The Landuse Evolution and Impact Assessment Model

The Landuse Evolution and Impact Assessment Model

a distributed modeling environmenta distributed modeling environment

Brian DealDon Fournier

L E A ML E A ML E A ML E A M

urban growthurban growthbetween 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%

Problem: Rampant Urban GrowthProblem: Rampant Urban Growth

Southern California urbanizationSouthern California urbanization

environmental impacts water quality and quantity

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 neededa 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?what should an urban transformation DSS include?

spatial and dynamic publicly accessible

web based and easy to use (democratized)

graphic be able to integrate submodels

capture feedback between systems open architecture for ease of modification and calibration

distributed computational environment it should include multiple scales multiple landuse change factors including:

physical, social and economic drivers be able to produce what-if landuse planning scenarios impact evaluation (so what?)

global climate change impacts, economic, environmental and societal impacts

transportable interdisciplinary

data

models

impacts

decisions

dynamic spatial modelingdynamic spatial modeling provides a forum for understanding the

implications of spatial problems visualization of the problem

discount rates personal vs. societal

the dynmaic spread of disease in Illinois

A AS

J TP

beta model scenariobeta model scenario

leam the landuse evolution and impact assessment model

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 IllinoisNSF

USGSNCSATRIES

ERDC - CERL

leam conceptual framework

leam conceptual framework

a scenario based spatial decision support tool

a scenario based spatial decision support tool

LEAMLEAM

scenarioX

scenarioY

outcome

decisiondecision

outcome

critical componentscritical components process based modeling environment

feedback

impact assessment environmental social economic

open architecture contextual experts

visualization advancements democratization

spatial and dynamic publicly accessible be able to integrate submodels

capture feedback between systems open architecture for ease of

modification and calibration distributed computational environment

it should include multiple scales multiple landuse change factors

including: physical, social and economic drivers

be able to produce what-if landuse planning scenarios

impact evaluation (so what?) global climate change impacts,

economic, environmental and societal impacts

transportable interdisciplinary

L E A ML E A ML E A ML E A M

planning groupplanning group planning groupplanning groupsimulationsimulation

model driversmodel drivers

random geography transport open space neighbor-

hood economic population social

landuse changelanduse change

water air habitat tes fiscal energy waste environ

sustainable indicessustainable indices

impact assessment

model driversmodel drivers

land use driversconceptual framework

land use driversconceptual framework

EXISTING LANDUSE ALT LANDUSE

TRANSFORMATION

RESIDENTIAL

COMERCIAL\IND

OPEN SPACE

EXISTING

USGS LU MAP

DEV PROBABILITY

development probabilitiesdevelopment probabilities

ECONOMICS

SOCIAL MODEL

UTILITIES

SPONTANEOUS

NEIGHBORS

DEV PROBABILITY

ECON TRENDS

DEM

GROWTH TRENDS

PRICE

PLANNING MAP

TRANSPORTATION MODEL

OPEN SPACE SWITCH

open space DEM economics social models utilities spontaneity organic growth trends transportation model

spatial datainputsspatial datainputs 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 growthorganic 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 growthdiffusive 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

spontaneityspontaneity 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 driverseconomic and population drivers

economicseconomics 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 areasgrowth areas different spatial entities

have varying growth rates aggressive vs passive

communities

DEMDEM elevational

restrictions and probabilities

transportation drivers

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

transportationtransportation

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

vehicle ‘sheds’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.

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.

vehicle tripsvehicle trips Current land use of Cell determines trip number 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.

Future Modifications Value of Multiple Attractors? Distance Considerations Self-regulating capability

portion of the 1965 vehicle trip map for Kane County

simulation output

simulation output

simulationsimulation

leam modelDundee Township

leam modelDundee Township

100,000 cells

county modelcounty model

1,000,000 cells

impact assessmentsimpact assessmentsSo what?So what?

landuse changelanduse change

water air habitat tes training energy waste environ

sustainable indicessustainable indices

impact assessment

water qualitywater quality Estimates amount of N (nitrogen), P

(phosphorus) and SS (suspended solids) Runoff Curve Numbers method developed by Soil

Conservation Service, USDA Variables

NLCD category Land use category read from the map Obtained from USGS

MONTHLY RAINFALL 20yrs average monthly rainfall of Aurora Obtained from NOAA

SOIL TYPE Hydrological soil group Original data obtained from USDA and reclassified

to HSG S and CN

S: Potential maximum retention after runoff begins Determined by CN

N Factor

Area

Amount of Runoff

S

NLCD Category

Q in cm

N in Runoff

MONTHLY RAINFALL

CN

DATA INPUT

Juveniles Adults

Maturing

Cubs Death

Migrating OUT

Adults Death

Cubs DR

Cubs

Adults

Juveniles

Growing

Arriving IN

Birthing

Juveniles Death

Juveniles DR

Migration Rate

Adult DR

Average dying age

Prop breeding adults

Prop breeding juveniles

Adult repro rate

Aging

Sex Ratio

K

K

K

Juveniles repro rate

habitat fragmentationraccoon model

frogsavian species

habitat fragmentationraccoon model

frogsavian species

economic impactseconomic impacts Why study the costs??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?How do we approach it? Source out all relevant contributing costs-factors,

social/environmental, market and private Methodology:Methodology:

Costs set within Leam framework

roads

utilities

schools

societal

environmental

impacts impacts climate change biodiversity water quality

surface/subsurface hydrology

energy associated externalities

air quality habitat loss/fragmentation economic impacts social impacts

quality of life drive times

impacts

0

20

40

60

80

100

120

140

waterqual

air qual waterquan

energy sustain

sustainability indicessustainability indices Ecological Indicators

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

impacts

the development of regional sustainable indices as they

relate to community interaction variables, climate change,

ecological factors and urban risk assessments

leambeta version leam

beta version

decisions

conclusionsconclusions 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.