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A model-based approach towards A model-based approach towards assessing landscape restoration assessing landscape restoration
activities in Watershed 263, Baltimore, activities in Watershed 263, Baltimore, MDMD
Brian VoigtBrian Voigt
University of Vermont - Spatial Analysis LabUniversity of Vermont - Spatial Analysis [email protected]@uvm.edu
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Current Research - UrbanSim
• Modeling urban development patterns in Chittenden County, VT using UrbanSim– simulate future land use and associated environmental
impacts under baseline conditions and alternative scenarios
• Quantifying effect(s) of future urban development patterns have on:– water quality, habitat fragmentation, aesthetics, auto-
dependency, energy consumption, etc.
• Intended to facilitate discourse not predict policy adoption or exact development locations
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Project Collaborators
• Austin Troy, University of Vermont
• Morgan Grove, USFS
• Guy Hager & George Friday, Parks and People Foundation
• Bill Stack, Department of Public Works
• Others– Watershed council, community residents,
BES collaborators
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Research Questions
• How do we design a simulation modeling framework to facilitate learning about future landscape trajectories based on human interventions and watershed restoration activities?
• How will social and environmental conditions within Watershed 263 change as the City of Baltimore and the Parks and People Foundation strive to meet the urban forestry initiative goals?
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Project Goals
• Use a participatory modeling approach to explore relationships among socio-economic and biophysical system characteristics of a complex natural – human urban system
• Help residents and resource managers to consider the effects of human interventions associated with varying levels of green infrastructure investment
• Facilitate a learning process about the natural, biological and socio-economic components of the watershed and their collective interactions that define the current state and potential trajectories of watershed evolution
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The Simile Modeling Environment
• Dynamic, spatially explicit, interactions & feedback
• Stocks, flows & parameters• Visual modeling
environment• Sub-models can be used
independently or grouped with other system components
• Use equation editor to formalize variable relationships and sub-model interactions
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WS263 Model Framework• Suite of sub-models that
interact with one another• Partition landscape into set of
grid cells and define initial condition based on biophysical and socio-economic parameters
• Agent-based approach representing household level decision-making (e.g. relocation, rent v own, etc.)
• Scenario-based analysis to improve our understanding of the system and accommodate variations in data interpretation and relative effects of system components
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Data Sources• Demographics
– US Census: Public-Use Microdata Samples (5% sample), Summary File data tables (SF1 & SF3)
– BNIA: neighborhood indicators• Biophysical
– BES: land cover, topography, water quality, air quality• Socio-economic
– BNIA: employment and population control totals, forecasts– BES: employment sites, real estate transaction data, current
land use / land use history, household surveys, PRISM classification
• Infrastructure– Sewer system, road network, transit
• Landscape interventions– PPF & DPW: list of completed, proposed, anticipated projects
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Proposed Model Components: 1
• Land use: probability of transition from one type to another• Land cover: changes with interventions, aging vegetation,
infrastructure addition / removal; relationship to water quality and other ecosystem services
• Land price: defined by a hedonic model at the cell level• Employment: allocate employment at the cell level based on
externally derived control totals using a gravity model • Residential location choice: internal and external; agents
(households) synthesized from US Census, PUMS, and household survey data with a focus on tenure, length of residency, employment and income; includes QOL attributes
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Proposed Model Components: 2
• Intervention: location choice; probability of success; exogenous inputs define number and type of projects; multiple sub-models for different types of interventions
• Landscape metrics – land use mix, proximity to amenities / disamentities, fragmentation, residential and employment densities; updated annually, these metrics will be used as variables in the other model components; statistical analysis and existing literature will estimate relationships between metrics and system components
• Mechanism to integrate external models (e.g. UFORE, etc.)
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Model Output
• Preliminary list of indicators– land value, canopy cover, habitat fragmentation,
residential relocation and vacancy rates, QOL, green infrastructure density and water quality
– refine list of indicators based on further collaboration with PPF and watershed council representatives
• Data visualization– results depicted graphically as maps, overlaid with
major streets and cultural landmarks, by joining the output to polygons bounded at alternative geographic scales (e.g., block group, neighborhood, etc.)
– convey findings and engage stakeholder discussions
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Expected Products
• Fully documented model– Detail assumptions, limitations, and future improvements– Transferable to other urban sites– Sub-models can be “recycled” for other applications
• Scenario analysis capability– Foster discussion among stakeholders– Useful for evaluating our knowledge of system
components and understanding of system interactions
• Algorithms for computing indicators
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Next Steps
• Explore relationships among diverse collection of data from multiple sources
• Define base year condition• Create synthetic population at the household level• Conceptual model development (early 2007)• Work with project collaborators to identify
appropriate indicators and techniques for conveying information / results to diverse stakeholder groups
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Questions?Questions?