1 firmography and its application to integrated modeling rolf moeckel | parsons brinckerhoff fifth...
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Firmography and its application to integrated modeling
Rolf Moeckel | Parsons Brinckerhoff
Fifth Oregon Symposium onIntegrated Land Use and Transport Models
Portland State University19 - 20 June 2008
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Introduction
Historic background
Employment simulation
Example 1: SEAM
Example 2: ILUMASS
Conclusion
Outline
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Businesses are major players in urban system
- There are more jobs as households
- About 15 to 20 percent of our daily traffic is business-related, if commuting is included it is almost half of our daily traffic
- Use of developable land
- Firms generate relevant emissions
- Relatively to their VMT, heavy trucks contribute disproportionately to emissions
Introduction
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Introduction
Historic background
Employment simulation today
Example 1: SEAM
Example 2: ILUMASS
Conclusion
Outline
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Von Thünen (1826)
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Raw material A
Raw material B
Consumption
Production location
Alfred Weber (1909)
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Hotelling (1929)
Weber (1909): Small firms that cluster have the same scale advantages as one large firm.
I
II
III
IV
Market A Market B
Market A Market B
Market A Market B
Market A Market B
Agglomeration effects
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Alonso (1964)
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Hansen (1959)j ij
ji cf
WA
)(
Wilson (1967) j
ijjijii cDOYXA exp
1
exp
jijjji cDYX
1
exp
iijiij cOXY
Accessibility
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Retailer A
Retailer B
Pro
babi
lity
to b
e ch
osen
Huff (1963)
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Early industrial period Post-industrial period
1. Workforce (Quantity) 1. Political conditions
2. Land, Location 2. Natural Quality
3. Workforce (Quality) 3. Workforce (Quality)
4. Capital 4. Capital
5. Natural Quality 5. Land, Location
6. Political conditions 6. Workforce (Quantity)
Spitzer 1991
Soft location factors
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Introduction
Historic background
Employment simulation
Example 1: SEAM
Example 2: ILUMASS
Conclusion
Outline
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His
tory
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Allocate jobs in Basic Sector
Allocate residential location of employees in Basic Sector
Calculate population densities
Calculate market areas for Retail Sector
Allocate jobs in Retail Sector
Allocate residential location of employees in Retail Sector
Equilibrium reached?no
End
yes
StartLowry (1964)
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INIMP: Industrial Impact Model (Putman 1967)
- Lowry model with 29 basic employment types
EMPAL: Employment Allocation Model (Putman 1983)
- Lowry model with 4 business types
Putman (1967/1983)
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Echenique et al. (1969)
- Lowry Model as starting point
- Production factors (output) and the needed input of other factors (input) are defined by input-output functions
- model iterates until equilibrium is reached
MEPLAN
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IRPUD Model
- Whereas the transport model reaches equilibrium, the land use is assumed to react with a time lag of several years.
- Mobility rate of firms is given exogenously, relocation is simulated based on location utility by Logit models.
IRPUD
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Landis/Zhang (1998): CUF
- Bid-auction approach for land development- Location choice simulated by Logit Models
Van Wissen (1999): SIMFIRMS
- The first large-scale microsimulation of firms- Simulates relocation and firmography
Microsimulation
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Discrete Choice
- Based on discrete choice theory of McFadden
- Commonly applies Logit models that are assumed to represent behavior under constraints well
- Explicitly introduces limited information
Bid-Auction approach
- Based on economic theory of Alonso
- Prices are immediate result of bid-auction process
- Iterates and reaches (almost) an equilibrium
- Assumes transparent market
Discrete choice versus Bid-auction approach
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PROs of simulating firms
- decision-taking unit is firm- if firm moves it is ensure that all employees relocate- induced relocation due to firm growth can be modeled
CONs of simulating firms
- is more complex- harder to calibrate due to lack of data and lack of theory- more prone to random effects due to Monte-Carlo
sampling
Simulation of firms versus simulation of jobs
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Introduction
Historic background
Employment simulation
Example 1: SEAM
Example 2: ILUMASS
Conclusion
Outline
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Zone Ind. 1 Ind. 2 … Ind. 23 Jobs by Zone 1 ui,f = 6.46 ui,f = 9.04 ui,f = 1.06 1,681 2 ui,f = 7.91 ui,f = 1.79 ui,f = 6.95 4,917 3 ui,f = 2.15 ui,f = 5.49 ui,f = 0.29 1,985
… 5002 ui,f = 18.47 ui,f = 1.30 ui,f = 4.82 615
Jobs by type 106,654 79,535 328,227 8,302,143
- Iterative Proportional Fitting used to estimate employment - Initial employment estimate based on location utility
If floorspace demand in a zone is higher than supply, its utility is reduced by a fixed factor.
SEAM: Simple Economic Allocation Model
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SEAM: Total employment validation
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Introduction
Historic background
Employment simulation
Example 1: SEAM
Example 2: ILUMASS
Conclusion
Outline
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This model simulates both population and firms microscopically.
The firm model simulates in random order
- birth of a new business
- growth or decline of a business
- closure of a business
- business relocation
ILUMASS: Events simulated for firms
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Economic cycles Economic restructuring
Employment growth and decline of existing firms
Birth and closure of firms
75%
25%
100
%
possible adjustment
Firmography: Growth/Decline and Birth/Death
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no change 8 % growth4 % decline
Simulating change of firm size
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Bu
sin
ess
Relo
cati
on
Considers moving?
Select a business
no
no
More sites?yes
End
no
Start
Select an alternative site as an offer
Check satisfaction at alternative site
Select a site and move business
Another business?yes
Really wants to move?
yes
no
yes
Simulating business relocation
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Replaceable location factors:
Non-replaceable location factors:
... 321 lllu
Sim
ula
tion
... 321 lllu
Location satisfaction of a business
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3.000
12.200
10.000 10.000 4.500
8.500
0 0
8.000
15.000
Bu
sin
ess
Relo
cati
on Example: Finding an premise
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> 1.2
λ
< -1.2
Base scenario
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Sce
nari
os
> 1.2
λ
< -1.2
Compact city scenario
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Sce
nari
os
> 1.2
λ
< -1.2
Decentralized concentration scenario
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Introduction
Historic background
Employment simulation
Example 1: SEAM
Example 2: ILUMASS
Conclusion
Outline
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Simulation of firms contains more uncertainty than simulation of households, because:
- Firms are more diverse than households- There is less established theory about firms- Historic events have a stronger impact of employment- Many firms are dependent on world economy- Less data is available for firms
There is a large variety for employment simulation, from simplistic to complex. There is no one model that served all purposes, the model choice heavily depends on the individual application.
Conclusions
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Integrated land-use transport modeling
Households
Dwellings
Person Transport
Businesses
Premises
Freight Transport
Accessibility
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Base ScenarioTotal Employment Change
Sce
nari
os
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Base ScenarioTotal Employment Change