1. A CONCEPTUAL APPROACH TO DESIGN THE K NOWLEDGE B ASED U RBAN D EVELOPMENT (KBUD) USING A GENT B ASED M ODELLING Rengarajan Satyanarain* & HO, Kim Hin / David Department of Real Estate School of Design and Environment National University of Singapore *Email: Satyanarain@nus.edu.sg European Real Estate Society (ERES) conference paper

2. Introduction: what are knowledge based urban developments?

3. Contents of the paper Develop physical planning guidelines which would help urban planners create effective zoning (mixed-use) policies. 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

4. 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 Hargadon & Sutton, 1997Kanter, 1988I 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 ; Allen (1984) Ancona,1990 ;Ancona and Caldwell’s,1992;Audretsch & Feldman, 1996 ; Feldman, 2000; Storper & Venables, 2004 ; Background : Influence of design on knowledge based work Interaction with peers  F2F  Productive/innovative

5. 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). Backhouse & Drew, 1992F Duffy, 1997 Penn, Desyllas, & Vaughan, 1999Peponis et al., 2007Serrato & 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 Background: Workspace design / Urban scale designs

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

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

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

10. General rule of mixed land use designs for KBUD’s I. Diversity  Triple helix model of Innovation. (Leydesdorff & Etzkowitz,1998). II. 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, 2005Boschma, 2005 Literature review – Current design practices Interactive design = “ Accommodate a diverse set of actors into a small area of land ”

11. 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 ”. Literature review – Current design practices Source: http://sap.mit.edu/resources/portfolio/seoul/

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

13. Proximity factors Key dimension Proximity Too littleToo high Institution Trust (based on common institutions) OpportunismLock-in OrganizationalControl Network disruptionBureaucracy Knowledge baseBase gap Lack of common base Physical barrier for fertilisation Cognitive baseKnowledge gap Misunderstanding Unintended spillovers GeographicalDistanceAn optimal mix of agents on these terms can facilitate reduced physical barriers to knowledge interaction Source: Boschma (2005) Literature review - Knowledge interaction determinants

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

15. 3. A simple 2-Dimensional Illustration of ‘lock-in’ design effect *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. ‘Optimal’ design =(Design criteria, Spatial constraints, Actors [Number & Distribution] ) Theoretical model of design (AGM) Design Theoretical Model of design

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

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

20. Actors in the KBUD Size 100-500 hectares 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 kjlm Embedded

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

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

23. 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 Optimal design algorithm

24. KBUD system Agents Economic forecasts Design Type 1.Knowledge bases 2.Institutional 3.Organizational 4.Cognitive Design Type 1.Knowledge bases 2.Institutional 3.Organizational 4.Cognitive Subsidiary land use I) Planning ratios Subsidiary land use I) Planning ratios Spatial constraints 1.Plot ratio 2.Land parcels (no.) 3.Minimum requirements (setbacks, accessory etc in sq m) Spatial constraints 1.Plot ratio 2.Land parcels (no.) 3.Minimum requirements (setbacks, accessory etc in sq m) AIP KI criteria Agent base land use model (AGB-LUM)’s architecture

25. Future work

26. Case study :One north KBUD system Data 1.Land use plans 2.Planning ratios 3.Plot ratio, Set backs etc 4.Land use designs Source: JTC

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

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

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

30. 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. Conclusion

31. Thank you for listening Q&A The End

33. Data 1.Land use plans 2.Planning ratios 3.Plot ratio, Set backs etc 4.Land use designs Source: JTC Case study :One north KBUD system

34. AgentsAssumptions Technology Firm  Unit of occupation: Firm  Minimum number of persons/firm: 20  Space per person: 70 sq ft  Space per firm: 1500 sq ft Research institution  Unit of occupation: Department/firm  Minimum number of persons department/firm: 20  Space per person: 70 sq ft  Space per Department: 1500 sq ft Educational (university)  Unit of occupation: Department  MnoD : 10 departments  Space per department: 2000 sq ft Service firm  Unit of occupation: Firm  (Mno)persons/firm: 20  Space per person: 50 sq ft  Space per firm: 2000 sq ft Sub-Agents Subsidiary land use specifications Green space  Regional ratio of 6 sq m per person (entire development) Retail  3 sq m per person Housing  80 sq m per person Recreational  3 sq m per person Source: Authors,2013 & One north masterplan (2008) Design Parameter assumptions

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

36. O NE NORTH -B IOPOLIS B ASELINE (AIP) TypePercentage Space needed (GFA) in Sq ft. Characteristics Representative unit Agents Work 48%285,600Research institution/firms Dept./firm285 Live 40%130,400Housing Apartment unit D Learn 9%38,250 Educational [university, school etc] Department D Play 3%122(meters) Green space (80 %) Sports & recreation (20%) N.A D Total 100% 41250.64 [meters] Source: One north masterplan,2008 Theoretical model of design

37. B ASELINE SCENARIO -2-D IMENSIONAL Screenshots Research Institutions Retail HousingGreen space Knowledge base Composition-Analytical (Biomedical sciences) Theoretical model of design

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

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

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

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

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

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

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

45. 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. Why is it important? E.g. Housing Estates Reduce commuting costs  less pollution. Make amenities accessible by walk  Schools,parks,retail etc.) Social goals  fostering sense of community

46. 3. Research problem 2 : The design process Defined land area divided into a set of N land parcels Actor i Spatial Constraints {a,b…z} є N i є [ University, public, private research institutes, firms, service companies etc] (T 0, T n ) Urban design criteria Zoning guidelines 1 Uncertainty of participants Static urban designs Design Criteria for knowledge interaction 2 Urban design KI 3