a study of alternative land use forecasting models
DESCRIPTION
A Study of Alternative Land Use Forecasting Models. Soon Chung and Fang Zhao Lehman Center for Transportation Research Florida International University Miami, Florida 11th TRB National Transportation Planning Applications Conference Daytona Beach, Florida May 8, 2007. Acknowledgements. - PowerPoint PPT PresentationTRANSCRIPT
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A Study of Alternative Land Use Forecasting Models
Soon Chung and Fang ZhaoLehman Center for Transportation
ResearchFlorida International University
Miami, Florida
11th TRB National Transportation Planning Applications Conference
Daytona Beach, FloridaMay 8, 2007
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Acknowledgements
• This project was funded by the Florida Department of Transportation Systems Planning Office
• Terrence Corkery, AICP Project Manager
• Mr. Michael Neihart, Volusia County MPO
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Outline
• Introduction• Objectives• UrbanSim• Study Area• Design of Simulation• Validation• Test Scenarios• Simulation Results• Findings
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Introduction
• Transportation models need good land use forecasts
• Many land use models have been developed– Simple to complex – Integrated or not integrated– With/without economic theory
basis– Support community visioning– GIS platform
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Objectives
• Understanding the state-of-the-art and state-of-the-practice of land use models
• Determining the data requirements
• Identifying application issues• Investigating need for data
processing and interfacing FSUTMS
• Identifying future research and implementation issues
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What Is UrbanSim?• A land use microsimulation model• Developed by the University of
Washington• Provide new land use forecasting and
analysis capabilities– Based on economic theories – Model the interactions of markets and
policies, including dynamic disequilibrium– Design to interface activity-based models
• Open Source software – source code free to use, modify, and redistribute (available at www.urbansim.org)
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UrbanSim Users• US Users
– Seattle– Eugene-Springfield– Houston– Honolulu– Salt Lake City– Phoenix– Detroit
• Europe Users– Amsterdam– Paris– Zurich
• Middle East Users – Tel Aviv• Potential Users - Downloaded from 80
Different Countries
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UrbanSim Model Structure
Travel DemandModel System
ScenarioAssumptions
Economic andDemographic
Transition2
Location Choice4
Land Price6
Data Store
Travel ModelOutputs
Control Totals
MacroeconomicModel
User SpecifiedEvents
Accessibility1
Mobility3
Real EstateDevelopment
5SQL DatabaseData Export
GISVisualization
ExternalModels
UserInputs
Model Coordinator
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Sub-Models• accessibility-model• household-transition-model• employment-transition-model• household-relocation-choice-model• employment-relocation-choice-model• household-location-choice-model• employment-non-home-based-
location-choice-model• employment-home-based-location-
choice-model• scaling-procedure-for-jobs-model• land-price-model• developer-model
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Data Required• Grid Cells• Parcel Data• Property Tax Data• Employment Data (Info/USA)• Environmental Layers
– Water– Wetlands– Floodplains– Parks and open space– National forests– Steep slopes (DEM)– Stream buffers (riparian areas)
• Planning and Political Layers– Traffic Analysis Zones (TAZs)– Cities– Urban growth boundaries– Military– Major public lands– Tribal lands
• Streets
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Land Price Model• Linear Regression Model• Dependent Variable – natural
logarithm of the total land value within a grid cell
• Independent Variables– Site characteristics
• Development type• Land use plan• Environmental constraints
– Regional accessibility• Access to population and employment
– Urban design-scale• Land use mix and density• Proximity to highways and arterials
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Household Location Model
• Discrete Choice Model• Variables
– Housing Characteristics• Prices (cost to income ratio)• Development types (density, land use mix)• Housing age
– Regional accessibility• Job accessibility by auto-ownership group• Travel time to CBD and airport
– Urban design-scale (local accessibility)
• Neighborhood land use mix and density• Neighborhood employment
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Employment Location Model• Discrete Choice Model
– Employment Home-Based Location Model – Employment Non-Home-Based Location
Model
• Variables– Real Estate Characteristics
• Price• Development type (land use mix, density)
– Regional accessibility• Access to population• Travel time to CBD, airport
– Urban design-scale• Proximity to highway, arterials• Local agglomeration economies within and
between sectors: center formation
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Developer Model• Discrete Choice Model• Variables
– Site characteristics• Existing development characteristics• Land use plan• Environmental constraints
– Urban design-scale• Proximity to highway and arterials• Proximity to existing development• Neighborhood land use mix and property
values• Recent development in neighborhood
– Regional• Access to population and employment• Travel time to CBD, airport
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Possible Scenarios
• Macroeconomic Assumptions– Household and employment control
totals
• Development constraints– Can select any combination of
• Political and planning overlays• Environmental overlays• Land use plan designation
– Determine which development types cannot occur
• Transportation infrastructure• User-specified events
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Study Area Selection Criteria
• Have recent household survey data
• Up-to-date GIS data, including parcel-level property data
• Being relatively self-contained
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Volusia County• 1,263 square
miles• Population –
443,343 in 2000• Surrounded by
Flagler, Marion, Lake, Seminole, and Brevard counties (most rural)
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Volusia County Planning Regions
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Simulation Process
UrbanSim TranPlan
HouseholdsJobs
ZDATA1ZDATA2
Composite Utility by Auto OwnershipHighway Travel Times
Accessibility_InputTravel_DataZones
1 2
3
4
TP*
UrbanSim
TP
UrbanSim UrbanSim UrbanSimUrbanSim UrbanSim UrbanSim
TP TP TP TP TPTP
2000 2020201720132010200720052002
*TP: FSUTMS/TranPlan
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Validation
Model Output was compared with
1. Model results adopted in the LRTP
2. 2005 InfoUSA Employment
Data
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Cumulative Percentage of TAZs vs. Differences in Zonal Households and Population
0
20
40
60
80
100
<= -1000 <= -500 <= -200 <= -100 <= 0 <= 100 <= 200 <= 500 <= 1000 > 1000
Differences
Cum
ulat
ive
Per
cent
age
2000 Total Households
2000 Total Population
2010 Total Households
2010 Total Population
2020 Total Households
2020 Total Population
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Cumulative Percentage of Employment Differences between UrbanSim and the 2005 InfoUSA Data
0
10
20
30
40
50
60
70
80
90
100
<= -1000 <= -500 <= -200 <= -100 <= 0 <= 100 <= 200 <= 500 <= 1000 > 1000
Absolute Difference
Cum
ulat
ed P
erce
nt
Industrial Emp.
Commercial Emp.
Service Emp.
Total Emp.
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Spatial Distribution of Differences between UrbanSim and 2005 InfoUSA Data
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Five Scenarios
• 3 alternatives from Volusia County 2020 LRTP– Alternative #2
– Alternative #3
– Final Plan
• 3 projections (low, mid, high) of population from Bureau of Economic and Business Research
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Scenarios
ScenariosTransportation
AlternativeProjection
1 Final Medium
2 Alternative 2 Medium
3 Alternative 3 Medium
4 Final Low
5 Final High
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Comparison of Volumes in 2020LRTP - Scenario 1
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Spatial Distribution of Households from Scenario 12020 – Base Year
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Spatial Distribution of Employment from Scenario 12020 – Base Year
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Comparison of Households between the 2020 LRTP and Scenario1
Planning Region
1997 2000 2010 2020
LRTP Scenario 1 LRTP Scenario 1 LRTP Scenario 1
Northeast 84,022 80,899 106,863 96,383 119,592 97,987
Southeast 23,846 22,116 31,880 30,424 35,437 32,410
Central 5,689 6,754 18,312 8,087 29,739 8,505
Northwest 2,288 2,176 2,855 2,741 3,255 2,842
Central-west 19,178 19,145 24,590 25,992 27,705 28,937
Southwest 36,300 40,492 40,950 47,198 44,648 49,367
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Comparison of Employment between the 2020 LRTP and Scenario1
Planning Region
1997 2000 2010 2020
LRTP Scenario 1 LRTP Scenario 1 LRTP Scenario 1
Northeast 100,648 101,315 130,583 106,643 130,583 118,548
Southeast 16,220 15,103 28,919 19,039 28,919 23,803
Central 2,626 6,778 9,761 8,369 9,761 10,565
Northwest 3,789 3,602 3,809 2,927 3,809 3,055
Central-west 20,434 25,440 30,587 36,443 30,587 46,432
Southwest 17,340 20,591 24,984 26,049 24,984 31,969
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Comparison of Alternative 2 and Final Plan
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Comparison of Volumes in 2020Scenario 2 – Scenario 1
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Spatial Distribution of Households from Scenario 22020 – Base Year
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Spatial Distribution of Employment from Scenario 2 2020 – Base Year
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Comparison of Households between Scenario1 and Scenario 2
PlanningRegion
2010 2020
Scenario 1 Scenario 2 Scenario 1 Scenario 2
Northeast 96,383 96,515 97,987 98,145
Southeast 30,424 30,486 32,410 32,372
Central 8,087 8,077 8,505 8,492
Northwest 2,741 2,748 2,842 2,848
Central-west 25,992 25,993 28,937 28,766
Southwest 47,198 47,214 49,367 49,391
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Comparison of Employment between Scenario1 and Scenario 2
PlanningRegion
2010 2020
Scenario 1 Scenario 2 Scenario 1 Scenario 2
Northeast 106,643 106,420 118,548 118,318
Southeast 19,039 19,647 23,803 24,561
Central 8,369 8,133 10,565 10,096
Northwest 2,927 2,958 3,055 2,985
Central-west 36,443 36,427 46,432 46,676
Southwest 26,049 25,999 31,969 31,781
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Comparison of Alternative 3 and Final Plan
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Comparison of Volumes in 2020Scenario 3 – Scenario 1
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Spatial Distribution of Households from Scenario 3 2020 – Base Year
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Spatial Distribution of Employment from Scenario 32020 – Base Year
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Comparison of Households between Scenario1 and Scenario 3
PlanningRegion
2010 2020
Scenario 1 Scenario 3 Scenario 1 Scenario 3
Northeast 96,383 96,397 97,987 97,899
Southeast 30,424 30,470 32,410 32,210
Central 8,087 8,051 8,505 8,448
Northwest 2,741 2,740 2,842 2,847
Central-west 25,992 26,003 28,937 28,916
Southwest 47,198 47,215 49,367 49,263
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Comparison of Employment between Scenario1 and Scenario 3
PlanningRegion
2010 2020
Scenario 1 Scenario 3 Scenario 1 Scenario 3
Northeast 106,643 107,154 118,548 118,100
Southeast 19,039 19,599 23,803 24,726
Central 8,369 8,594 10,565 11,172
Northwest 2,927 2,943 3,055 2,974
Central-west 36,443 36,576 46,432 46,243
Southwest 26,049 25,781 31,969 31,255
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Comparison of Volumes in 2020 Scenario 4 – Scenario 1
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Comparison of Households between Scenario1 and Scenario 4
PlanningRegion
2010 2020
Scenario 1 Scenario 4 Scenario 1 Scenario 4
Northeast 96,383 78,728 97,987 63,487
Southeast 30,424 27,121 32,410 25,767
Central 8,087 8,053 8,505 8,135
Northwest 2,741 2,704 2,842 2,593
Central-west 25,992 25,311 28,937 26,405
Southwest 47,198 46,596 49,367 46,032
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Comparison of Employment between Scenario1 and Scenario 4
PlanningRegion
2010 2020
Scenario 1 Scenario 4 Scenario 1 Scenario 4
Northeast 106,643 91,757 118,548 82,841
Southeast 19,039 15,076 23,803 13,157
Central 8,369 6,402 10,565 7,373
Northwest 2,927 2,567 3,055 2,036
Central-west 36,443 28,679 46,432 27,235
Southwest 26,049 20,471 31,969 18,612
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Comparison of Volumes in 2020Scenario 5 – Scenario 1
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Comparison of Households between Scenario1 and Scenario 5
PlanningRegion
2010 2020
Scenario 1 Scenario 5 Scenario 1 Scenario 5
Northeast 96,383 96,325 97,987 97,766
Southeast 30,424 30,511 32,410 31,854
Central 8,087 8,002 8,505 8,310
Northwest 2,741 2,759 2,842 2,835
Central-west 25,992 25,687 28,937 28,183
Southwest 47,198 46,827 49,367 48,572
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Comparison of Employment between Scenario1 and Scenario 5
PlanningRegion
2010 2020
Scenario 1 Scenario 5 Scenario 1 Scenario 5
Northeast 106,643 115,927 118,548 136,304
Southeast 19,039 21,412 23,803 26,950
Central 8,369 8,969 10,565 12,147
Northwest 2,927 3,229 3,055 3,621
Central-west 36,443 38,156 46,432 50,922
Southwest 26,049 28,565 31,969 36,708
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Findings• High data requirements
– Impute missing data– Join property TAX data with parcel layer– Join employment data with parcel layer
• Data mining and synthetic data cleaning tools, currently being designed, will facilitate working with messy data
• Model estimation requires knowledge of discrete choice models
• Consultations with local government agencies are desirable in developing model specifications and estimating model parameters