Study on Development and Application of MAS for Impact Analysis of Large-scale Shopping Center Development

1
Study on Development and Application of MAS for
Impact Analysis of Large-scale Shopping Center
Development

• ZJ. Shen, M. Kawakami, P. Chen
• Kanazawa University, Japan
• 2006. DDSS

2
Contents

• Introduction
• Location Regulations for B-shops
• Framework of Shopsim-MAS
• Policy Scenarios Evaluation
• Discussion and Further Research

3
Background

• The commercial environment of many local cities
  in Japan is experiencing decline in their centre
  areas.
• Local governments have developed all kinds of
  city center generation policies to constrain this
  trend and revitalize the central city commercial
  environment.
• It is difficult to evaluate the potential impact
  of current policies on the city future due to the
  uncertainty inherent in urban system.
• MAS simulation is reconized as a tool to
  visualize impact of planning policies for
  presenting the complexity of the urban system.

4
Introduction

• Picture of center area in metropolitan

prosperous commercial street in Osaka
5
Introduction

• Pictures of center area in local city

Decline of commercial environment
6
Introduction

• Pictures of large-shopping mall

• In suburban area

7
Contents

• Introduction
• Location Regulations for B-shops
• Framework of Shopsim-MAS
• Policy Scenarios Evaluation
• Discussion and Further Research

8
Location Regulations for B-shops

• Planning regulations on location of large-scale
  shopping center
• (B-shop)

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Location Regulations for B-shops

• Land use zoning

10
Location Regulations for B-shops

• Bylaw regarding location and floor space

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Location Regulations for B-shops

• Principle scenarios for shopping center location
  as Decision table for developer agent (Bylaw
  regulations and land use zonings)

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Location Regulations for B-shops

• The location alternatives are limited in the
  possible areas according to land use zonings
  regulation and bylaw regarding large-scale
  shopping mall.
• These location alternatives reflect the different
  scenarios of commercial development.

13
Contents

• Introduction
• Location Regulations for B-shops
• Framework of Shopsim-MAS
• Policy Scenarios Evaluation
• Discussion and Further Research

14
Framework of Shopsim-MAS

• provincial city of Japan
• Mono central
• gt urban sprawl
• gt suburb house development
• gt large suburban shopping mall
• Poly central
• gt declination in inner city
• gt Policy change -gt location regulations of lager
  shopping mall

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Framework of Shopsim-MAS --- Shop choice
(percolation model)

• Percolation model for getting spatial pattern
• Percolation probability Ps or Pb
• Pb for shopping in B-shop
• Ps for shopping in S-shop
• Pb Ps 1
• If Pb gt 0.5 then percolation phenomenon will
  occur.
• To keep the S_shop market share, Ps should be
  more than 0.5

S-shop
B-shop
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Framework of Shopsim-MAS --- Shop choice
(percolation model)

• A random utility model for shopping Probability
  in Percolation model
• Agents (Household) shop choice of B_shop or
  S_shop
• Chose B_shop if Uib gt Uis
• Chose S_shop if Uis gt Uib

S-shop
B-shop
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Framework of Shopsim-MAS----Shop choice
(percolation model)
A random utility model for household shop
choice The random factor can be used to adjust
percolation probability, which will generate
diverse spatial patterns
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Framework of Shopsim-MAS----shop choice model

• According to local regulations of large
  scale shopping mall, influence factors of
  percolation probability should be set as
  location (set as decision table )and floor area.
  
• Xkij is the kth attribute describing store j
  attracting household i.,
• price X1j Pj and
• floor space X2j Sj (Price Pj is added by
  authors)
• distance Cij is a measure of the disutility of
  travel between site of household i and site of
  shop j. (Cij is added by authors)

• Shopping choice in simulation based on utility is
  deterministic process, as random factor to
  control individual choice.

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Framework of Shopsim-MAS --- Shop choice
(percolation model)

• Cij is a measure of the disutility of travel
  between site of household i and site of shop j.
• Percolation probability become unstable in this
  case, however it is relative to its spatial
  position.

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Framework of Shopsim-MAS --- Shop choice
(percolation model)

• percolation probability is shifted if household
  position is near ot far away from a shop.

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Framework of Shopsim-MAS----spatial pattern
(percolation model)

• random value was set to translate the probability
  of random utility model into simulation
• in Uib, 10000
• in Uis, 10000
• Rate of shoping
• in B_shop0.08
• in S_shop0.92
• price in S 300, in B 200
• travel cost 120/cell

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Framework of Shopsim-MAS----spatial pattern
(percolation model)

• random value was set to translate the probability
  of random utility model into simulation
• in Uib, 10000
• in Uis, 5000
• Rate of shoping
• in B_shop0.5
• in S_shop0.5
• price in S 300, in B 200
• travel cost 120/cell

23
Framework of Shopsim-MAS----spatial pattern
(percolation model)

• random value was set to translate the probability
  of random utility model into simulation
• in Uib, 5000
• in Uis, 5000
• Rate of shopping
• in B_shop0.94
• in S_shop0.06
• price in S 300, in B 200
• travel cost 120/cell

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Framework of Shopsim-MAS----spatial pattern
(percolation model)

• random value was set to translate the probability
  of random utility model into simulation
• in Uib, 2000
• in Uis, 2000
• Rate of shopping
• in B_shop0.16
• in S_shop0.84
• price in S 300, in B 200
• travel cost 120/cell

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Framework of Shopsim-MAS----spatial pattern
(percolation model)

• random value was set to translate the probability
  of random utility model into simulation
• in Uib, 500
• in Uis, 500
• (critical point)
• Rate of shoping
• in B_shop0.24
• in S_shop0.76
• price in S 300, in B 200
• travel cost 120/cell

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Framework of Shopsim-MAS ---shop choice model

• Therefore, percolation Probability of B_shop or
  S_shop is decided by Pj, Sj and Cij. For
  translating probability of random utility model
  into agents individual behavior, a random
  variable is defined.
• If percolation probability changed gradually, the
  spatial pattern of percolation will be changed
  gradually. This phenomenon can be used in the
  market share simulation using MAS.
• However, how about fitness of Individual shopping
  choice based on ramdam utility and percolation
  probability in simulation is still a further
  study.

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Framework of Shopsim-MAS----uban space and agents

• Urban Space
• Agents
• Planner
• Developer
• Shop
• S-shop
• B-shop
• Household

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Framework of Shopsim-MAS----object model
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Framework of Shopsim-MAS----Simulation Process

• The user of Shopsim-MAS defines a policy scenario
  to be implemented. -gt decision table
• The planner agent sets the spatial structure and
  initiates the scenario.
• S-shop agents and existing B-shop agents are
  created in the urban space. Household agents are
  created and distributed to the whole central city
  urban planning area.The developer agent places
  the new B-shop in urban space according to
  defined scenarios.
• The user sets the initial values of parameters
  .For clear simulation results, random value is
  set as 500 under critical point.
• Households then decide where to go shopping until
  their demands are fulfilled (demands of each
  household50).

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Contents

• Introduction
• Location Regulations for B-shops
• Framework of Shopsim-MAS
• Policy Scenarios Evaluation
• Discussion and Further Research

31
Policy Scenarios Evaluation----Define four cases
of scenario

• Base Scenario
• No new B-shop are permitted to develop
• Centre Activation(CA)
• B-shop can only locate in the centre
  commercial area without upper limitation for
  floor space.
• Railway Station Development (RSD)
• B-shop can only be opened near the station,
  with an upper limitation of 10000 m2.
• Neighbouring Commerce Promotion (NCP)
• B-shop can only locate in neighbour
  commercial area, with an upper limitation of 3000
  m2.

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Policy Scenarios Evaluation----Analysis of CA
scenario
Floor space
3000m2
5000m2
20000m2
15000m2
10000m2
8000m2
Market share of existing B-shop
Market share of the new B-shop
Market share of S-shop

• The spatial effects of CA scenario as shown in
  figures.
• It can be see that CA scenario do have effect in
  improving the market performance of the city
  centre, but may do severe harm to the centre
  S-shops at the same time if there is no
  limitation on B-shops scale.

Sale statistics in CA scenario
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Policy Scenarios Evaluation----Compare scenarios

• To compare three scenarios, the floor space of
  the new B-shop is set same as 3000m2.
• Both Figures show that in RSD and NCP scenario,
  the loss of market share of S-shops caused by the
  new B-shop is more than in CA scenario .
• Both Figures also indicate that CA scenario
  might be the only effective to improve center
  commerce among three scenarios.

Sale statistics of Center shops and S-shops
Base
CA
NCP
RSD
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Contents

• Introduction
• Location Regulations for B-shops
• Framework of Shopsim-MAS
• Policy Scenarios Evaluation
• Discussion and Further Research

35
Discussion and Further Research

• The use of MAS for impact analysis of large scale
  shopping center development regulations is
  proposed in this paper.
• By introducing real urban land use zoning to form
  agents behavior constraints, the Shopsim-MAS
  simulate the virtual urban space in a more
  practical way in the context of urban planning.
• Percolation model and random utility model are
  employed in this simulation and spatial pattern
  of the market share influenced by urban bylaw and
  planning regulations can be visualized.
• The simulation results of four possible policy
  scenarios indicate that to develop new B-shop in
  the city center might be an effective measure to
  improve commercial activity of city centre.
• However, how about the behavior of households
  (random factors in this simulation that will
  influence the spatial pattern of market share) ?