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Modeling and Data at the Puget Sound Regional Council: (For a Few Dollars More…). COG/MPO Mini-Conference SANDAG Friday, July 29th, 2005 Kevin Murphy [email protected] Jeff Frkonja [email protected] Mark Simonson [email protected]. Who We Are. Membership - PowerPoint PPT Presentation
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Transportation leadership you can trust.
Modeling and Data at the Puget Sound Regional Council:
(For a Few Dollars More…)
COG/MPO Mini-ConferenceSANDAG
Friday, July 29th, 2005
Kevin Murphy [email protected] Frkonja [email protected]
Mark Simonson [email protected]
Who We Are
Membership
• King, Kitsap, Pierce and Snohomish Counties
• 70 cities
• 4 Ports
• Tribes
• State agencies
• 7 Transit agencies
• Associate members
Over 3.4 million residents
An estimated 1.9 million jobs
Challenges of Growth
In 1950:• 1,200,000 People
• 500,000 Jobs
In 2000:
• 3,300,000 People
• 1,900,000 Jobs
By 2040:
• 5,000,000 People
• 3,000,000 Jobs
What We Do
Key Responsibilities
• Long range growth, economic and transportation planning
• Transportation funding
• Economic development coordination
• Regional data
• Forum for regional issues
Decision-Making
Organization
FY 2006-07 Budget:
• $6.6 Million DSA
• ($20.2 Million Agency)
• 17.3 DSA FTE
• (51.0 FTE Agency)
Business Practices
to Support Systems
Data Systems And Analysis Products
Current and Historical Data
• Census tabulations
• Covered Employment
• Annual Pop & HH Estimates
Forecasts (regional & sub-regional)
Modeling (travel demand, air quality)
GIS (analysis & mapping)
Transportation Data Collection
• Surveys
• Counts
Transportation Finance Data & Forecasts
Some Questions We Get Asked
Impacts on the regional economy from:
• Traffic congestion
• Transportation revenue increases (taxes, fees, tolls, etc.)
Return on particular transportation investments
Aging population impacts
What types of questions do you get asked?
Transportation leadership you can trust.
Regional Economic & Demographic Forecasting
Regional Forecasts
(Pop, Emp, HH)
Regional (STEP) & Small Area Forecasts
Two-Step, Top-Down Process
• STEP (Synchronized Translator of Econometric Projections
• EMPAL (Employment Allocation Model)
• DRAM (Disaggregate Residential Allocation Model)
4 County Region
Individual Counties
219 Forecast Analysis Zones
PSRC Model Organization
Regional Forecast Model-STEP--PSEF-
Land Use Model-DRAM/EMPAL-
-UrbanSim-
Travel Demand Model-EMME/2 current-
-EMME/2 improved-Air Quality
Model(Emmissions)
-Mobile 6-
Transportation Tax Base / Revenue
Model
Land UseSketch
Planning Tool-Index-
How the Models Work - STEP
Economic base theory• Pre-1983, sectors were either export (basic) or local (non-
basic)
• Revised to recognize aspect of both in each sector
Exogenous US forecasts as input• Historically purchased from vendor
Econometric model equations forecast 116 endogenous variables
Boeing, Microsoft variables projected independently
How the Models Work - STEP Blocks
EMPLOYMENTProductivity & output =
employment
OUTPUTCore forecast block
POPULATIONLagged link to
employment growth
INCOMEInd. employment, national
wage rates Reg CPI
ProductivityProductivity SpendingSpending
Demand for Demand for Labor ForceLabor Force
Wage Rates & CPIWage Rates & CPI
Switching from STEP to New Model (PSEF -?)
RFP in 2004: Replacing STEP (NAICS data time series disruptions)
• Meet our MPO, RTPO, Interlocal Agreement Obligations
• NAICS-friendly
• Support both old and new land use models
• Long-range forecast ability out 30 years
• Transparency, ease of use and maintenance for staff
How the Models Work - PSEF
No Output Block
Mixed Regression and ARIMA Model
NAICS Sectoring Plan
Quarterly Trend and Forecast Data
Annual Forecasts at County-Level
• Will be used as a waypoint for Small Area Forecasts
E-views replaces Fortran
NAICS Sectoring Plan - PSEF
Other Variables - PSEF
Input Data - PSEF
Long-range US forecasts (Global Insight)
Regional trend data (1970-current)
• Census, BEA, Washington State ESD (BLS)
Just Wage & Salary Employment
• Total Employment will need to be a post-processing task
Lessons Learned: Regional Forecasts
Watching for secondary variable output / consistency
• Ave HH Size
• Recent Trends vs Long Range Trends
US Exogenous Forecasts
• Productivity, GDP Growth
Member Jurisdiction Involvement
Questions of Others
Linking regional forecasts with:
• traffic congestion / travel model forecasts
• transportation revenue policy (taxes, fees, tolls, etc.)
Recognizing aging population
• Lower Ave HH Size, different trip generation rates?
Transportation leadership you can trust.
Land Use Forecasting: DRAM & EMPAL
Base Year Employment
Base Year Pop & HH
Base Year Land Use
Current Yr Employment
Current Yr Pop & HH
Current Yr Land Use
Initial Travel Impedances
From PSRC Travel Demand Model
EMPAL DRAM
How the Models Work – DRAM and EMPAL
DRAM/EMPAL Land Use Forecast Data
Total Population
• Household population
• Group Quarters population
Total Households
• Percent Multi-Family, Single Family
• Income quartiles
Total Jobs By Sector
• Manufacturing
• WTCU (Wholesale, Transportation, Communications, Utilities)
• Retail
• FIRES (Finance, Insurance, Real Estate, Services)
• Government and Education
Current Land Use Forecast Geography
219 Forecast Analysis Zones (FAZs)
Built from 2000 Census Tracts
Building Consensus for Models & Forecasts
No longer adopt forecasts
Boards approval needed for RFPs and contracts
Include non-PSRC staff on RFP, interview teams for consultants
TACs for model and forecast work
Extensive review & outreach through Regional Technical Forum monthly meetings
UrbanSim example
• Multiple workshops to cover issues involved in implementing new model
Transportation leadership you can trust.
Land Use Forecasting: Moving to UrbanSim
Survey Results from 2001 Study – Important Aspects of Land Use
Model
1. Analyze Effects of Land Use on Transportation2. Analyze Multimodal Assignments3. Promote Common Use of Data4. Manage Data Needs5. Analyze All Modes of Travel6. Analyze Effects of Land Use Policies7. Support Visualization Techniques8. Analyze Effects of Transportation Pricing Policies9. Analyze Effects of Growth Management Policies10. Analyze Effects of Transportation on Land Use
Land Use Model ChangesChanging Demands: GMA and more complex analysis questions:
• More “what if” questions
• Model policies and land use impacts – Better interaction between transportation and land use
• More flexible reporting geography
Our DRAM/EMPAL Limitations:
• Zonal geography
• No implicit land use plan inputs
Direction from PSRC Boards during Destination 2030 Update = Improve land use modeling ability
RFQ issued in 2002
• Entered into interagency agreement and annual contracts with UW Center for Urban Simulation and Policy Analysis (CUSPA – Dr. Paul Waddell) = The UrbanSim Model
UrbanSim Overview
Modeling “Actors” instead of zones
Notable Advantages
• Potential new output (built SQFT, land value)
• Direct modeling of land use plans, development constraints such as wetlands, floodplains, etc.
• Geographic flexibility
Very Data Hungry
• Assessor’s files, Census, Employment Data (Key Input), Land Use plans, Environmental constraints
• Modeled Unit = 150 Meter Grid cell (5.5 Acres)
• Roughly 790,000 in region (versus 219 FAZs)
http://www.urbansim.org/
UrbanSim Schematic
ID SectorPSRC
Category1 Resource Res Con2 Construction Res Con3 Manufacturing - Aviation Manuf4 Manufacturing - Other Manuf5 Transportation WTCU6 Communications and Utilities WTCU7 Wholesale Trade WTCU8 Eating and Drinking Places Retail9 Other Retail Trade Retail10 Finance, Insurance, and Real Estate FIRES11 Producer Services FIRES12 Consumer Services FIRES13 Health Services FIRES14 Federal Government, Civilian Gov15 Federal Government, Military Gov16 Education, K-12 Educ17 Education, Higher Educ18 State, Local Government Gov
Changes in Land Use Forecasts: Employment
Existing EMPAL Detail: Total Jobs By Sector• Manufacturing
• WTCU (Wholesale, Transportation, Communications, Utilities)
• Retail
• FIRES (Finance, Insurance, Real Estate, Services)
• Government and Education
UrbanSim Detail: One Record per Job
Changes in Land Use Forecasts: Residential
Existing DRAM Detail: Total Population• Household population• Group Quarters population
Total Households• Percent Multi-Family, Single
Family• Income quartiles
UrbanSim Detail: One Record for each Household
Changes in Land Use Forecasts: Land Use Data
NEW INPUTS: Implicit to Model compared to DRAM/EMPAL
• Assessor’s Files
• Land Use Designations
• Environmental Areas
• Land and Building Assessed Value
New Land Use Categories: PLUs and DevType IDs
Planned Land Use (PLU) = Comprehensive Plan designations in UrbanSim
Development Type IDs = “Built” attributes of each grid cell, based on
• Housing Units
• Non-Residential Square Feet
• Environmental Overlays
UrbanSim Data: Plan Types (Comprehensive Land Use Plans)
Model Comp Plan Designations Implicitly
• Four-County Aggregate Classifications
• Part of Model Specification (Can’t add on the fly)
• One of two parts of the “Constraint” Process
UrbanSim: Development Type IDs (Built Land Use)
Or, Overall Land Use Mix of each Grid cell
• Measures of units/square feet of built environment
• Part of Model Specification (Can’t add on the fly)
• One of two parts of the “Constraint” Process
Data Acquisition and Pre-Processing: Current LU
(Development Type)
Data Acquisition and Pre-Processing: Planned LU
Changing the PLU Categories
Triple Balancing Act
• Detail in comp plans
• Job categories
• Development Type IDs
Assign each (660) comp plan code to PLU
• Started with 20+, wound up with 19 final PLU codes
• More detail in Residential, Commercial, Industrial, Mixed Use, and Government/Tribal/Military
New PLUs
Sample Maps of New PLUs
Comp Plan vs Zoning Example
Mixed Use in Comp Plan
• 2-5 du/ac, Office, Comm Bus
Multiple Zoning Classes
R4
R5
Comp Plan Descriptions & Consistency
Light Yellow = Single Family High Density Residential…
• Was in 12+ DU / Acre 6 DU /Acre
3-5 DU /Acre
Centroid vs ‘Majority Rules’ Approach
New PLU Acreage Summaries
DevType IDs
Example: Development Constraints Table
Example: RES-Light (1-
4 DU/Acre)
PLU + DevTypeIDs = Development Constraints Table
Lessons Learned: Land Use Models
Involve local staff in data assembly issues and forecast results review
Plan for the update and maintenance
• Staff retention
• CUSPA automated a lot of data processing applications
Underestimated time spent on data cleaning
• Allow time for 2-3 loops, data assembly, model testing
Hard to gauge the “correct altitude” to fly at for dat cleaning
• IE Employment data to parcels
• Other uses of base year data
• Reviewer concerns vs impacts on the model
Questions for Others
Plancast vs Forecast
• Balancing plans & comments against model results
How strict or loose to model comp plans?
Transportation leadership you can trust.
Regarding Employment Data
Different Employment Databases
Geocoded Points
Covered employment
Total employment
“Modeling” employment
Covered employment
Total employment
Factors to ESD Totals
Factors from STEP database
Specific adjustments
1
2
3
4
Assemble Employment Data
ES202 business inventory from Employment Securities Division
Government and Educational Survey, PSRC
Assign employment sectors (based on STEP model sectors)
Manual verification of major employer geocoding to parcel
Parcels, Streets, and Manual Matches
Arc-Info
Arcview
Interns
Assign Employment to Parcels
Provides cross-checking of employment and parcel data (should be consistent)
Automated procedures for assignment of businesses to parcels
• Operates on one census block at a time
• Uses multiple decision rules− Address of business falls between 2 parcels
− Availability of nonresidential SQFT
− Tax-exempt properties
− Sector to Land Use probability distribution by FAZ group
− Check for mis-geocoding to wrong block
• Field verification of algorithm on small sample of blocks
Impute Missing Data on Parcels
Automated imputation procedures for:
• Land Use code
• Year Built
• Housing Units
• Sqft
Based on spatial query of nearby parcels with similar characteristics
Uses SQL queries and Perl scripts
Interagency Agreement: Restrictions on Data Use
Confidentiality – Require reviewers and users of database to sign agreement• Geocoding accuracy
• Travel demand modeling
• GMA analysis
Suppression – Publication rules to prevent individual employers from being identified• One employer accounts for 80% or more of total employment
• There are less than 3 employers
• If showing totals, suppression of one value means one other must be suppressed
Transportation leadership you can trust.
Appendix AAppendix A
Step-By-Step UrbanSim Data Assembly Methodology
UrbanSim Data Integration Process
Parcel file
BusinessEstablishment
File
CensusPUMS, STF3
GIS Overlays:Environmental
UGBCity
CountyTraffic Zone
DataIntegrationProcess
Input Data
Jobs
JobIDSectorGridId
Households
HouseholdIDPersonsWorkersChildrenAge of HeadIncomeGridId
Data Store
Grid Cell
GridIdTotal Housing UnitsVacant Housing UnitsTotal Nonres SqftVacant Nonres SqftDevelopment TypeLand ValueResidential Imp ValueNonres Imp ValueEnviron OverlaysUGBCityCountyTraffic Zone
UrbanSim Data Preparation
Coverage: King, Kitsap, Pierce, Snohomish
Base Year: 2000
Input databases:
• Parcels from each county (2001)
• Employment data from ES202 and survey of Government and Educational Establishments
• Census data from PUMS, SF3
• Transportation model outputs
• Environmental GIS layers
• Planning and political GIS layers
Major Steps in Data Preparation
1. Determine study area boundary
2. Generate grid over study area
3. Assemble and standardize parcel data
4. Impute missing data on parcels
5. Assemble employment data
6. Assign employment to parcels
7. Convert Parcel data to grid
8. Convert other GIS layers to grid
9. Assign Development Types
10. Synthesize household database
11. Diagnose data quality and make refinements
12. Document data and process
1. Determine study area boundary
Initial application will be to 4-County Central Puget Sound
• King, Kitsap, Pierce, Snohomish
Potential later extension to other counties
• Island, Mason, Skagit, Thurston
2. Generate Grid Over Study Area
Uses grid cell size of 150 x 150 meters
Areas in water or outside project boundary coded as NODATA
150 Meter Grid Cells
3. Assemble and Standardize Parcels
Parcel database assembly for all 4 counties
• Conversion of county land use codes to regional standard
• Consolidation of key fields:− Lot size− Land use− Housing units− Sqft building space− Year built− Zoning− Land use plan− Assessed land value− Assessed improvement value
Microsoft Access Version
MySQL with Replication
Parcel Data
Parcel Counts:
• King County: 542,446
• Kitsap County: 100,336
• Pierce County: 260,230
• Snohomish County: 211,677
• Region Total: 1,114,689
Generalized Land Uses - Parcel
Civic and Quasi-Public
Commercial
Fisheries
Forest, harvestable
Forest, protected
Government
Group Quarters
Hospital, Convalescent Center
Industrial
Military
Mining
Mobile Home Park
Generalized Land Uses - Parcel
Office
Park and Open Space
Parking
Recreation
Right-of-Way
School
Single Family Residential
Transportation, Communication, Utilities
Tribal
Vacant
Warehousing
Water
4. Impute Missing Data on Parcels
Automated imputation procedures for:
• Land Use code
• Year Built
• Housing Units
• Sqft
Based on spatial query of nearby parcels with similar characteristics
Uses SQL queries and Perl scripts
5. Assemble Employment Data
ES202 business inventory from Employment Securities Division
Government and Educational Survey, PSRC
Assign employment sectors (based on STEP model sectors)
Manual verification of major employer geocoding to parcel
6. Assign Employment to Parcels
Provides cross-checking of employment and parcel data (should be consistent)
Automated procedures for assignment of businesses to parcels
• Operates on one census block at a time
• Uses multiple decision rules− Address of business falls between 2 parcels
− Availability of nonresidential SQFT
− Tax-exempt properties
− Sector to Land Use probability distribution by FAZ group
− Check for mis-geocoding to wrong block
• Field verification of algorithm on small sample of blocks
7. Convert Parcel Data to Grid
GIS overlay of parcels on gridcells
Allocate parcel quantities to gridcells in proportion to land area in each cell
Aggregate data in grid cells
Convert employment from parcel geocoding to grid cell
8. Convert Other GIS Layers to Grid
Environmental Layers
• Completed:− Water
− Wetlands
− Floodplains
− Parks and Open Space
− National Forests
• Pending – need feedback on definitions to use for:− Steep slopes
− Stream buffers (riparian areas)
Convert Other GIS Layers to Grid
Planning/Political Layers• Completed:
− Cities− Counties− Urban Growth Boundaries− Military− Major Public Lands− Tribal Lands
Note: Current data sources may be replaced if better data are available
All grid-based data stored as attributes on gridcells table
GIS Data Sources (Page 1)National Forests at 500k
• Source: Washington State Department of Transportation
Military Bases at 500k• Source: Washington State Department of Transportation
Shoreline Management Act – Streams• Source: Washington State Department of Ecology
Q3 Flood Data, King, Kitsap, Pierce, Snohomish• Source: Washington State Department of Ecology
State Tribal Lands• Source: Washington State Department of Ecology
National Wetlands Inventory• Source: Puget Sound Regional Council
• Procedures: The wetlands have been identified using high altitude aerial photography and classified by the Cowardin Classification Scheme.
GIS Data Sources (Page 2)Park and Open Space
• Source: Puget Sound Regional Council
• Procedures: Regional Council staff collected the data from the four counties and their local jurisdictions.
Major Public Lands• Source: Puget Sound Regional Council
• Procedures: Spatial delineation was digitized by the Department of Natural Resources Division of Information Technology from 1:100,000 DNR Public Lands Quads and Bureau of Land Management 1:100,000 Public Lands Quads.
Waterbodies• Source: Puget Sound Regional Council
DEM30• Source: Puget Sound Regional Council
Urban Growth Boundary• Source: Puget Sound Regional Council
9. Assign Development Types
25 Development Types Assigned
Type 25 is Vacant Undevelopable
• Composite of characteristics used to assign:− Percent of cell in water, wetland, floodplain, steep slope, public
lands, etc.
− Need feedback on conditions to use
− Implication: undevelopable cells preserved in the model
All cells not classified as Undevelopable are assigned a type using a lookup table based on the number of housing units, sqft of nonresidential space, and mix of uses
Development TypesDevtype Name UnitsLow UnitsHigh SqftLow SqftHigh Primary Use
1 R1 1 1 0 999 Residential
2 R2 2 4 0 999 Residential
3 R3 5 9 0 999 Residential
4 R4 10 14 0 2499 Residential
5 R5 15 21 0 2499 Residential
6 R6 22 30 0 2499 Residential
7 R7 31 75 0 4999 Residential
8 R8 76 65000 0 4999 Residential
9 M1 1 9 1000 4999 Mixed_R/ C
10 M2 10 30 2500 4999 Mixed_R/ C
11 M3 10 30 5000 24999 Mixed_R/ C
12 M4 10 30 25000 49999 Mixed_R/ C
13 M5 10 30 50000 9999999 Mixed_R/ C
14 M6 31 99999 5000 24999 Mixed_R/ C
15 M7 31 99999 25000 49999 Mixed_R/ C
16 M8 31 99999 50000 9999999 Mixed_R/ C
17 C1 0 0 1000 24999 Commercial
18 C2 0 9 25000 49999 Commercial
19 C3 0 9 50000 9999999 Commercial
20 I1 0 0 1000 24999 Industrial
21 I2 0 9 25000 49999 Industrial
22 I3 0 9 50000 9999999 Industrial
23 GV 0 99999 0 9999999 Government
24 VacantDevelopable 0 0 0 0 VacantDevelopable
25 Undevelopable 0 0 0 0 Undevelopable
10. Synthesize Household DatabaseNeed spatial distribution of households
Beckman (1995) developed household synthesis methodology for TRANSIMS
We extended Beckman’s approach:
• Parcel-based housing counts
• Discount by vacancy rate to get target household count
• Assign household characteristics:− Joint probability distribution from PUMS
− IPF scale to tract marginal distributions from SF3
Application of the synthesizer will need to wait for Census Bureau release of 5% PUMS
11. Diagnose data quality and make refinements
Data Quality Indicators
• Automated database queries
• Before and after each major imputation or allocation procedure
• Different geographic levels:− Parcel
− Grid cell (150 meter)
− Census block
− TAZ
− FAZ Group
− City
− County
Data Quality Indicators
Example: Parcels Missing Year Built
• King 13%
• Kitsap 31%
• Pierce 41%
• Snohomish 19%
12. Document Data and Process
Overview of Data Processing
• Major steps, procedures, decisions
Data Summaries
Data Quality Indicators
• Before and after processing
Data Preparation Tools – User Guide
• Data imputation
• Household Synthesis
• Job Allocation
• Conversion to grid
• Assignment of Development Types
• Data Quality Indicator Queries