The Landuse Evolution and
Impact Assessment Model
LEAM
a distributed modeling environment
Brian Deal
Don Fournier
Problem:
Rampant
Urban Growth
urban growth
between 1970 and 1990:
New York’s metro population grew 5%
total land area increased 61%
Chicago’s metro population grew 4%
total land area increased 46%
Cleveland’s metro population declined 11%
total land area still grew 33%
Southern California urbanization
environmental impacts
water quality and quantity
 each year more than 100,000 acres of
wetlands are destroyed, in large part to build
sprawling new developments
 wetlands can remove up to 90 percent of the pollutants in
water
 wetlands
 destruction leads directly to polluted water
 sprawl increases the risk of flooding
 development pressures lead to building on floodplains
 in the last eight years, floods in the United States killed more
than 850
 people and caused more than $89 billion in property damage
 much of this flooding occurred in places where weak zoning
laws allowed developers to drain wetlands and build in
floodplain
a dialogue is needed
 as competition for land has intensified, so has disagreement
over how to balance economic use and conservation of
natural resources
 the lack of a genuine dialogue between advocates of public
and private interests has led to a paralysis of effective
decision making at every level of government
a decision support system is needed to improve the gaps
in our basic understanding of the urban community, their
dynamics and transformation, resource requirements,
and landscape sustainability
what should an urban
transformation DSS include?
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spatial and dynamic
publicly accessible
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web based and easy to use
 (democratized)
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distributed computational environment
physical, social and economic drivers
be able to produce what-if landuse planning scenarios
impact evaluation (so what?)
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capture feedback between systems
it should include multiple scales
multiple landuse change factors including:
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models
graphic
open architecture for ease of modification and calibration
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decisions
be able to integrate submodels
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data
global climate change impacts, economic, environmental
and societal impacts
transportable
interdisciplinary
impacts
dynamic spatial
modeling
 provides a forum for understanding the
implications of spatial problems
 visualization of the problem
A
AS
J
TP
the dynmaic spread of disease in Illinois
 discount rates
 personal vs. societal
beta model scenario
leam
the landuse evolution and impact
assessment model
 a dynamic spatial modeling environment
 distributed modeling approach
 scenario based planning tool
 societal and environmental impact assessment
 planning decision support tool
University of Illinois
NSF
USGS
NCSA
TRIES
ERDC - CERL
leam
conceptual
framework
a scenario based
spatial decision support tool
outcome
scenario
X
LEAM
scenario
Y
decision
outcome
critical components
 process based modeling environment
 feedback
 impact assessment
 environmental
 social
 economic
 open architecture
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
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 democratization

physical, social and economic drivers
be able to produce what-if landuse
planning scenarios
impact evaluation (so what?)



distributed computational environment
it should include multiple scales
multiple landuse change factors
including:


capture feedback between systems
open architecture for ease of
modification and calibration

 contextual experts
 visualization advancements
spatial and dynamic
publicly accessible
be able to integrate submodels
global climate change impacts,
economic, environmental and societal
impacts
transportable
interdisciplinary
LEAM
model drivers
economic
population
social
geography
transport
open space
neighborhood
simulation
planning group
random
planning group
landuse change
water
air
habitat
tes
fiscal
energy
impact assessment
sustainable indices
waste
environ
model drivers
land use drivers
conceptual framework
COMERCIAL \IND
OPEN SPACE
RESIDENTIAL
USGS LU MAP
EXISTING LANDUSE
EXISTING
AL T LANDUSE
TRANSFORMATION
DEV PROBABIL ITY
development
probabilities
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open space
DEM
economics
social models
utilities
spontaneity
organic
growth trends
transportation model
PRICE
DEM
OPEN SPACE SWITCH
ECON TRENDS
ECONOMICS
SOCIAL MODEL
DEV PROBABILITY
UTILITIES
SPONTANEOUS
NEIGHBORS
PLANNING MAP
GROWTH TRENDS
TRANSPORTATION MODEL
spatial data
inputs
 USGS
 7.5 Minute DEM quads
 NLCD Land Use Classification data
 DLG Roads data
 USDA
 SSURGO data
 County Soil Surveys
 State Geological Survey (e.g. ISGS)
 100 Year Flood Zone data
 Municipal Boundaries data
 State Dept. of Transportation (e.g. IDOT)
 Annual Average 24 hour Traffic Volume Maps
 County Development Dept. (e.g. Kane County Development Dept.)
 Growth and Development Policies / Maps
organic growth
 simulates the expansion of established cells
 cells that have two or three urbanized neighbors are
evaluated to determine whether each will become a
new urbanized cell
diffusive growth
 diffusive growth uses resource availability and probabilistic
modeling techniques to determine the likelihood of
development. All urbanized patches (res, com, rds,..) diffuse
“resources” and influence the probability of further
development
 resources can be available utilities (potable water, sewer, electricity,
etc.) and economic or other resources available to the community
spontaneity
 simulates the influence of randomized urban
development
 if a randomly-drawn location passes a test of
development suitability, it becomes a new
urban location
economic and population drivers
economics
 population growth is responsible for the housing demand
 based on the statistical household-size predictions of Kane-County
 economic sector is the important factor that “decides” if the
existing demand can be realized or if the particular budget
constraint is too high
 the demand for houses influences the average house price
 rising over time in response to increased demand
growth areas
 different spatial entities
have varying growth
rates
 aggressive vs passive
communities
DEM
 elevational
restrictions and
probabilities
transportation
drivers
 The Goals
 Understand the importance of
transportation in the development
process.
 Understand connection between
vehicle trips and increased
development, as well as vehicle
congestion & site un-attractiveness
 Road Access
 the probability for the environmental
change of a cell is affected by road
proximity
 Road Capacity
 a development probability based on
road capacity
 road capacity interacts with congestion
factor
 Congestion
 the level of road congestion affects the
probability of development
 Cost Surface Map
 depicts the ease of passage over
particular land uses
 Transportation Drainage Map
 calculates least time cost route
 transportation “watersheds”
 drain auto uses to calculate congestion
coefficients
transportation
vehicle ‘sheds’

Vehicle-shed Concept & “Drainage”
Process
 Algorithm using Cost-surface Map
and Roads file.
 Creates Vehicle-sheds at Federal
and State Highway scales to
compute congestion.
 Watershed drainage concept
adjusted for vehicles.
 Assumption that all vehicles “drain”
toward downtown Chicago, IL.
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Probability for Development
considers congestion.
 decreases with increasing vehicle
traffic
 decreases when congestion begins
to impede vehicle flow.
 consequently, Cell “attractiveness”
diminishes with increasing
congestion.

Road Capacity and Outside vehicle
inputs considered.
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Current land use of Cell determines
trip number
vehicle trips
 Traffic Counts
 Outside inputs of the model.
 Rate of outside input calculated from
1965-1992 data.

Annual Average 24 Hour Traffic
Volume ( IDOT & USDOT ).
 Results in a Development Probability
due to Transportation
 Factored into the development
model.
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Future Modifications
 Value of Multiple Attractors?
 Distance Considerations
 Self-regulating capability
portion of the 1965 vehicle trip map for Kane County
simulation
output
simulation
leam model
Dundee Township
100,000 cells
county model
1,000,000 cells
impact assessments
So what?
landuse change
water
air
habitat
tes
training
impact assessment
sustainable indices
energy
waste
environ
water quality
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Estimates amount of N (nitrogen), P
(phosphorus) and SS (suspended solids)
Runoff Curve Numbers method developed
by Soil Conservation Service, USDA
Variables
 NLCD category
MONTHLY RAINFALL
DATA INPUT
Area
 Land use category read from the map
Q in cm
 Obtained from USGS
 MONTHLY RAINFALL
 20yrs average monthly rainfall of Aurora
 Obtained from NOAA
S
Amount of Runoff
 SOIL TYPE
 Hydrological soil group
 Original data obtained from USDA and
reclassified to HSG
CN
N Factor
 S and CN
 S: Potential maximum retention after runoff
begins
 Determined by CN
N in Runoff
NLCD Category
habitat fragmentation
raccoon model
frogs
avian species
Arriv ing IN
Adult repro rate
Migrating OUT
Adults
Prop breeding adults
Av erage dy ing age
Migration Rate
Adults
Juv eniles
Cubs
Sex Ratio
Birthing
Aging
Maturing
Growing
Juv eniles
Cubs Death
Juv eniles repro rate
Juv eniles Death
Cubs DR
Prop breeding juv eniles
Adults Death
K
Adult DR
K
K
Juv eniles DR
economic impacts
 Why study the costs??
 Provide useful information to planners and
policymakers for a more comprehensive
evaluation of alternative urban forms
 How do we approach it?
 Source out all relevant contributing costs-factors,
social/environmental, market and private
 Methodology:
 Costs set within Leam framework
roads
utilities
schools
societal
environmental
impacts
impacts
 climate change
 biodiversity
 water quality
 surface/subsurface hydrology
 energy
 associated externalities
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air quality
habitat loss/fragmentation
economic impacts
social impacts
 quality of life
 drive times
140
120
100
80
60
40
20
0
water
qual
air qual
water
quan
energy
sustain
impacts
sustainability indices
 Ecological Indicators
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Water use vs. availability
Solid waste generation vs. landfill capacity
Sewage generation vs. processing capacity
Energy use and emissions
 Economic Indicators
 Cost per household of infrastructure
 Social Indicators
 Open space per capita
 Social cost of loss of land
 Presence of native wildlife
 Mission related indicators
 Training lands
 Energy availability
the development of regional
sustainable indices as they
relate to community interaction
variables, climate change,
ecological factors and urban risk
assessments
leam
beta version
decisions
conclusions
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The LEAM modeling environment presents a novel way of representing
landuse change models. The 30-meter x 30-meter resolution of the
model represents more clearly, we believe, the social dynamic present
in landuse change decision making. The use this resolution enables
the introduction of variables that can not be represented in larger
scaled models.
Dynamic spatial modeling is important for the development of a robust
landuse decision support system (DSS). The DSS should include:
 evaluation criteria for: global climate change impacts, economic,
environmental and socially based landuse interactions
 landuse policy scenarios and given evaluation criteria to determine future
environmental and landuse sustainability impacts
 infrastructure and community based landuse assessment models to assess
impacts, resource requirements, and salient linkages
 a set of regional sustainable indices as they relate to community interaction
variables, climate change and urban risk assessments
The overall goal of the DSS should be to improve the gaps in our basic understanding
of the urban community, resource requirements, and landscape sustainability.