maoh_micro_analytical_model_approach
The Location of Firms in the City of
Hamilton, Canada: A Micro-Analytical
Model Approach
By
Hanna Maoh and Pavlos Kanaroglou
McMaster University
Center for Spatial Analysis (CSpA)
School of Geography and Geology
51 st Annual North American Regional
Science Meeting, Seattle, WA, Nov 11 th –
13 th , 2004
Outline
Introduction
Study Area and Data
Modeling Approach
Results
Conclusion
Introduction
Sustainable cities via simulation models
The agent-based Approach
Firm demographic model for the city of
Hamilton in Ontario Canada
Location choice component (See diagram)
The evolutionary process of business establishment population over time
Baseline
Establishment population
(time t)
Stayers, relocating and out-migrants
+
In-migrating and new born establishments
Searching pool
Relocating,
Newborn and
In-migrating
Business failure
Submodel
Stayers
Location
Choice
Submodel
Continuer
Establishments Growth/Decline submodel
State transition
Submodel
Stayers at time t+1
Establishment population
(time t+1)
Relocating
Newborn
In-migrating
Time t+1
Data
Statistics Canada Business Register
Maintain annual information about business establishments in Canada; We used 1996 – 1997 and 2001 – 2002 data
Attributes: Establishment size, location (postal code and SGC), SIC code and Establishment
Number (EN)
Census population Data for 1996 and 2001
GIS data: Land use cover, road network, regional malls
Modeling Approach
Establishments search the city for the location that will maximize their profit
Searching pool: relocating, new born and in-migrating establishments
Measuring firmographic events:
Continuer establishments : if for two consecutive years, the establishment has the same EN and the Hamilton SGC
Relocating establishments : if a continuer establishment has a different postal code address or coordinates between two consecutive years
Newborn establishments : if the establishment has an EN number in year t
+ 1 which did not exist in year t
In-migrating establishments : Those with the same EN in two consecutive years, but with a different SGC, and an SGC in Hamilton for the later year
Representing space
Bidding process
Establishments in the pool will out-bid each other for a particular location which will be assigned to the highest bidder
The bidding and maximizing profit processes can be modeled using discrete choice models (Martinez, 1992)
Space at the micro-level
Use boundaries of developed land parcels; but postal code addresses has a one-to-many relationship with parcel
Alternatively
Divide the city into grid cells of 200 x 200 meters; extract grid cells that correspond to developed commercial and industrial land uses to create the set of alternative locations
We employ a MNL model to handle the location choice decisions:
P n exp( V ni
)
(i)
= ___________
exp( V nj
) j
We model the location choice problem by major economic sectors
Creation of Choice set:
Grid cells resulted into a large choice set of 2635 and
2855 alternatives (cells) in the two periods 1996-1997 and 2001 – 2002, respectively
Therefore
Random sample of alternatives (McFadden, 1978) : 9 randomly selected cells (locations) in addition to the chosen cell (location)
Linear in parameter systematic utility V ni of: is a function
Location characteristics and establishment attributes
Model Specification
Model specification is based on information we gathered from the urban economic literature and the available data
Location specific factors included:
Distance to CBD ( CBDPRO )
Main road and highway proximity ( MRHWYPRO )
Regional Mall proximity ( MALLPRO )
Measures of Agglomeration economies ( AGGLO n
); n is economic sector
Geography classification: Inner suburbs ( MOUNTAIN ) and outer suburbs
( SUBURBS )
Density of new residential development ( NEWDEVELOP )
Density of old residential development ( OLDDEVELOP )
Population density ( POPDENS ) and Household density ( HHLDDENS )
Household income density ( HHLDINCDENS )
Average Housing value density ( AVGDWELLVAL )
Percentage of a particular land use at a given location ( LANDUSE k
); k is type of land use
Firm specific factors included:
Dummies to reflect firmographic event ( NEWBORN ) and type of industry the firm belongs to ( INDUSTRY sic
); SIC is 2-digit or 3-digit SIC code
Estimation Results
Most firms in Hamilton prefer locating on land far away from the CBD
Central location is important for:
Printing, publishing, and allied manufacturing firms (SIC 28),
Communication and utilities firms SIC(48 – 49)
Food, beverage drug and tobacco wholesale firms,
Finance insurance, business services, accommodation food and beverages and other services
New born manufacturing firms (i.e: incubation plant hypothesis )
Main road and highway proximity is important for all firms except for
All construction firms except for Electrical work firms (SIC 426)
Other product wholesale trade firms (SIC59)
NEWBORN Health and social services AND accommodation food and beverage firms favor land in close proximity to main roads and highways
Land in proximity to Regional Malls attracts retail trade firms specialized in food, beverage and drugs (SIC60), apparel, fabric and yarn (SIC 61) and general retailing stores (SIC 65).
Construction, communication and transportation firms avoid land in close proximity to regional malls
Agglomeration economies is prominent in the city of Hamilton.
All firms seems to appreciate the externalities associated with clustering in the local market
All Construction firms except for electrical work firms (SIC 426) favor locating in the inner suburbs above the escarpment.
Other services firms show affiliation of location in the inner suburbs area
Wholesale trade and retail trade firms show evidence of suburbanization . This is true for all firms except for food stores
(SIC 601), gasoline service station firms (SIC 633), motor vehicle repair shops (SIC 635) and general merchandize stores
(SIC641)
Construction, wholesale trade, retail trade, real estate, businesses, and accommodation food and beverage firms favor locations with new residential development
Construction and retail show evidence of avoiding the location with old residential development
Manufacturing firms avoid highly populated areas
High Income Locations are attracting services and retail trade firms except for firms specialized in selling shoe, apparel fabric and yarn (SIC 61), household furniture, appliances and furnishing retail (SIC 62) and automotive vehicles parts and accessories sales and services (SIC 63)
Land use variables suggest that:
Construction, communication and transportation firms locate predominantly on open space land
Manufacturing and communication firms favor locations with resource and industrial land use .
Retail trade and services firms show high affiliation with commercial land use
General merchandize Stores (SIC 64) and SIC(65) show affiliation with residential land use areas (i.e: population oriented )
Service firms also show affiliation with governmental and open space land uses
Location behavior over time: An Example from the retail sector
Variable
HWYMRPRO
AGGLOM5
SUBURBS
NEWDEVLOP
OLDDEVLOP
HHLDINCDENS
LANDUSE4
LANDUSE5
INDUSTRY10 x MALLPRO
INDUSTRY11 x HHLDINCDENS
INDUSTRY12 x LANDUSE4
INDUSTRY13 x SUBURBS
No. of Observations
L(0)
L(B)
Rho
2
Adj. Rho
2
Parameter
1996 – 1997
0.376421
(3.168)
0.022177
(6.829)
0.426167
(2.294)
0.003345
(3.189)
-0.000629
(-2.734)
0.000002
(1.698)
-0.559876
(-2.719)
1.152924
(2.946)
0.650485
(2.890)
-0.000002
(-1.535)
0.643616
(1.888)
-0.425008
(-1.752)
396
-911.824
-812.938
0.10845
0.10544
-0.567610
(-2.192)
0.870484
(2.149)
0.704633
(2.018)
-0.000001
(-0.863)
-0.100075
(-0.255)
-0.494691
(-1.720)
303
-697.6833
-600.3443
0.13952
0.13571
Parameter
2001 – 2002
0.383930
(2.798)
0.049132
(8.889)
0.389474
(1.845)
0.001438
(1.706)
-0.000925
(-3.287)
0.000002
(1.844)
Estimation results of the
2001 – 2002 models
Suggest a consistency in the location choice
Behavior over time
Conclusion
The research was successful in extending the conventional firm location modeling approach to study location choice behavior at the micro-level
Results suggest that the estimated models have the potential to be used in an agent-based simulation model
Research highlight the potential of using
Statistics Canada Business Register to study firm location behavior and to develop agent-based firmographic models
Acknowledgments
We would like to thank Statistics Canada for supporting this research through their (2003 – 2004) Statistics Canada PhD
Research Stipend program.
We would like to acknowledge the Micro-Economic Analytical
Division (MEAD) for providing the corresponding author with office space for one year to facilitate his access to the Business
Register data.
Thanks go to Dr. John Baldwin, Dr. Mark Brown and Mr.
Desmond Beckstead for their useful discussions, input and assistance.
We are grateful to SSHRC for financial support through a
Standard Research Grant and a SSHRC doctoral fellowship
