Advertisement

Combining Agent-Based Modeling with Big Data Methods to Support Architectural and Urban Design

  • Matthias ScheutzEmail author
  • Thomas Mayer
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

Big Data analytics are increasingly used to discover potentially interesting patterns in large data sets. In this chapter, we discuss the potential of combining Big Data methods with those of agent-based simulations to support architectural and urban designs, for agent-based models allow for the generation of novel datasets to study hypothetical situations and thus designs. Specifically, we present two conceptual studies that investigate the utility of agent-based models in conjunction with Big Data analytics in the context of multi-level pedestrian areas and current office designs, respectively. The analyses of the case studies suggest that it will be worthwhile, both for urban designers and architects, to pursue a combined agent-based simulation Big Data analytics approach.

Keywords

Master Plan Urban Design Office Space Secondary Network Bonus System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Ali, S., Nishino, K., Manocha, D., Shah, M.: Modeling, Simulation and Visual Analysis of Crowds: A Multidisciplinary Perspective, p. 422. Springer Science & Business Media (2013)Google Scholar
  2. Batty, M., Jiang, B., Thurstain-Goodwin, M.: Local movement: agent-based models of pedestrian flows. CASA Working Papers, Centre for Advanced Spatial Analysis (UCL) London, UK. http://discovery.ucl.ac.uk/225/1/paper4.pdf (1998)
  3. Besner, J.: Historical Perspective and A Model of Partnership? Actualité immobilière, Special issue: Montréal Underground, UQAM, Montréal, 1991, pp. 3–11, 12–24 (1991)Google Scholar
  4. Besner, J.: Develop the underground space with a master plan or incentives. Associated research Centers for the Urban Underground Space. In: Proceedings of 11th ACUUS Conference, Athens, Greece 2007, pp. 1–7. http://observatoiredelavilleinterieure.ca/documents/ACUUS_XI-Besner.pdf (2007)
  5. Boddy, T.: Underground and overhead: building the analogous city. In: Sorkin, M. (ed.) Variations on a Theme Park: The New American City and the End of Public Space, New York, 1992, pp. 123–153 (1992)Google Scholar
  6. Boisvert, M.: Modeling pedestrian flows in Montreal’s indoor city. In: Associated research Centers for the Urban Underground Space, Proceedings of 10th ACUUS Conference, Moscow, Russia, pp. 1–24. http://www.observatoiredelavilleinterieure.ca/documents/ovi_moscow_2005.pdf (2005)
  7. Brennan, A., Chugh, J.S., Kline, T.: Traditional versus open office design: a longitudinal field study. Environ. Behav. 34, 279–299. http://senate.ucsf.edu/2013-2014/mb4-brennan%20et%20al%20article%20on%20moving%20into%20open%20space%20offices.pdf (2002)Google Scholar
  8. CABE-Commission for Architecture and the Built Environment and British Council for Offices: The impact of office design on business performance. CABE-Homepage, London, pp. 1–79. http://webarchive.nationalarchives.gov.uk/20110118095356/http://www.cabe.org.uk/publications/the-impact-of-office-design-on-business-performance (2005)
  9. CCD, Urban Center for Computation and Data: www.urbanCCD.org (2015)
  10. City of Toronto, City Planning Division: PATH Pedestrian Network, Master Plan. City of Toronto, Ontario. https://www1.toronto.ca/city_of_toronto/city_planning/transportation_planning/files/pdf/path_masterplan27jan12.pdf (2012)
  11. Cui, J., Allan, A., Lin, D.: Influencing factors for developing underground pedestrian systems in cities. In: Proceedings of Australasian Transport Research Forum 2011, Adelaide, Australia, 28–30 Sept 2011. http://www.atrf.info/papers/2011/2011_Cui_Allan_Lin.pdf (2011)
  12. Cui, J., Allan, A., Lin, D.: The development of grade separation pedestrian system: a review. In: Tunnelling and Underground Space Technology, vol. 38, Sept 2013, 151–160 http://research-hub.griffith.edu.au/display/nf8e897b0880aa4ade00e0d3e9a69251f (2013)Google Scholar
  13. Costa, F.F.: Big data in genomics: challenges and solutions (Is Life Sciences Prepared for a Big Data Revolution)? G.I.T. Lab. J. 1112, 1–4 (2012)Google Scholar
  14. Crawford, M.: The world in a shopping mall. In: Sorkin, M. (ed.) Variations on a Theme Park: The New American City and the End of Public Space, New York, pp. 3–30 (1992)Google Scholar
  15. Cull, B.: 3 Ways Big Data is Transforming Government. FCW http://fcw.com/articles/2013/09/25/big-data-transform-government.aspx (2013)
  16. CUSP, Center for Urban Science and Progress, NYU: http://cusp.nyu.edu/ (2015)
  17. Dainis c/o hongkiat.com: Creative & Modern Office Designs Around the World. Hongkiat design and technology weblog (hongkiat.com) http://www.hongkiat.com/blog/creative-modern-office-designs/ (2014)
  18. Danalet, A., Farooq, B., Bierlaire, M.: Towards an activity-based model for pedestrian facilities Swiss Transport Research Conference. In: Proceedings of 13th STRC Conference, Ascona, pp. 1–31. http://www.strc.ch/conferences/2013/Danalet_Farooq_STRC13.pdf (2013)
  19. DCP NYC; Department of City Planning City of New York: Midtown Manhattan Pedestrian Network Development Project. Official Website of the City of New York. http://www.nyc.gov/html/dcp/pdf/transportation/mmp1_full.pdf (2000)
  20. DCP NYC; Department of City Planning City of New York: Privately Owned Public Spaces/history. Official Website of the City of New York. http://www.nyc.gov/html/dcp/html/pops/pops_history.shtml (2014)
  21. Dijkstra, J., Timmermans, H., de Vries, B.: Activation of shopping pedestrian agents—empirical estimation results. Appl. Spat. Anal. Policy 6(4), 255–266. http://link.springer.com/article/10.1007%2Fs12061-012-9082-3 (2012)Google Scholar
  22. Duhigg, C.: How companies learn your secrets. In: New York Times Sunday Magazine, p. MM30, 19 Feb 2012. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html (2012)
  23. El-Geneidy, A., Kastelberger, L., Abdelhamid, H.T.: Montreal’s roots, exploring the growth of Montreal’s indoor city. J. Transp. Land Use Univ. Minn. 4(2), 33–46 http://tram.mcgill.ca/Research/Publications/Montreal_indoor_city_final_in_JTLU.pdf (2011)
  24. Gandomi, A., Haider, M.: Beyond the hype: big data concepts, methods, and analytics. Int. J. Inf. Manage. 35(2), 137–144 (2015)Google Scholar
  25. Gao, Y., Gu, N.: Complexity, human agents, and architectural design: a computational framework. Des. Principles Practices 3(6), 115–126. http://www.sciencedirect.com/science/article/pii/S2095263512000167 (2009)Google Scholar
  26. Grim, P., Mar, G.R., St. Denis, P.: The philosophical computer: exploratory essays in philosophical computer modeling, Bradford, p. 333 (1998)Google Scholar
  27. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S.K., Huse, G., Huth, A., Jepsen, J.U., Joergensen, C., Mooij, W.M., Mueller, B., Pe’er, G., Piou, C., Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Rueger, N., Strand, E., Souissi, S., Stillman, R.A., Vabo, R., Visser, U., DeAngelis, D.L.: A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198, 115–126 (2006)Google Scholar
  28. Jerde, C.: Design value through the lens of big data. Homepage Gensler (gensler.com): Gensler on Work. http://www.gensleron.com/work/2013/4/26/design-value-through-the-lens-of-big-data.html (2013)
  29. Kavulya, G., Gerber, D.J., Becerik-Gerber, B.: Designing in complex system interaction: Multi-agent based systems for early design decision making. In: Proceedings of the 28th ISARC International Association for Automation and Robotics in Construction, Seoul, Korea, pp. 694–698. http://www.iaarc.org/publications/fulltext/S21-2.pdf (2011)
  30. Lucarelli, F.: The Endless Interior: Calgary’s Plus 15 Skywalk System. Socks-studio.com online-magazine, Paris. http://socks-studio.com/2014/04/13/the-endless-interior-calgarys-plus-15-skywalk-system/ (2014)
  31. Mayer-Schönberger, V., Cukier, K.: Big data: a revolution that will transform how we live, work, and think. Eamon Dolan/Houghton Mifflin Harcourt (2013)Google Scholar
  32. Mitchum, R.: From Spreadsheets to Solutions: New Platform Enables Next Generation of Open City Data. University of Chicago-Homepage, Chicago (www.uchicago.edu); Articles 2014 https://www.ci.uchicago.edu/press-releases/spreadsheets-solutions-new-platform-enables-next-generation-open-city-data (2014)
  33. Moore, A.: Trading Density for Benefits: Section 37 Agreements in Toronto IMFG Perspectives. Toronto 2, 2–7. http://munkschool.utoronto.ca/imfg/uploads/221/imfg_perspectives___moore_%28feb_2013%29.pdf (2013)
  34. Raford, N., Ragland, D.: Pedestrian volume modeling for traffic safety and exposure analysis. In: Transportation Research Board, Washington, TRB 85th Annual Meeting Compendium of Papers CD-ROM. https://escholarship.org/uc/item/9cn8d3nq (2005)
  35. one simulation.com: Evacuating of people. Company-homepage, Leiden, NL http://www.onesimulations.com/index.php?p=fire_safety
  36. Pejtersen, J.H., Feveile, H., Christensen, K.B., Burr, H.: Sickness absence associated with shared and open-plan offices. Scand. J. Work Environ. Health 37(5), 376–382 (September 2011). http://www.istor.org/discover/10.2307/23064898?uid=3737528&uid=2&uid=4&sid=21104696972827 (2011)
  37. Remote Utilities: Employee monitoring and surveillance: important laws you should know remote utilities blog. Usoris Systems LLC, Seychelles http://www.remoteutilities.com/about/blog/RemoteUtilities/employee-monitoring-and-surveillance-important-laws-you-should-know/ (2013)
  38. Roelofsen, P.: The impact of office environments on employee performance. J. Facil. Manage. 1(3), 247–264. http://www.emeraldinsight.com/doi/abs/10.1108/14725960310807944 (2002)
  39. Scheutz, M., Harris, J.: An overview of the SimWorld agent-based grid experimentation system. In: Werner, D., Kurowski, K., Schott, B. (eds.) Large-Scale Computing Techniques for Complex System Simulations, Wiley (2011)Google Scholar
  40. Smith, A.D.: Online social networking and office environmental factors that affect worker productivity. Int. J. Procurement Manage. 6(5), 578–608. http://www.inderscience.com/info/inarticle.php?artid=56173 (2013)Google Scholar
  41. Solecki, W., Seto, K.C., Marcotullio, P.J.: It’s Time for an Urbanization Science. Environment Magazine, January–February http://www.environmentmagazine.org/Archives/Back%20Issues/2013/January-February%202013/urbanization-full.html (2013)
  42. Sorkin, M. (ed.): Variations on a Theme Park. Farrar, Straus and Giroux, New York. http://books.google.at/books/about/VariationsonaThemePark.html?id=QMhohDJgHIYC&rediresc=y (1992)
  43. Spacelab, Space Syntax Laboratory at The Bartlett, Centre for Advanced Spatial Analysis: Knowledge Transfer Partnership project ‘Big Data in the Office’. Space-Syntax-Homepage https://www.bartlett.ucl.ac.uk/space-syntax/research/projects/ktp_big_data_in_the_office (2014)
  44. Sussman, A., Hollander, J.: Cognitive Architecture: Designing for How We Respond to the Built Environment, Routledge (2015)Google Scholar
  45. Timmermans, H. (ed.): Pedestrian Behavior: Models, Data Collection and Applications. Emerald Group Publishing, Bingley UK (2009)Google Scholar
  46. Torrens, P.M., McDaniel, A.: Modeling geographic behavior in riotous crowds. Ann. Assoc. Am. Geogr. 103(1), 20–46 (2013)Google Scholar
  47. Urban Redevelopment Authority: Central area underground master plan revisions to the cash grant incentive scheme for underground pedestrian links. Official Website of the URA Singapore http://www.ura.gov.sg/uol/circulars/2012/aug/dc12-12.aspx (2014)
  48. Urban Systems Collaborative: http://urbansystemscollaborative.org/ (2015)
  49. Urbitran Associates: Pedestrian Flow Modeling for Prototypical Maryland Cities. Maryland Department of Transportation, Hanover, MD. http://smartgrowth.umd.edu/assets/cliftondaviesallenraford_2004.pdf (2004)
  50. U.S. Department of Commerce: Space Allowance and Management Program. Official Website of United Department of Commerce http://www.osec.doc.gov/opog/dmp/daos/dao21721.html (2013)
  51. Vitra, A.G.: The Citizen-Office-Concept. Homepage Vitra-AG (vitra.com), Birsfelden, Switzerland. http://www.vitra.com/en-br/office/index-concepts/citizenoffice (2014)
  52. Waber, B., Magnolfi, J., Lindsay, G.: Workspaces That Move People. Harvard Business Review, October 2014. http://hbr.org/2014/10/workspaces-that-move-people/ar/2 (2014) (online-version)
  53. Yanagisawa, D., Kimura, A., Tomoeda, A., Ryosuke, N., Suma, Y., Ohtsuka, K., Nishinari, K.: Introduction of frictional and turning function for pedestrian outflow with an obstacle. Phys. Rev. E 80(3), 036110 (2009)Google Scholar
  54. Zachariadis, V.: An agent-based approach to the simulation of pedestrian movement and factors that control it. In: Proceedings of Computers in Urban Planning and Urban Management CUPUM 2005, pp. 1–16. London, UK. http://128.40.111.250/cupum/searchpapers/papers/paper372.pdf (2005)

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Tufts UniversityMedfordUSA
  2. 2.Independent ArchitectViennaAustria

Personalised recommendations