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UrbanSim Model and Data Development. John Britting Wasatch Front Regional Council. Background. Local application under development for 7 years Lawsuit settlement agreement increased our focus with respect to application

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urbansim model and data development

UrbanSim Model and Data Development

John Britting

Wasatch Front Regional Council

  • Local application under development for 7 years
  • Lawsuit settlement agreement increased our focus with respect to application
  • Need to determine whether suitable for use locally by January 2004; with peer review
    • Learned a lot
    • modeling system operational
    • not suitable for use yet
what we want from urbansim
What we want from UrbanSim
  • Tool for scenario comparisons
  • Interrelationships between infrastructure policies & land-use
    • Can (potentially) inform planning process w.r.t. secondary/cumulative impacts
outcome of peer review
Move ahead…

Should be useful in short-term

Refine to meet WFRC’s needs

Reasonable results for significant policies

(Eventually) Superior to current process

Commitment to better planning


Add’l tuning is required for immediate use

Needs a more timely and improved dataset

Difficult to interpret the impacts of less significant policies

Not ready for corridor studies

Outcome of Peer Review
wfrc resolution on urbansim
WFRC Resolution on UrbanSim

The Council finds that additional testing of UrbanSim is needed…, (including) research into model refinement, data, policy implications, estimation of resources needed, and an outreach program to familiarize planning staffs in the region on the appropriate and useful applications of UrbanSim…

good news
Good News
  • Reasonable response to large-scale policy changes
  • The model is operational
  • Initial outreach efforts were useful
not good news
Not Good News
  • Little/no sensitivity to less significant policies
  • Database needs to be improved
  • Randomness
  • 0% or 100% residential vacancy rates
  • Price inflation
  • Still discovering bugs
technical work plan
Technical Work Plan
  • Data Development
    • Refine representation of land policies and existing land-use
  • Model Development
    • Re-estimate and validate all models
    • Improve logic
  • Application Utilities
    • Make implementation smoother and more effective (and working correctly)
    • Overcome randomness
    • Summarize and present key indicators quickly (utilize SQL and GIS effectively to save time)
need for data development
Need for Data Development

Residential Capacity

Non-Residential Capacity

Dark blue is NYC dense

key models in urbansim
Key Models in UrbanSim
  • Land Price Model
  • Developer Models
  • Residential Location Choice Model
  • Employment Location Choice Models
land price model development
Land Price Model Development
  • Goals
    • Reasonable relationships that will hold over time
    • Thorough validation effort
    • Account for variation in value by type of use
      • Redevelopment analysis
    • Appropriate sensitivity to transportation accessibility
      • Transportation/Land-use interaction
preliminary results
Preliminary Results
  • Distance from highway
    • Residential/commercial (+, then -); Industrial (-)
  • Land price of neighborhood (+)
  • Access. to employment (Residential +)
  • Access. to population (Non-residential +)
  • Consistency between Zoning/Use (+)
  • Environmental factors (slope, open, roads, etc.)
    • Residential (+, then -); Non-residential (-)
defining accessibility
Defining Accessibility
  • Regional measure was initially used – function of logsum and activity at destination
  • Local measure was also used in location choice models (walking distance)
  • We have three urban areas
    • Regional, sub-regional and local accessibility likely to be important
regional vs sub regional access
Regional vs. Sub-regional Access
  • Regional on left
  • Both measures have merit
  • Sub-regional shows logical patterns around 3 big urban cores
land price model validation
Land Price Model Validation
  • Initial review suggests patterns are similar
  • More to do to validate
  • Categorical in application
dealing with inflation
Dealing with Inflation
  • Average land price increases substantially over 33 year simulation (4% per year)
    • Accessibility/pop/emp are the culprits
  • Average income stays in year 2000 dollars
  • Options:
    • Use a different accessibility measure
    • Inflate income or deflate price
    • Use categorical price variables
additional model development
Additional Model Development
  • Need to use predicted land price in estimation
  • Need to make implied behavior more consistent with theoretical understanding
  • Existing models:
    • (HH/Dev) higher price is always more attractive
    • (HH) closer proximity to highways is preferred
    • Tricky to quantify residential character of neighborhoods
    • A lot of multi-collinearity that clouds transparency
  • Careful validation is necessary
residential location
Residential Location
  • Segmented by income quartile
  • Logical sensitivities to price by income segments (non-linear)
  • Larger households prefer lower density; smaller households prefer higher density (non-linear)
  • All income groups tend to cluster
  • Wrestling with accessibility measures
model development challenges
Model Development Challenges
  • Not a lot of experience in practice with these models
  • Many non-linear relationships (e.g. price, density, accessibility)
  • 0-car vs. 1-car vs. 2-car accessibility parameters – what is a reasonable relationship?
  • Behavior vs. Patterns in data
  • KISS
sensitivity to accessibility
Sensitivity to Accessibility
  • How much is appropriate?
  • Use sensitivity testing to understand model response (Right direction? How much change is needed to get a response?)
  • Quantify average contribution to choice probabilities & price
  • Utilize year-built data and TDM to do historical validation (quantify changes in accessibility and development from 1992-2002)
schedule milestones
  • LRP is due in 28 months
  • Expect the work-plan coming out of the peer review to be complete in 1 year
  • Experimentation is on-going
  • Land-use plan refinements will be completed within 2 months
  • Land price and residential location models will be completed within 1 month