thinking spatially 121806

8/3/2019 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

thinking spatially 121806

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