Land Use Scenario DevelopeRLand Use Scenario DevelopeR

LUSDRLUSDR

A Stochastic Microsimulation of A Stochastic Microsimulation of

Household and Business Location to Household and Business Location to

Support Strategic Land Use and Support Strategic Land Use and

Transportation PlanningTransportation Planning

Presentation to PNREC 2006Brian Gregor

Transportation Planning Analysis UnitMay 11, 2006

Presentation Outline

• Background:

– Planning and modeling concepts related to LUSDR.

– Description of the Jackson County regional problem solving study.

• Description of LUSDR

• Results

• Next steps

Short Background on Land Short Background on Land Use and Transportation Use and Transportation Planning and Modeling Planning and Modeling ConceptsConcepts

Land Use and Transportation Planning

Legend

County

Urban Growth Area

Poss. Urban Reserve

Highway

Poss. Highway

Urban development goesin urban growth boundaries.

County lands arepredominantly resourcezoning and very low densitydevelopment.

Urban reserves are future sourcesof land for inclusion into urbangrowth boundaries. They affect future transportation demand.Highway location affects

land development patterns.

Approaches to Transportation and Approaches to Transportation and Land Use ModelingLand Use Modeling

• Standard transport models assume one invariant land use allocation unresponsive to transportation.

• With integrated land use and transport models, land use allocations vary with transportation, but most are equilibrium models.

• LUSDR also varies land use with transportation, but there are many possible solutions.

Why Evaluate Alternative Land Use Why Evaluate Alternative Land Use Patterns? Patterns?

• The overall number of trips may not change much, but the patterns of trip origins and destinations will.

• Roadway traffic can be very different depending on the land use assumptions.

• Assessment of many plausible land use patterns help identify potential problems.

O

O

O O

D

D

D D

Modeling Multiple Scenarios to Assess Risks

A

B

A

0 2000 4000 6000 8000

05

15

25

Traffic Volume

Num

ber

of

Sce

na

rios

Num

ber

of

Sce

na

rios

B

0 2000 4000 6000 8000

05

15

25

35

Traffic Volume

• Goal of RPS: To achieve regional consensus on where urban reserves should be designated to accommodate a doubling of population.

• Modeling Objectives

– Develop a moderately large set of plausible future land use patterns.

– Model the effects of the different land use patterns on the transportation system.

– Identify key features of land use patterns affecting transportation performance.

Jackson County Regional Problem Solving Jackson County Regional Problem Solving (RPS) Study(RPS) Study

What is LUSDR and What is LUSDR and How Does It Work?How Does It Work?

LLand and UUse se SScenario cenario DDevelopeevelopeRR

• Creates variation through stochastic microsimulation.

• Stochastic means that there is a random component to the model but average behavior is replicated.

• Microsimulation means that individual household, business and development decisions are modeled.

Stochastic MicrosimulationStochastic Microsimulation

= all of these places meet requirements

Shopping center might be located here in one simulation

Might be located here in another simulation

residentialdevelopments

generatehouseholds

loc type

generateemployment

estab.

generateresidential develop-

ments

generatebusiness develop-

ments

selectdevelopments by

period

employmentestablishments

businessdevelopments

households

developmentsto site

sitedevelopments

developmentsby taz

balancesupply & demand

developmentssited inperiod

update landinventory

land inventorycomprehensive

plancompatibility

area

unit price

Iterate throughall periods

Iterate until all developments are sited

sizeworkers

age of headincome

tenurebldg. type

emp typenum emp

cluster type

num hhdev type

loc typearea

unit price

num empdev type

loc typearea

unit price dev perioddev period

loc typearea

unit pricelocation

taz attributesaffecting sitepreferences

slopeinterchange

traffic exp.accessibility

updateaccessibilities

population byage cohort

KEY

function

data

attribute

Start with Population by Age & Start with Population by Age & Total Population GrowthTotal Population Growth

0-4 10-14 20-24 30-34 40-44 50-54 60-64 70-74 80-84

Population by Age

Age

Pro

po

rtio

n

0.0

00

.01

0.0

20

.03

0.0

40

.05

0.0

6

HhSize Worker AgeOfHead Income Ownrent BldgtypeHh1 h2 w2 a1 i4 rent SFDHh2 h2 w1 a4 i2 rent SFDHh3 h2 w3 a1 i2 rent A5PHh4 h3 w2 a2 i3 own SFDHh5 h1 w2 a2 i1 own SFDHh6 h4 w4 a2 i5 own SFDHh7 h2 w3 a1 i3 rent SFDHh8 h2 w1 a2 i5 own SFDHh9 h2 w3 a3 i1 rent SFDHh10 h2 w1 a3 i5 own SFDHh11 h2 w1 a3 i4 rent SFAHh12 h3 w2 a4 i4 rent A24Hh13 h4 w3 a2 i4 rent MHHh14 h2 w2 a2 i2 own SFD

Example of Household Records Example of Household Records Generated by LUSDRGenerated by LUSDR

Comparisons of Observed and Comparisons of Observed and Estimated Household Building Estimated Household Building Type ProportionsType Proportions

Building Type Categories

1990 Census

1990 Estimated

2000 Census

2000 Estimated

Single Family Detached

63.9 63.7 63.9 63.8

Single Family Attached

2.2 3.1 2.2 3.1

2-4 Unit Apartment 7.6 8.0 8.3 7.7

5+ Unit Apartment 8.0 8.7 9.3 8.6

Mobile Home 17.5 16.0 14.9 16.4

Other 0.8 0.4 0.5 0.4

Assign to Assign to DevelopmentsDevelopments

HhSize Worker AgeOfHead Income Ownrent Bldgtype DevIdHh1 h1 w1 a1 i5 own SFDH SFDH-161Hh2 h1 w1 a1 i3 own MHpark MHpark-1Hh3 h1 w1 a1 i4 rent SFDM SFDM-126Hh4 h1 w1 a1 i1 rent SFDM SFDM-207Hh5 h1 w1 a1 i2 rent SFDM SFDM-758Hh6 h1 w1 a1 i2 rent SFDM SFDM-660

Development Sizes of SFDM Subdivisions

Subdivision Size

Nu

mb

er

0 100 200 300 400 500

01

00

30

05

00

Employment Generated from Employment Generated from HouseholdsHouseholds

• Total employment calculated from household workers

• Total split jointly into 2-digit NAICS types and cluster type (see below)

• Employment split into establishments (firms) based on size distributions

• Establishments grouped into clusters based on size distributions

Clusters Identified by Analyzing Tax Clusters Identified by Analyzing Tax Assessment, Buildings and Assessment, Buildings and Employment DataEmployment Data

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#S

#S

#S#S#S#S

#S

Generate Employment Establishments Generate Employment Establishments and Put in Developmentsand Put in Developments

Histogram of Observed and Simulated Firm Sizes

log(employment)

De

nsi

ty

0 2 4 6 8

0.0

0.1

0.2

0.3

Id DevType LocType NumEmp UnitArea TotArea UnitPrice Period ACC-3 ACC EmpGrp6 3 6256.882 22524.775 9.133772 p4 ACC-1 ACC EmpGrp6 98 6256.882 735809.323 9.133772 p1 ACC-12 ACC EmpGrp6 1 6256.882 7508.258 9.133772 p2 ACC-14 ACC EmpGrp6 2 6256.882 15016.517 9.133772 p4 ACC-8 ACC EmpGrp6 2 6256.882 15016.517 9.133772 p3 ACC-4 ACC EmpGrp6 6 6256.882 45049.550 9.133772 p1

selectdevelopments by

period

developmentsto site

identifycandidate

tazs

developmentsby taz

balancesupply & demand

developmentssited inperiod

update landinventory

land inventory

comprehensiveplan

compatibility

Iterate throughall periods

Iterate until all developments are sited

loc typearea

unit pricelocation

taz attributesaffecting sitepreferences

updateaccessibilities

slope

steep slopeinterchange

distancetrafficexposure

local hhaccessibility

local empaccessibility

regional hhaccessibility

regional empaccessibility

locationtype

area

unit price

Iterate through developments

candidate tazs

selectpreferred taz

calculatelocation probabilities

locationprobabilities

KEY

function

data

attribute

Candidates identified based on area in plan designations and plan compatibility.

Random draw from candidate tazs weighted by location probabilities.

For each taz, the area demanded in each plan category is allocated to developments.

Find suitable TAZs based on having Find suitable TAZs based on having enough land with compatible planning enough land with compatible planning categoriescategories

ModelType Res Com Ind Os Rur A5P 1.00 0.50 0.00 0.0 0.00 SFA 1.00 0.50 0.00 0.0 0.00 MHpark 1.00 0.50 0.20 0.0 0.00 MHsub 1.00 0.50 0.10 0.0 0.01 SFDH 1.00 0.50 0.10 0.0 0.01 SFDM 1.00 0.50 0.10 0.0 0.01 ACC 0.00 1.00 0.25 0.0 0.00 ADM 0.25 1.00 0.25 0.0 0.00 AGF 0.00 0.25 1.00 0.0 1.00 CNS 0.00 0.50 1.00 0.0 0.00 EDU 0.75 0.25 0.00 0.0 0.00 FIN 0.25 1.00 0.25 0.0 0.00FIN_CLUST 0.25 1.00 0.25 0.0 0.00 HLH 0.30 1.00 0.20 0.0 0.00HLH_CLUST 0.30 1.00 0.20 0.0 0.00 INF 0.25 1.00 0.25 0.0 0.00 MFG 0.00 0.25 1.00 0.0 0.00MFG_CLUST 0.00 0.25 1.00 0.0 0.00

ModelType Res Com Ind Os Rur MIN 0.00 0.00 1.00 0.0 1.00 MNG 0.25 1.00 0.25 0.0 0.00 OSV 0.25 1.00 0.25 0.0 0.00OSV_CLUST 0.25 1.00 0.25 0.0 0.00 PRF 0.25 1.00 0.25 0.0 0.00PRF_CLUST 0.25 1.00 0.25 0.0 0.00 PUB 0.75 1.00 0.50 0.0 0.00 REC 0.20 1.00 0.00 0.1 0.00 REL 0.00 0.75 0.50 0.0 0.00 RST 0.10 1.00 0.25 0.0 0.00RST_CLUST 0.10 1.00 0.25 0.0 0.00 RTL 0.25 1.00 0.25 0.0 0.00RTL_CLUST 0.25 1.00 0.25 0.0 0.00 TRN 0.00 0.50 1.00 0.0 0.00 UTL 0.00 0.50 1.00 0.0 0.00 WHL 0.00 0.25 1.00 0.0 0.00WHL_CLUST 0.00 0.25 1.00 0.0 0.00

Plan Compatibility Ratings

Construction & Manufacturing Preference Probabilities

Retail Preference Probabilities

ResultResultss

How to Interpret the Shapes ofHow to Interpret the Shapes ofBox Percentile PlotsBox Percentile Plots

3600

037

000

3800

039

000

Em

ploy

men

t

center line shows the

medianvalue

fifty percent of thevalues are between the

top and bottom horizontal lines

top and bottomshow the range

of values

the width indicatesthe relative frequencyof the value

5

3000

5000

7000

9000

1: Eagle Point

p1 p3 p5 p7 p9

3000

4000

5000

6000

2: White City

p1 p3 p5 p7 p9

8000

1200

016

000

2000

0 3: Central Point

p1 p3 p5 p7 p9

2000

3000

4000

5000

4: Jacksonville

p1 p3 p5 p7 p9

1400

018

000

5: West Medford

p1 p3 p5 p7 p9

2000

030

000

4000

0

6: East Medford

p1 p3 p5 p7 p9

4500

5500

7: Phoenix

p1 p3 p5 p7 p9 3500

4000

4500

5000

8: Talent

p1 p3 p5 p7 p9

1000

012

000

1400

0

9: Ashland

p1 p3 p5 p7 p9

Comparison of Households for Runs Starting at 2002(black) and 2030(red)(horizontal red line = 2030 base)

1000

3000

5000

1: Eagle Point

p1 p3 p5 p7 p9

6000

1000

014

000

2: White City

p1 p3 p5 p7 p9

4000

8000

1200

0

3: Central Point

p1 p3 p5 p7 p9

1000

1400

1800

4: Jacksonville

p1 p3 p5 p7 p9

2500

030

000

3500

0

5: West Medford

p1 p3 p5 p7 p9

2500

035

000

4500

0

6: East Medford

p1 p3 p5 p7 p9

2000

4000

6000

7: Phoenix

p1 p3 p5 p7 p9

2000

3000

4000

8: Talent

p1 p3 p5 p7 p9

9000

1000

011

000

9: Ashland

p1 p3 p5 p7 p9

Comparison of Employment for Runs Starting at 2002(black) and 2030(red)(horizontal red line = 2030 base)

Next Next StepsSteps

ObjectivesObjectives

• To make LUSDR a tool that can help with land use forecasting and strategic planning throughout the state.

• To integrate LUSDR with transportation models for metropolitan and small urban areas.

• To connect LUSDR models with the next statewide model.

TasksTasks

• Incorporate densification, mixed use development, and redevelopment into LUSDR.

• Fully connect LUSDR with travel demand models.

• Get data from more places and develop transferable components.

• Develop an interface and standard graphical outputs.