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Rengarajan Satyanarain* & HO, Kim Hin / David Department of Real Estate

European Real Estate Society (ERES) conference paper. A conceptual approach to design the Knowledge Based Urban Development (KBUD) using Agent Based Modelling. Rengarajan Satyanarain* & HO, Kim Hin / David Department of Real Estate School of Design and Environment

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Rengarajan Satyanarain* & HO, Kim Hin / David Department of Real Estate

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  1. European Real Estate Society (ERES) conference paper A conceptual approach to design the Knowledge Based Urban Development (KBUD) using Agent Based Modelling Rengarajan Satyanarain* & HO, Kim Hin / David Department of Real Estate School of Design and Environment National University of Singapore *Email: Satyanarain@nus.edu.sg

  2. Introduction: what are knowledge based urban developments? 1

  3. Contents of the paper 2 We look at how to design (land use planning) a Knowledge Based Urban Development (KBUD) so as to enhance intra-cluster knowledge interactions. Research Implication Develop physical planning guidelines which would help urban planners create effective zoning (mixed-use) policies.

  4. Background : Influence of design on knowledge based work 3 Knowledge catalysing the process of technological innovation is undisputed in the Science and Technology (S&T) literature. Sources: Hargadon & Sutton, 1997; Kanter, 1988; I Nonaka & Konno, 1998 Individuals working in knowledge intensive industries require information resources [Medium of access] E.g. Face-to-Face, Journal articles and other forms of media (television, internet, newspapers etc.) Face-to-face (F2F contact ) Sources:Allen (1984) ; Ancona,1990 ;Ancona and Caldwell’s ,1992;Audretsch & Feldman, 1996 ; Feldman, 2000; Storper & Venables, 2004 ; Interaction with peersF2F  Productive/innovative

  5. Background: Workspace design / Urban scale designs 4 Workspace planning /design studies for knowledge based environments Space syntax Analysis: Exploit differences in spatial layouts, circulation systems, visibility, adjacencies, mean integration etc to maximize the probability of interaction. Scale : Building Sources : Backhouse & Drew, 1992; F Duffy, 1997; Penn, Desyllas, & Vaughan, 1999; Peponis et al., 2007; Serrato & Wîneman, 1999). Urban planning/design studies for knowledge based environments There are almost no studies looking at how to design interactive environments on an urban scale as required for KBUD. Scale : Precinct

  6. Research problem 3. 6 Designs have been Ad-hoc and experimental. Euclidian (single land use) Mixed use zoning vs. A mixed use design should promote “knowledge” interactions (planned and spontaneous) This is achieved through complimentary zoning Premise: some actors have higher chances of interaction than others.

  7. The research question 3. 7 • What is the urban design criteria of the knowledge based urban development ? Knowledge interactions Social Environmental Economic Transportation

  8. Knowledge/information interactions 8 What are knowledge interactions? “the continuous and dynamic interaction between tacit and explicit knowledge that happens at the individual, group ,institutional, organizational, and inter-organizational levels that leads to creation/sharing or transfer of knowledge” - Nonaka & Takeuchi (1995). Source: Alan frost (2003); Adapted from classification given by Asheim and Gertler, (2005)

  9. Knowledge/information interactions 8 Intra-cluster interactions Knowledge bases Source: Alan frost (2003); Adapted from classification given by Asheim and Gertler, (2005)

  10. Literature review – Current design practices 9 General rule of mixed land use designs for KBUD’s • Diversity • Triple helix model of Innovation . (Leydesdorff & Etzkowitz ,1998). • Geographical proximity “short distances literally bring people together, favour information contacts and facilitate the exchange of tacit knowledge. The larger the distance between agents, the less the intensity of these positive externalities, and the more difficult it becomes to transfer tacit knowledge” -Boschma, 2005 Interactive design = “Accommodate a diverse set of actors into a small area of land”

  11. Literature review – Current design practices 10 DMC Seoul KBUD design • a “futuristic info-media industrial complex”, has planned for a city street which is to host “entertainment and retail establishments, technology companies, prestige housing, R&D institutions, and universities”. • The same street supposedly would host leisure activities such as “theatres, cafés, stores, nightclubs and LCD screens as big as whole buildings”. Source: http://sap.mit.edu/resources/portfolio/seoul/

  12. Literature review - Knowledge interaction determinants 11 Spatial proximity maybe necessary Mixed land uses Not sufficient Other dimensions of proximity ..

  13. Literature review - Knowledge interaction determinants 12 • Source: Boschma (2005)

  14. Theoretical criteria of a knowledge interactive urban design 13 Interaction level (I) 0 Proximity 1 Lock-in Knowledge base Institutional Organizational Cognitive Lock-in

  15. A simple 2-Dimensional Illustration of ‘lock-in’ design effect 3. 14 *Illustrative purpose only E.g. Illustration of Design “lock-in effects” in a KBUD A) “Institutional lock-in” B) “Cognitive lock-in” A B

  16. Methodology

  17. Theoretical Model of design 15 ‘Optimal’ design =(Design criteria, Spatial constraints, Actors [Number & Distribution] ) Theoretical model of design (AGM) Design

  18. Methodology- Land use design models in planning 3. 16 • Land use design optimization problems • Single land use model: Meier,(1968) • Multiple land use: Correia and Madden,(1985); Davis and grant,(1987) Single objective Linear Programming methodology • Multiple land use : Kenneth (1965) ;Barber (1976); Arad and Berechman (1978);Williams and Revelle (1996); Makowski (1997);Janssen et al (2008); Multiple objective Regular grid (non-overlapping) No explicit representation of space Multiple objective Spatially explicit Multiple land use Overlapping

  19. Methodology- Agent based modeling 17 Typical Land use design model (MAS) Decision function S Physical definition (conceptual/real) Actor classification Constraints (limits of the system) Operational objective functions (evaluation) Self-select Agents criteria zones Unsatisfied constraints 1 2 3 4 Source:Ligtenberg et al, (2004)

  20. Actors in the KBUD 18 • Agents • Firm (high tech, service, business etc.) • University department (i) • Public research institute (PRI) • Private institute (PVRI) • Misc (Retail, commercial, housing etc) • Classification • J= Institution • K=Organization • L =Knowledge base (Asheim et al,2007) • M= Cognitive field Agents Size 100-500 hectares Embedded j k l m

  21. Theoretical model of design 17 Land use design Quantity variables Quality variables Location variables Types of land uses Zonal interaction Space constraints Source: Adapted from Kenneth Schlager,1965

  22. Theoretical model of design Where, 18 Quality variable Quantity variables

  23. Optimal design algorithm 19 Agent rules • Start • Define space [e.g. plot ratio, parcel size, road length etc] • Initiate agents (AIP). Occupy random position in space. • Minimize the mean distance between ‘related’ agents. [KI – Design criteria] • Upon reaching equilibrium, locate to the nearest available block. • If KI is unsatisfied, re-define space and repeat step 2. • If KI is satisfied. Initiate subsidiary agents (i.e. service ratio requirements). • End

  24. Agent base land use model (AGB-LUM)’s architecture 20 Spatial constraints Plot ratio Land parcels (no.) Minimum requirements (setbacks, accessory etc in sq m) Economic forecasts AIP KBUD system Agents KI criteria Design Type Knowledge bases Institutional Organizational Cognitive Subsidiary land use I) Planning ratios

  25. Future work

  26. Case study :One north KBUD system 21 Data Land use plans Planning ratios Plot ratio, Set backs etc Land use designs Source: JTC

  27. Phase 1 & 2-Biopolis-Land use distribution (by organization) 22 Organizational composition • Research institution • Technology firm • University (learning) • misc

  28. Model output 23 Input data 1) Agent Identification 2) Coordinate map 3) Rules 4) Planning ratio ( i.e. minimum requirements) Output data 1)Land use composition 2)Plot Ratios 3) Subsidiary land uses 4) Zonal maps (2-D)

  29. Research Contribution 3. 24 Land use design models in planning Agent based modeling literature Land use design models in planning KBUD Literature Governance , Institutional planning models , Planning metrics Urban design Linear programming Knowledge interaction criteria (KIC) KBUD Theoretical model of urban design (Our contribution) No theoretical basis on how to effectively mix land uses . Have not paid attention to the role of urban design in KBUD literature 2 Planning practice Previous urban design models have predominantly used linear programming methodology (LPM). 1 3

  30. Conclusion 25 • Our paper addresses the issue of urban design for knowledge based urban development. • Urban designs emphasizing spatial proximity (density) and diversity alone may not favor interactive environments. • Propose a theoretical framework for a design tool using ABM approach.

  31. The End 26 Thank you for listening Q&A

  32. Case study :One north KBUD system 21 Data Land use plans Planning ratios Plot ratio, Set backs etc Land use designs Source: JTC

  33. Design Parameter assumptions 22 Source: Authors,2013 & One north masterplan (2008)

  34. Model output 23 Input data 1) Agent Identification 2) Coordinate map 3) Rules 4) Planning ratio ( i.e. minimum requirements) Output data 1)Land use composition 2)Plot Ratios 3) Subsidiary land uses 4) Zonal maps (2-D)

  35. Theoretical model of design 23 One north-Biopolis Baseline (AIP) Source: One north masterplan,2008

  36. Theoretical model of design 24 Baseline scenario -2-Dimensional Knowledge base Composition-Analytical (Biomedical sciences) Retail Research Institutions Housing Green space Screenshots

  37. Phase 1 & 2-Biopolis-Land use distribution 25 Total population Knowledge base Composition

  38. Phase 1 & 2-Biopolis-Land use distribution (by instituition) 26 Land use design –Institutional base Institutional Composition Subsidiary land uses

  39. Phase 1 & 2-Biopolis-Land use distribution (by organization) 27 Organizational composition • Research institution • Technology firm • University (learning) • misc

  40. Fully populated model by institutional-Sample design 28 Design Type Knowledge base –High Institutional-High Public Private

  41. Model output 29 Input data 1) Agent Identification 2) Coordinate map 3) Rules 4) Planning ratio ( i.e. minimum requirements) Output data 1)Land use composition 2)Plot Ratios 3) Subsidiary land uses 4) Zonal maps (2-D)

  42. Summary of the paper 30 The paper provides a theoretical criteria to help design KBUD. Proposes an new methodology (AGM) to aid land use planning. Towards a more scientific and dynamic approach in designing mixed use developments. A flexible approach reduces reliance on long term designs.

  43. The ‘Lock-in’ design phenomenon Institutional ‘Lock-in’ Knowledge base ‘Lock-in’ Organizational ‘Lock-in’

  44. Why is it important? 5 • Design goals (criteria) are important for physical planning to take shape over time. • Effective zoning can help actors share resources efficiently. • It can prevent land use conflicts arising from different actors. • E.g. Housing Estates • Reduce commuting costs  less pollution. • Make amenities accessible by walk  Schools ,parks,retail etc.) • Social goals  fostering sense of community

  45. Research problem 2 : The design process 3. 4 Actori (T0,Tn ) iє [ University, public, private research institutes, firms, service companies etc] Defined land area divided into a set of N land parcels Urban design criteria Spatial Constraints {a,b…z} є N KI Urban design Zoning guidelines Uncertainty of participants Static urban designs Design Criteria for knowledge interaction 1 2 3

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