The landuse evolution and impact assessment model
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The Landuse Evolution and Impact Assessment Model. L E A M. a distributed modeling environment. Brian Deal Don Fournier. Problem: Rampant Urban Growth. Southern California urbanization. environmental impacts water quality and quantity.

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The Landuse Evolution and Impact Assessment Model

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The landuse evolution and impact assessment model

The Landuse Evolution and Impact Assessment Model

L E A M

a distributed modeling environment

Brian Deal

Don Fournier


Problem rampant urban growth

Problem: Rampant Urban Growth


Southern california urbanization

Southern California urbanization


Environmental impacts water quality and quantity

environmental impactswater 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

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

data

decisions

models

impacts

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


The landuse evolution and impact assessment model

dynamic spatial modeling

  • provides a forum for understanding the implications of spatial problems

  • visualization of the problem

    • discount rates

    • personal vs. societal

A

AS

J

TP


Beta model scenario

beta model scenario


Leam the landuse evolution and impact assessment model

leamthe 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

leam conceptual framework


A scenario based spatial decision support tool

a scenario based spatial decision support tool

outcome

scenarioX

LEAM

decision

scenarioY

outcome


Critical components

critical 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


The landuse evolution and impact assessment model

economic

population

social

geography

transport

open space

neighbor- hood

random

fiscal

energy

waste

environ

water

air

habitat

tes

L E A M

model drivers

simulation

planning group

planning group

landuse change

impact assessment

sustainable indices


Model drivers

model drivers


Land use drivers conceptual framework

land use driversconceptual framework


Development probabilities

PRICE

DEM

OPEN SPACE SWITCH

ECON TRENDS

ECONOMICS

SOCIAL MODEL

DEV PROBABILITY

UTILITIES

SPONTANEOUS

NEIGHBORS

PLANNING MAP

GROWTH TRENDS

TRANSPORTATION MODEL

development probabilities

  • open space

  • DEM

  • economics

  • social models

  • utilities

  • spontaneity

  • organic

  • growth trends

  • transportation model


Spatial data inputs

spatial 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 growth

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

  • 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

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

economic and population drivers


Economics

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

growth areas

  • different spatial entities have varying growth rates

    • aggressive vs passive communities


The landuse evolution and impact assessment model

DEM

  • 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


Transportation

transportation

  • 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 trips

vehicle 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

simulation output


Leam model dundee township

leam modelDundee Township

100,000 cells


County model

county model

1,000,000 cells


Impact assessments

landuse change

training

energy

waste

environ

water

air

habitat

tes

impact assessment

sustainable indices

impact assessments

So what?


Water quality

water quality

MONTHLY RAINFALL

  • 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

DATA INPUT

Area

Q in cm

S

Amount of Runoff

CN

N Factor

NLCD Category

N in Runoff


Habitat fragmentation raccoon model frogs avian species

habitat fragmentationraccoon modelfrogsavian species


Economic impacts

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

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


Sustainability indices

impacts

sustainability 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

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

leambeta version


Conclusions

conclusions

  • 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.


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