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

3

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