Transcript of Thinking Spatially 121806
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Thinking spatially:
Economic models ofurban land use change
Elena G. Irwin
Associate Professor
Department of Agricultural, Environmental and Development Economics
Ohio State University
Presentation prepared for the conference on Spatial Thinking in theSocial Sciences, University of Illinois, December 17-18, 2006.
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Key points
Pattern vs. process-based models of land use change
Traditional geographic models: emphasize pattern over process
Traditional economic models: emphasize process over pattern
Qualitative changes in land use change patterns points out
limitations of pattern-based geographic models
Increased availability of fine-scale data points out limitations of
highly stylized economic models
We need hybrid models that combine process and pattern
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Example: pattern-based model of urban land
use change
Cellular automaton urban growth model
Non-behavioral model of land use cell transitions that are
determined by relative geographic location of cell (spatial rules)
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Washington Baltimore historical urban growth
(Urban Growth in American Cities - Glimpses of
US Urbanization, USGS Circular 1252, 2003;
Available online at
http://landcover.usgs.gov/LCI/urban/data.phpSource: Clarke and Gaydos, 1998
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How have economists traditionally
represented space?
Space is typically represented in economic units vs. geographicalunits, e.g.
Urban economics: transportation costs
New geographical economics: regional economy
Behavioral (i.e., process-based) models of economic agents(households or firms) that provide simple explanation and prediction
of spatial pattern
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Urban economic model of land use: space
as transportation costs
Monocentric model (or bid-rent model)
Pre-determined central employment area
Accessibility to central employment district drives firm andhousehold location decisions
Otherwise space is a featureless plane
Predicts concentric ring of urban land use around centralbusiness district and declining density gradient
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Monocentric model land use prediction
Low
density
residential
Higher densityresidential
distance from city
Undeveloped
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Monocentric model land use prediction(distance via major roads)
Low
densityresidential
Undeveloped
distance from city
Higher density
residential
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Empirical test: urban density gradient
Empirical test of monocentric city
model: urban density gradient
(Clark, 1951; Mills, 1972;
Edmonston, 1975) Assume negative exponential:
Estimate density gradient:
_ a( ) expD x D x K! 0
D x
DK
x x!
x = distance from city; D = population density; = density gradient
Negative Exponential Density Gradient
0
2000
4000
6000
8000
0 5 10 15
suburbs
persons/sq
mile
city
= -0.25K
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How well does this model describe actual
patterns of urban land use?
Using population density gradient estimates, Anas, Arnott and Small
(1998) estimate that the monocentric model explains approximately
63% of urban decentralization between 1950-70 in the US
To what extent does this conclusion depend on spatial scale,
geographical extent and type of data?
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Urban land proportion
0 - 0.050.051 - 0.1
0.13 - 0.270.25 - 0.5
0.5 - 1
10 0 10 20 Miles
Urban Land Density (NRI Data 1997)
State of Maryland
Washington DC
Baltimore
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State of Maryland
Population Density (Census tract 2000)
10 0 10 20 Miles
Persons / sq km0 - 13.68113.681 - 21.29521.295 - 27.38627.386 - 38.138.1 - 52.45552.455 - 67.64267.642 - 100.328100.328 - 171.101171.101 - 489.099489.099 - 6393851.358
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State of Maryland
2000 Urban and Rural Land Use (Department of Planning)
High Urban
Rural
Water
10 0 10 20 Miles
Low Urban
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Washington D.C. area: population density vs. land use (2000)
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Explaining residential land use patterns
(Irwin, Bockstael and Cho, 2006)
How well does basic monocentric model explain finer scale
variations in residential pattern?
Is there structural change across time? across urban-ruralgradient? are results scale dependent?
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Explaining residential land use patterns
(Irwin, Bockstael and Cho, 2006)
Regression analysis using Maryland 1973 and 2000 land use raster
data (100 m cell size)
Dependent variables: %undeveloped in 1 and 5 sq km
neighborhoods
Explanatory variables
Distance via roads to major urban centers
Distance via roads to suburban and small city centers
Controls for local spatial heterogeneity (soil and topography)
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Measure of residential pattern: %undeveloped in neighborhood
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Variable Estimate
Standard
Error t value Estimate
Standard
Error t value
Intercept -25.159 0.418 -60.21 -13.656 0.422 -32.38
log(DC dist) 4.080 0.063 64.4 7.574 0.064 118.45
log(BA dist) 4.639 0.050 93.06 7.541 0.050 149.87
log(35k+ dist) 2.486 0.078 31.75 0.987 0.079 12.5
log(10k+ dist) 1.617 0.078 20.75 0.087 0.079 1.1
Dataset: MDP 1973 (175,496 obs)
Neighborhood = 0.5 sq km Neighborhood = 5 sq km
Adj R-Sq: 0.1651 Adj R-Sq: 0.3026
Variable Estimate
Standard
Error t value Estimate
Standard
Error t value
Intercept -27.057 0.292 -92.82 -35.599 0.250 -142.37
log(DC dist) 4.543 0.051 88.35 8.189 0.044 185.66
log(BA dist) 4.579 0.040 113.3 7.401 0.035 213.52
log(35k+ dist) 2.809 0.056 49.95 3.022 0.048 62.65
log(10k+ dist) 1.788 0.054 33.3 3.055 0.046 66.34
Dataset: MDP 2000 (365,438 obs)
Neighborhood = 0.5 sq km Neighborhood = 5 sq km
Adj R-Sq: 0.1574 Adj R-Sq: 0.3816
Results reported for distance variables only
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Urban-rural county typology
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Variable Estimate
an ar
Error t value
Intercept -67.42717 0.46243 -145.81
log(DC dist) 11.04036 0.06897 160.07
log(BA dist) 12.70976 0.06279 202.41
log(other dist) 6.35461 0.06389 99.45
log(10k+ dist) 0.35066 0.13563 2.59
Neighborhood = 5 sq km
Adj R-Sq: 0.3937
Dataset: Large urban 2000 (159,642 obs)
Variable Estimate
an ar
Error t value
Intercept -88.99276 0.73313 -121.39
log(DC dist) -0.62311 0.20329 -3.07
log(BA dist) 19.02737 0.1749 108.79
log(35k+ dist) 14.35187 0.1155 124.26
log(10k+ dist) 11.2783 0.09684 116.47
Neighborhood = 5 sq kmAdj R-Sq: 0.5161
Dataset:Suburban 2000 (55,931 obs)
Variable Estimate
an ar
Errort value
Intercept 28.90027 1.00904 28.64
log(DC dist) -0.82313 0.16117 -5.11
log(BA dist) 9.62599 0.21068 45.69
log(35k+ dist) 3.56933 0.09694 36.82
log(10k+ dist) -4.31805 0.13289 -32.49
Neighborhood = 5 sq km
Adj R-Sq: 0.0676
Dataset: Exurban 2000 (79,830 obs)
Results reported for distance variables only
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Results using finer scale land use data
Distance to city explains some of the variation in urban pattern
Scale dependence: distance explains about 30% of variationwith larger neighborhood size vs. 15% of variation with smallerneighborhood size
Spatial heterogeneity: in exurban areas, about 93% of variationis unexplained vs. 49% unexplained in suburban areas
Other spatial processes matter, particularly at local scale andparticularly in exurban areas
Need explicit representation of geographic space to capturethese other processes
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Spatial interactions hypothesis
(Irwin and Bockstael, 2002)
Can the fragmented pattern of development be explained as the
result of interactions among developed land use parcels?
Positive spatial externalities
clustered pattern
Negative spatial externalities scattered pattern
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Data
Geo-coded land parcel centroids from two Maryland exurbancounties
Seven year history of convertible parcels (1991-1997)
Parcel characteristics: zoning, network road distance to D.C.,public sewer, soil, slope, etc.
Neighborhood variable: percent of residential land within a givenbuffer of each parcel centroid
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Binary dependent variable:
1 if converted in time period t, 0 otherwise
VariableVariable EffectEffectDistance to DC negative and significant
Soil quality negative and significant
Minimum lot size positive up until approx. 3.8
acresPublic sewer positive and significant
Steepness of slope negative and significant
Distance to nearest
road
insignificant
%Development in
Inner Neighborhood
insignificant
%Development in
Outer Neighborhood
negative and significant
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predicted LU change
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Accounting for multiple spatial processes
Can spatial interactions be incorporated into monocentric model?
No: monocentric model simplifies space to one dimension (distanceto city)
Can distance be incorporated into a model of spatial interactions?
Yes: explicit representation of geographic space allows for
consideration of multiple spatial processes
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Hybrid models of process and pattern
Process-based model: agent decision making
Pattern-based model: agents are located in geographic space
As a result, space can matter in multiple ways
Spatial heterogeneity
Distance (e.g., to employment, recreation)
Spatial interactions and externalities
Spatial scale, scale-dependent effects, cross-scale interactions
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Made possible by
Availability of finer scale land use/cover data
Geographic data software
Computational ability and methods
H
ybrid models of process and pattern
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Some modeling challenges
Hybrid models require a combination of theoretical, empirical andsimulation approaches
Theoretical challenges
Identifying relevant spatial and temporal scales Accounting for interactions across spatial and temporal scales
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globe
country
region
metro
county
neigh-
borhood
parcel
month quarter year decade century
transportation and
communications costs
economic restructuring
householdwealth
land quality, public services, surrounding land uses
living costs,
agglomeration
economics, labor
force, employment,
publicservices,
infrastruc-
ture, local
policies
neighborhood amenities, zoning,access
time
space
Determinants of Household/Firm Location & Land Use Decisions (Irwin, 2006)
urban &
natural
amenities
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Some modeling challenges (continued)
Empirical challenges
Identifying spatial processes vs. measurement error
Data accuracy, appropriate data for question
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Some modeling challenges (continued)
Simulation challenges
Specifying parameters and spatial environment (e.g., the rightamount of spatial heterogeneity)
Validating model specification
Testing pattern hypotheses and summarizing model results
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Some modeling methods
Theoretical
Complex systems theory
Behavioral economics
Empirical
Pattern detection and metrics using GIS
Spatial econometrics
Simulation
Agent-based (or multiagent) models and geographic automata systems
Object-oriented programming