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Modeling and Data at the Puget Sound Regional Council: (For a Few Dollars More…)

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 kmurphy@psrc.org Jeff Frkonja jfrkonja@psrc.org Mark Simonson msimonson@psrc.org. Who We Are. Membership

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Modeling and Data at the Puget Sound Regional Council: (For a Few Dollars More…)

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  1. 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 kmurphy@psrc.org Jeff Frkonja jfrkonja@psrc.org Mark Simonson msimonson@psrc.org

  2. 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

  3. 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

  4. What We Do • Key Responsibilities • Long range growth, economic and transportation planning • Transportation funding • Economic development coordination • Regional data • Forum for regional issues

  5. Decision-Making

  6. Organization • FY 2006-07 Budget: • $6.6 Million DSA • ($20.2 Million Agency) • 17.3 DSA FTE • (51.0 FTE Agency)

  7. Business Practices to Support Systems

  8. 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

  9. 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?

  10. Regional Economic & Demographic Forecasting

  11. Regional (STEP) & Small Area Forecasts Regional Forecasts (Pop, Emp, HH) 4 County Region • Two-Step, Top-Down Process • STEP (Synchronized Translator of Econometric Projections • EMPAL (Employment Allocation Model) • DRAM (Disaggregate Residential Allocation Model) 219 Forecast Analysis Zones Individual Counties

  12. PSRC Model Organization Regional Forecast Model -STEP- -PSEF- Land Use Sketch Planning Tool -Index- Land Use Model -DRAM/EMPAL- -UrbanSim- Transportation Tax Base / Revenue Model Travel Demand Model -EMME/2 current- -EMME/2 improved- Air Quality Model (Emmissions) -Mobile 6-

  13. 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

  14. How the Models Work - STEP Blocks Productivity Spending Wage Rates & CPI Demand for Labor Force OUTPUT Core forecast block INCOME Ind. employment, national wage rates Reg CPI EMPLOYMENT Productivity & output = employment POPULATION Lagged link to employment growth

  15. 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

  16. 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

  17. NAICS Sectoring Plan - PSEF

  18. Other Variables - PSEF

  19. 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

  20. 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

  21. 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?

  22. Land Use Forecasting: DRAM & EMPAL

  23. How the Models Work – DRAM and EMPAL EMPAL DRAM Base Year Employment Current Yr Employment Base Year Pop & HH Current Yr Pop & HH Base Year Land Use Current Yr Land Use Initial Travel Impedances From PSRC Travel Demand Model

  24. 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 DRAM/EMPAL Land Use Forecast Data

  25. Current Land Use Forecast Geography • 219 Forecast Analysis Zones (FAZs) • Built from 2000 Census Tracts

  26. 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

  27. Land Use Forecasting: Moving to UrbanSim

  28. Survey Results from 2001 Study – Important Aspects of Land Use Model • Analyze Effects of Land Use on Transportation • Analyze Multimodal Assignments • Promote Common Use of Data • Manage Data Needs • Analyze All Modes of Travel • Analyze Effects of Land Use Policies • Support Visualization Techniques • Analyze Effects of Transportation Pricing Policies • Analyze Effects of Growth Management Policies • Analyze Effects of Transportation on Land Use

  29. Land Use Model Changes • Changing 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

  30. UrbanSim Overview http://www.urbansim.org/ • 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)

  31. UrbanSim Schematic

  32. 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: Employment

  33. 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: Residential

  34. NEW INPUTS: Implicit to Model compared to DRAM/EMPAL Assessor’s Files Land Use Designations Environmental Areas Land and Building Assessed Value Changes in Land Use Forecasts: Land Use Data

  35. 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

  36. 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

  37. 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

  38. Data Acquisition and Pre-Processing: Current LU (Development Type)

  39. Data Acquisition and Pre-Processing: Planned LU

  40. 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

  41. New PLUs

  42. Sample Maps of New PLUs

  43. Mixed Use in Comp Plan 2-5 du/ac, Office, Comm Bus Multiple Zoning Classes Comp Plan vs Zoning Example R4 R5

  44. Comp Plan Descriptions & Consistency • Light Yellow = Single Family High Density Residential… • Was in 12+ DU / Acre 6 DU /Acre 3-5 DU /Acre

  45. Centroid vs ‘Majority Rules’ Approach

  46. New PLU Acreage Summaries

  47. DevType IDs

  48. Example: Development Constraints Table Example: RES-Light (1-4 DU/Acre)

  49. PLU + DevTypeIDs = Development Constraints Table

  50. 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

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