Transcript UrbanSim Model and Data Development

UrbanSim Model and
Data Development
John Britting
Wasatch Front Regional Council
Background
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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
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What we want from UrbanSim
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Tool for scenario
comparisons
Interrelationships
between infrastructure
policies & land-use
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Can (potentially) inform
planning process w.r.t.
secondary/cumulative
impacts
Outcome of Peer Review
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Move ahead…
 Should be useful in shortterm
 Refine to meet WFRC’s
needs
 Reasonable results for
significant policies
 (Eventually) Superior to
current process
 Commitment to better
planning
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However…
 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
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
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Reasonable response to
large-scale policy changes
The model is operational
Initial outreach efforts
were useful
Not Good News
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2030 Residential Vacancy Rate
0
0.001 - 0.01
0.01 - 0.02
0.02 - 0.03
0.03 - 0.04
0.04 - 0.05
0.051 - 0.1
0.1 - 0.25
0.25 - 0.5
0.5 - 0.75
0.75 - 0.999
Vacant
No Dwelling Units
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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
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Data Development
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Model Development
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Refine representation of land policies and existing land-use
Re-estimate and validate all models
Improve logic
Application Utilities
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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
Residential Capacity
Non-Residential Capacity
Dark blue is NYC dense
Key Models in UrbanSim
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Land Price Model
Developer Models
Residential Location Choice Model
Employment Location Choice Models
Land Price Model Development
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Goals
Reasonable relationships that will hold over time
 Thorough validation effort
 Account for variation in value by type of use
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 Redevelopment
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analysis
Appropriate sensitivity to transportation accessibility
 Transportation/Land-use
interaction
Preliminary Results
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Distance from highway
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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.)
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Residential (+, then -); Non-residential (-)
Defining Accessibility
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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
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Regional, sub-regional and local accessibility likely to
be important
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
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Initial review
suggests patterns
are similar
More to do to
validate
Categorical in
application
Dealing with Inflation
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Average land price increases substantially over
33 year simulation (4% per year)
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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
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Additional Model Development
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Need to use predicted land price in estimation
Need to make implied behavior more consistent with
theoretical understanding
Existing models:
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(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
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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
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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
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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)
Randomness
Random Variation in Residential Units by Geographic Level
0.5
Average Coefficient of Variation
0.45
0.4
0.35
Gridcells
TAZs
Small Districts
Medium Districts
Large Districts
County
Region
0.3
0.25
0.2
0.15
0.1
0.05
0
cv_dur_1998
cv_dur_2000
cv_dur_2003
cv_dur_2008
cv_dur_2012
Year
cv_dur_2016
cv_dur_2020
cv_dur_2025
cv_dur_2030
Schedule/Milestones
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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