modeling and data at the puget sound regional council: (for a few dollars more…)

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 kmurphy@psrc.orgJeff Frkonja jfrkonja@psrc.org

Mark Simonson msimonson@psrc.org

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