maoh_micro_analytical_model_approach by liuqingyan

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									 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

  51st Annual North American Regional
 Science Meeting, Seattle, WA, Nov 11th –
               13th, 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            Stayers,                                     Searching pool
Establishment
 population
                      relocating    +   In-migrating and
                                           new born
                                                                      Relocating,
                         and                                         Newborn and
    (time t)         out-migrants        establishments
                                                                     In-migrating



 Business                                                            Location
  failure                                                             Choice
                       Stayers
 Submodel                                                            Submodel



  Continuer                                                            Relocating
Establishments    Growth/Decline               Stayers                  Newborn
                    submodel                  at time t+1             In-migrating
                                                                        Time t+1


   State
 transition                                                Establishment
 Submodel                                                   population
                                                             (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:
              exp(Vni)
    Pn(i) = ___________     We model the location choice problem by
                            major economic sectors
              exp(Vnj)
             j

   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 Vni is a function
    of:
         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 (AGGLOn); 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 (LANDUSEk); k is
        type of land use
   Firm specific factors included:
      Dummies to reflect firmographic event (NEWBORN) and type of industry
        the firm belongs to (INDUSTRYsic); 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
                            Parameter    Parameter
Variable                   1996 – 1997   2001 – 2002
HWYMRPRO                    0.376421      0.383930
                              (3.168)       (2.798)
AGGLOM5                     0.022177      0.049132
                              (6.829)       (8.889)
SUBURBS                     0.426167      0.389474
                              (2.294)       (1.845)

                                                       Estimation results of the
NEWDEVLOP                   0.003345      0.001438
                              (3.189)       (1.706)
OLDDEVLOP                   -0.000629    -0.000925

HHLDINCDENS
                              (-2.734)     (-3.287)    2001 – 2002 models
                            0.000002      0.000002

LANDUSE4
                              (1.698)
                            -0.559876
                                            (1.844)
                                         -0.567610
                                                       Suggest a consistency
LANDUSE5
                              (-2.719)
                            1.152924
                                           (-2.192)
                                          0.870484
                                                       in the location choice
INDUSTRY10 x MALLPRO
                              (2.946)
                            0.650485
                                            (2.149)
                                          0.704633
                                                       Behavior over time
                              (2.890)       (2.018)
INDUSTRY11 x HHLDINCDENS    -0.000002    -0.000001
                              (-1.535)     (-0.863)
INDUSTRY12 x LANDUSE4       0.643616     -0.100075
                              (1.888)      (-0.255)
INDUSTRY13 x SUBURBS        -0.425008    -0.494691
                              (-1.752)     (-1.720)
No. of Observations             396           303
L(0)                         -911.824    -697.6833
L(B)                         -812.938    -600.3443
Rho 2                        0.10845       0.13952
Adj. Rho 2                   0.10544       0.13571
                   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

								
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