Who We AreMembership King, Kitsap, Pierce and Snohomish Counties70 cities4 PortsTribes State agencies7 Transit agenciesAssociate members

Over 3.4 million residentsAn estimated 1.9 million jobs

Challenges of GrowthIn 1950:1,200,000 People 500,000 Jobs

In 2000:3,300,000 People1,900,000 JobsBy 2040:5,000,000 People3,000,000 Jobs

What We DoKey Responsibilities Long range growth, economic and transportation planning Transportation funding Economic development coordination Regional dataForum for regional issues

Decision-Making

OrganizationFY 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 ProductsCurrent and Historical DataCensus tabulationsCovered EmploymentAnnual Pop & HH EstimatesForecasts (regional & sub-regional)Modeling (travel demand, air quality)GIS (analysis & mapping)Transportation Data CollectionSurveysCountsTransportation Finance Data & Forecasts

Some Questions We Get AskedImpacts on the regional economy from: Traffic congestionTransportation revenue increases (taxes, fees, tolls, etc.) Return on particular transportation investmentsAging population impacts

What types of questions do you get asked?

Transportation leadership you can trust.

Regional Economic & Demographic Forecasting

Regional (STEP) & Small Area Forecasts Two-Step, Top-Down ProcessSTEP (Synchronized Translator of Econometric Projections

EMPAL (Employment Allocation Model) DRAM (Disaggregate Residential Allocation Model)

Regional Forecasts (Pop, Emp, HH)4 County RegionIndividual Counties219 Forecast Analysis Zones

PSRC Model OrganizationRegional 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 / RevenueModelLand UseSketch Planning Tool-Index-

How the Models Work - STEPEconomic base theoryPre-1983, sectors were either export (basic) or local (non-basic)Revised to recognize aspect of both in each sectorExogenous US forecasts as inputHistorically purchased from vendorEconometric model equations forecast 116 endogenous variablesBoeing, Microsoft variables projected independently

How the Models Work - STEP BlocksEMPLOYMENTProductivity & output = employmentOUTPUTCore forecast blockPOPULATIONLagged link to employment growthINCOMEInd. employment, national wage rates Reg CPI

Switching from STEP to New Model (PSEF -?)RFP in 2004: Replacing STEP (NAICS data time series disruptions)Meet our MPO, RTPO, Interlocal Agreement ObligationsNAICS-friendlySupport both old and new land use modelsLong-range forecast ability out 30 yearsTransparency, ease of use and maintenance for staff

How the Models Work - PSEFNo Output BlockMixed Regression and ARIMA ModelNAICS Sectoring PlanQuarterly Trend and Forecast DataAnnual Forecasts at County-LevelWill be used as a waypoint for Small Area ForecastsE-views replaces Fortran

NAICS Sectoring Plan - PSEF

Other Variables - PSEF

Input Data - PSEFLong-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 ForecastsWatching for secondary variable output / consistencyAve HH SizeRecent Trends vs Long Range TrendsUS Exogenous ForecastsProductivity, GDP GrowthMember Jurisdiction Involvement

Questions of OthersLinking regional forecasts with: traffic congestion / travel model forecasts transportation revenue policy (taxes, fees, tolls, etc.) Recognizing aging populationLower Ave HH Size, different trip generation rates?

Transportation leadership you can trust.

Land Use Forecasting: DRAM & EMPAL

How the Models Work DRAM and EMPALBase Year EmploymentBase Year Pop & HHBase Year Land UseCurrent Yr EmploymentCurrent Yr Pop & HHCurrent Yr Land UseInitial Travel ImpedancesFrom PSRC Travel Demand ModelEMPALDRAM

DRAM/EMPAL Land Use Forecast DataTotal PopulationHousehold populationGroup Quarters population

Total HouseholdsPercent Multi-Family, Single FamilyIncome quartilesTotal Jobs By SectorManufacturingWTCU (Wholesale, Transportation, Communications, Utilities)RetailFIRES (Finance, Insurance, Real Estate, Services)Government and Education

Current Land Use Forecast Geography219 Forecast Analysis Zones (FAZs) Built from 2000 Census Tracts

Building Consensus for Models & ForecastsNo longer adopt forecastsBoards approval needed for RFPs and contractsInclude non-PSRC staff on RFP, interview teams for consultantsTACs for model and forecast workExtensive review & outreach through Regional Technical Forum monthly meetingsUrbanSim exampleMultiple 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 ModelAnalyze Effects of Land Use on TransportationAnalyze Multimodal AssignmentsPromote Common Use of DataManage Data NeedsAnalyze All Modes of TravelAnalyze Effects of Land Use PoliciesSupport Visualization TechniquesAnalyze Effects of Transportation Pricing PoliciesAnalyze Effects of Growth Management PoliciesAnalyze 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 useMore flexible reporting geographyOur DRAM/EMPAL Limitations:Zonal geographyNo implicit land use plan inputs Direction from PSRC Boards during Destination 2030 Update = Improve land use modeling abilityRFQ issued in 2002Entered into interagency agreement and annual contracts with UW Center for Urban Simulation and Policy Analysis (CUSPA Dr. Paul Waddell) = The UrbanSim Model

UrbanSim OverviewModeling Actors instead of zonesNotable AdvantagesPotential new output (built SQFT, land value)Direct modeling of land use plans, development constraints such as wetlands, floodplains, etc.Geographic flexibilityVery Data HungryAssessors files, Census, Employment Data (Key Input), Land Use plans, Environmental constraintsModeled Unit = 150 Meter Grid cell (5.5 Acres)Roughly 790,000 in region (versus 219 FAZs)http://www.urbansim.org/

UrbanSim Schematic

Changes in Land Use Forecasts: EmploymentExisting EMPAL Detail: Total Jobs By SectorManufacturingWTCU (Wholesale, Transportation, Communications, Utilities)RetailFIRES (Finance, Insurance, Real Estate, Services)Government and EducationUrbanSim Detail: One Record per Job

Changes in Land Use Forecasts: ResidentialExisting DRAM Detail: Total PopulationHousehold populationGroup Quarters populationTotal HouseholdsPercent Multi-Family, Single FamilyIncome quartiles

UrbanSim Detail: One Record for each Household

Changes in Land Use Forecasts: Land Use DataNEW INPUTS: Implicit to Model compared to DRAM/EMPALAssessors FilesLand Use DesignationsEnvironmental AreasLand and Building Assessed Value

New Land Use Categories: PLUs and DevType IDsPlanned Land Use (PLU) = Comprehensive Plan designations in UrbanSimDevelopment Type IDs = Built attributes of each grid cell, based onHousing UnitsNon-Residential Square FeetEnvironmental Overlays

UrbanSim Data: Plan Types (Comprehensive Land Use Plans)Model Comp Plan Designations ImplicitlyFour-County Aggregate ClassificationsPart of Model Specification (Cant 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 cellMeasures of units/square feet of built environmentPart of Model Specification (Cant 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 CategoriesTriple Balancing ActDetail in comp plansJob categoriesDevelopment Type IDsAssign each (660) comp plan code to PLUStarted with 20+, wound up with 19 final PLU codesMore detail in Residential, Commercial, Industrial, Mixed Use, and Government/Tribal/Military

New PLUs

Sample Maps of New PLUs

Comp Plan vs Zoning ExampleMixed Use in Comp Plan2-5 du/ac, Office, Comm BusMultiple Zoning ClassesR4R5

Comp Plan Descriptions & ConsistencyLight Yellow = Single Family High Density ResidentialWas in 12+ DU / Acre6 DU /Acre3-5 DU /Acre

Centroid vs Majority Rules Approach

New PLU Acreage Summaries

DevType IDs

Example: Development Constraints TableExample: RES-Light (1-4 DU/Acre)

PLU + DevTypeIDs = Development Constraints Table

Lessons Learned: Land Use ModelsInvolve local staff in data assembly issues and forecast results reviewPlan for the update and maintenance Staff retentionCUSPA automated a lot of data processing applicationsUnderestimated time spent on data cleaningAllow time for 2-3 loops, data assembly, model testingHard to gauge the correct altitude to fly at for dat cleaningIE Employment data to parcelsOther uses of base year dataReviewer concerns vs impacts on the model

Questions for OthersPlancast vs ForecastBalancing plans & comments against model results

How strict or loose to model comp plans?

Transportation leadership you can trust.

Regarding Employment Data

Different Employment DatabasesGeocoded PointsCovered employmentTotal employmentModeling employmentCovered employmentTotal employmentFactors to ESD TotalsFactors from STEP databaseSpecific adjustments1234

Assemble Employment DataES202 business inventory from Employment Securities DivisionGovernment and Educational Survey, PSRCAssign employment sectors (based on STEP model sectors)Manual verification of major employer geocoding to parcel

Parcels, Streets, and Manual MatchesArc-InfoArcviewInterns

Assign Employment to ParcelsProvides cross-checking of employment and parcel data (should be consistent)Automated procedures for assignment of businesses to parcelsOperates on one census block at a timeUses multiple decision rulesAddress of business falls between 2 parcelsAvailability of nonresidential SQFTTax-exempt propertiesSector to Land Use probability distribution by FAZ groupCheck for mis-geocoding to wrong blockField verification of algorithm on small sample of blocks

Impute Missing Data on ParcelsAutomated imputation procedures for:Land Use codeYear BuiltHousing UnitsSqftBased on spatial query of nearby parcels with similar characteristicsUses SQL queries and Perl scripts

Interagency Agreement: Restrictions on Data UseConfidentiality Require reviewers and users of database to sign agreementGeocoding accuracyTravel demand modelingGMA analysisSuppression Publication rules to prevent individual employers from being identifiedOne employer accounts for 80% or more of total employmentThere are less than 3 employersIf showing totals, suppression of one value means one other must be suppressed

Transportation leadership you can trust.

Appendix AStep-By-Step UrbanSim Data Assembly Methodology

UrbanSim Data Integration Process

UrbanSim Data PreparationCoverage: King, Kitsap, Pierce, SnohomishBase Year: 2000Input databases:Parcels from each county (2001)Employment data from ES202 and survey of Government and Educational EstablishmentsCensus data from PUMS, SF3Transportation model outputsEnvironmental GIS layersPlanning and political GIS layers

Major Steps in Data PreparationDetermine study area boundaryGenerate grid over study areaAssemble and standardize parcel dataImpute missing data on parcelsAssemble employment dataAssign employment to parcelsConvert Parcel data to gridConvert other GIS layers to gridAssign Development TypesSynthesize household databaseDiagnose data quality and make refinementsDocument data and process

1. Determine study area boundaryInitial application will be to 4-County Central Puget SoundKing, Kitsap, Pierce, SnohomishPotential later extension to other countiesIsland, Mason, Skagit, Thurston

2. Generate Grid Over Study AreaUses grid cell size of 150 x 150 metersAreas in water or outside project boundary coded as NODATA

150 Meter Grid Cells

3. Assemble and Standardize ParcelsParcel database assembly for all 4 countiesConversion of county land use codes to regional standardConsolidation of key fields:Lot sizeLand useHousing unitsSqft building spaceYear builtZoningLand use planAssessed land valueAssessed improvement valueMicrosoft Access VersionMySQL with Replication

Parcel DataParcel Counts:King County: 542,446Kitsap County:100,336Pierce County:260,230Snohomish County:211,677Region Total: 1,114,689

Generalized Land Uses - ParcelAgricultureCivic and Quasi-PublicCommercialFisheriesForest, harvestableForest, protectedGovernmentGroup QuartersHospital, Convalescent CenterIndustrialMilitaryMiningMobile Home Park

Generalized Land Uses - ParcelMulti-Family ResidentialOfficePark and Open SpaceParkingRecreationRight-of-WaySchoolSingle Family ResidentialTransportation, Communication, UtilitiesTribalVacantWarehousingWater

4. Impute Missing Data on ParcelsAutomated imputation procedures for:Land Use codeYear BuiltHousing UnitsSqftBased on spatial query of nearby parcels with similar characteristicsUses SQL queries and Perl scripts

5. Assemble Employment DataES202 business inventory from Employment Securities DivisionGovernment and Educational Survey, PSRCAssign employment sectors (based on STEP model sectors)Manual verification of major employer geocoding to parcel

6. Assign Employment to ParcelsProvides cross-checking of employment and parcel data (should be consistent)Automated procedures for assignment of businesses to parcelsOperates on one census block at a timeUses multiple decision rulesAddress of business falls between 2 parcelsAvailability of nonresidential SQFTTax-exempt propertiesSector to Land Use probability distribution by FAZ groupCheck for mis-geocoding to wrong blockField verification of algorithm on small sample of blocks

7. Convert Parcel Data to GridGIS overlay of parcels on gridcellsAllocate parcel quantities to gridcells in proportion to land area in each cellAggregate data in grid cellsConvert employment from parcel geocoding to grid cell

8. Convert Other GIS Layers to GridEnvironmental LayersCompleted:WaterWetlandsFloodplainsParks and Open SpaceNational ForestsPending need feedback on definitions to use for:Steep slopesStream buffers (riparian areas)

Convert Other GIS Layers to GridPlanning/Political LayersCompleted:CitiesCountiesUrban Growth BoundariesMilitaryMajor Public LandsTribal LandsNote: Current data sources may be replaced if better data are availableAll grid-based data stored as attributes on gridcells table

GIS Data Sources (Page 1)National Forests at 500kSource: Washington State Department of TransportationMilitary Bases at 500kSource: Washington State Department of TransportationShoreline Management Act StreamsSource: Washington State Department of Ecology Q3 Flood Data, King, Kitsap, Pierce, SnohomishSource: Washington State Department of EcologyState Tribal LandsSource: Washington State Department of EcologyNational Wetlands InventorySource: Puget Sound Regional CouncilProcedures: 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 SpaceSource: Puget Sound Regional CouncilProcedures: Regional Council staff collected the data from the four counties and their local jurisdictions. Major Public LandsSource: Puget Sound Regional CouncilProcedures: 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. WaterbodiesSource: Puget Sound Regional CouncilDEM30Source: Puget Sound Regional CouncilUrban Growth BoundarySource: Puget Sound Regional Council

9. Assign Development Types25 Development Types AssignedType 25 is Vacant UndevelopableComposite of characteristics used to assign:Percent of cell in water, wetland, floodplain, steep slope, public lands, etc.Need feedback on conditions to useImplication: undevelopable cells preserved in the modelAll 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 Types

Devtype

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 householdsBeckman (1995) developed household synthesis methodology for TRANSIMSWe extended Beckmans approach:Parcel-based housing countsDiscount by vacancy rate to get target household countAssign household characteristics:Joint probability distribution from PUMS IPF scale to tract marginal distributions from SF3Application of the synthesizer will need to wait for Census Bureau release of 5% PUMS

11. Diagnose data quality and make refinementsData Quality IndicatorsAutomated database queriesBefore and after each major imputation or allocation procedureDifferent geographic levels:ParcelGrid cell (150 meter)Census blockTAZFAZ GroupCityCounty

Data Quality IndicatorsExample: Parcels Missing Year BuiltKing13%Kitsap31%Pierce41%Snohomish19%

12. Document Data and ProcessOverview of Data ProcessingMajor steps, procedures, decisionsData SummariesData Quality IndicatorsBefore and after processingData Preparation Tools User GuideData imputationHousehold SynthesisJob AllocationConversion to gridAssignment of Development TypesData Quality Indicator Queries

Tribes: Muckleshoot and SuquamishState Agencies: WSDOT & Transportation commissionMandates: TEA-21, federal and state Clean Air Acts, GMA, federal Public Works and Economic Development Act.Rim of wheel = business practices hub of wheel = support systems Discussing couple of primary support systems we are responsible forKey questions we try to answer.Integrated set of models Strive for consistent inputsIn process of replacing STEP model NAICS time-series disruption

WA State = WA Projection and Simulation ModelUS = 800 equation-long TRENDLONG model by DRI

BEA = Income, W+S employmentMan = Manufacturing outputPop = Pop & HH in Census yearsOFM = Other yearsBLS = Time series data for input-output industries, est. of proprietorsESD = covered and W+S employment

Note that every update of STEP means recalibration of the equations, so they are reflecting current data trends best as possible. Note that confidentiality will still be applicableReally a series of sub-models, not designed to forecast how much growth the region will see, but given such a forecast, where will the growth locate, given inputs to the model such as travel times, land use plans, etc.Note that confidentiality will still be applicableSF MF not in current build, note one record for every householdDRAM/EMPAL relied on targets/ adjustments to reflect these things in the model process; had some acres of land estimates, but didnt play as large of a role in the model inputs.DRAM/EMPAL relied on targets/ adjustments to reflect these things in the model process; had some acres of land estimates, but didnt play as large of a role in the model inputs.DRAM/EMPAL relied on targets/ adjustments to reflect these things in the model process; had some acres of land estimates, but didnt play as large of a role in the model inputs.Before I wrap up, need to touch briefly on two important topics, Restrictions and Limitations

Once the geocoding accuracy review is completed, then database become available for travel demand modeling and GMA analysis by local member jurisdictions