modeling and data at the puget sound regional council for a few dollars more
Download
Skip this Video
Download Presentation
Modeling and Data at the Puget Sound Regional Council: (For a Few Dollars More…)

Loading in 2 Seconds...

play fullscreen
1 / 88

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


  • 220 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Modeling and Data at the Puget Sound Regional Council: (For a Few Dollars More…)' - adamdaniel


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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 [email protected]

Jeff Frkonja [email protected]

Mark Simonson [email protected]

who we are
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
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
What We Do
  • Key Responsibilities
    • Long range growth, economic and transportation planning
    • Transportation funding
    • Economic development coordination
    • Regional data
    • Forum for regional issues
organization
Organization
  • FY 2006-07 Budget:
    • $6.6 Million DSA
    • ($20.2 Million Agency)
    • 17.3 DSA FTE
    • (51.0 FTE Agency)
data systems and analysis products
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
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?
regional step small area forecasts
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

psrc model organization
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-

how the models work step
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
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

switching from step to new model psef
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
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
input data 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
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
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?
how the models work dram and empal
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

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

DRAM/EMPAL Land Use Forecast Data
current land use forecast geography
Current Land Use Forecast Geography
  • 219 Forecast Analysis Zones (FAZs)
  • Built from 2000 Census Tracts
building consensus for models forecasts
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
survey results from 2001 study important aspects of land use model
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
land use model changes
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
urbansim overview
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)
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: Employment
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: Residential
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

Changes in Land Use Forecasts: Land Use Data
new land use categories plus and devtype ids
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
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
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
changing the plu categories
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
comp plan vs zoning example
Mixed Use in Comp Plan

2-5 du/ac, Office, Comm Bus

Multiple Zoning Classes

Comp Plan vs Zoning Example

R4

R5

comp plan descriptions consistency
Comp Plan Descriptions & Consistency
  • Light Yellow = Single Family High Density Residential…
    • Was in 12+ DU / Acre

6 DU /Acre

3-5 DU /Acre

example development constraints table
Example: Development Constraints Table

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

lessons learned land use models
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
Questions for Others
  • Plancast vs Forecast
    • Balancing plans & comments against model results
  • How strict or loose to model comp plans?
different employment databases
Different Employment Databases

Covered employment

Geocoded Points

2

Factors to ESD Totals

1

Factors from STEP database

Covered employment

Total employment

3

Total employment

4

“Modeling” employment

Specific adjustments

assemble employment data
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
assign employment to parcels
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
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
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
appendix a

Appendix A

Step-By-Step UrbanSim Data Assembly Methodology

urbansim data preparation
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
Major Steps in Data Preparation
  • Determine study area boundary
  • Generate grid over study area
  • Assemble and standardize parcel data
  • Impute missing data on parcels
  • Assemble employment data
  • Assign employment to parcels
  • Convert Parcel data to grid
  • Convert other GIS layers to grid
  • Assign Development Types
  • Synthesize household database
  • Diagnose data quality and make refinements
  • Document data and process
1 determine study area boundary
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
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
3 assemble and standardize parcels
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 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
Generalized Land Uses - Parcel
  • Agriculture
  • 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 parcel1
Generalized Land Uses - Parcel
  • Multi-Family Residential
  • 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
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
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
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
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
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
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
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
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
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
10 synthesize household database
10. Synthesize Household Database
  • Need 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
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
Data Quality Indicators
  • Example: Parcels Missing Year Built
    • King 13%
    • Kitsap 31%
    • Pierce 41%
    • Snohomish 19%
12 document data and process
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
ad