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Data, Science and Cities

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Abstract

This chapter focusses on the key role played by big data and data science in evaluating scientific theories of the city, in helping us understand the real time city, and in helping us to plan better functioning cities. A crucial message is that big data on cities and data science are complementary and parallel to urban design and urban planning. While the focus in urban design and planning is on the “making” of cities, the focus in data science is developing the “understanding” of the processes and forms that make up a city. Finally, a scientific understanding should provide the evidence and information base on which planning and design decisions are shaped and enacted.

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References

  • Alizadeh T, Farid R, Sarkar S (2018) Towards understanding the socio-economic patterns of sharing economy in Australia: an investigation of Airbnb listings in Sydney and Melbourne metropolitan regions. Urban Policy Res 36(4):445–463. https://doi.org/10.1080/08111146.2018.1460269

    Article  Google Scholar 

  • Alizadeh T, Sarkar S, Burgoyne S (2019) Capturing citizen voice online: enabling smart participatory local government. Cities 95:102400

    Article  Google Scholar 

  • Alonso W (1964) Location and land use. Harvard University Press, Cambridge, MA

    Book  Google Scholar 

  • Anas A, Richard A, Small KA (1997) Urban spatial structure. The University of California Transportation Center Working Paper Series, UCTC No. 357

    Google Scholar 

  • Barthelemy M (2016) The structure and dynamics of cities: urban data analysis and theoretical modelling. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Batty M (2005) Cities and complexity: understanding cities with cellular automata, agent based models, and fractals. MIT Press, Cambridge, MA

    Google Scholar 

  • Batty M (2013a) Big data, smart cities and city planning. Dialogues Hum Geogr 3(3):274–279

    Article  Google Scholar 

  • Batty M (2013b) A new science of cities. MIT Press, Cambridge, MA

    Book  Google Scholar 

  • Batty M (2017) Big data and the city. Built Environ 42(3):31–43

    Google Scholar 

  • Batty M, Longley P (1994) Fractal cities. Academic Press, London

    Google Scholar 

  • Bettencourt LMA, Lobo J, Helbing D, Kuhnert K, West GB (2007) Growth, innovation, scaling, and the pace of life in cities. Proc Natl Acad Sci 104(17):7301–7306

    Article  Google Scholar 

  • Dhar V (2013) Data science and prediction. Assoc Comput Mach 56(12):64–73

    Google Scholar 

  • Fotheringham AS, Batty M, Longley P (1989) Diffusion-limited aggregation and the fractal nature of urban growth. Pap Reg Sci Assoc 67(1):55–69

    Article  Google Scholar 

  • Jacobs J (1961) The death and life of great American cities. Random House, New York

    Google Scholar 

  • Jacobs J (1969) The economy of cities. Radom House, New York

    Google Scholar 

  • Krugman P (1996) Confronting the mystery of urban hierarchy. J Jpn Int Econ 10:399–418

    Article  Google Scholar 

  • Kulish M, Richards A, Gillitzer C (2011) Urban structure and housing prices: some evidence from Australian cities. Research discussion paper: Reserve Bank of Australia, 2011-03, pp 1–46

    Google Scholar 

  • Makse HA, Andrade JS, Batty M, Havlin S, Stanley E (1998) Modeling urban growth patterns with correlated percolation. Phys Rev E 58:7054

    Article  Google Scholar 

  • Moylan E, Sarkar S (2019) Defining urban centres using alternative data sets. Transport Findings, online: https://transportfindings.org/article/8166-defining-urban-centres-using-alternative-data-sets

  • Myatt GJ (2007) Making sense of data: a practical guide to exploratory data analysis and data mining. Wiley, Hoboken

    Book  Google Scholar 

  • Negroponte N (1995) Being digital. Hodder and Stoughton, Australia and New Zealand

    Google Scholar 

  • Sarkar S (2019) Urban scaling and the geographic concentration of inequalities by city size. Environ Plan B 46(9):1627–1644. https://doi.org/10.1177/2399808318766070

    Article  Google Scholar 

  • Sarkar S, Phibbs P, Simpson R, Wasnik S (2018) The scaling of income distribution in Australia: possible relationships between urban allometry, city size, and economic inequality. Environ Plan B 45(4):603–622

    Google Scholar 

  • Sarkar S, Wu H, Levinson D (2019) Measuring polycentricity via network flows, spatial interaction and percolation. Urban Stud:1–21. https://doi.org/10.1177/0042098019832517

  • Schutt R, O’Neil C (2013) Doing data science: straight talk from the frontline. O’Reilly Media, Inc, Sebastopol

    Google Scholar 

  • Soltani A, Tanko M, Burke MI, Farid R (2015) Travel patterns of urban linear ferry passengers: analysis of smart card fare data for Brisbane, Queensland, Australia. Transp Res Rec: J Transp Res Board 2535:79–87. https://doi.org/10.3141/2535-09

    Article  Google Scholar 

  • Townsend AM (2013) Smart cities: big data, civic hackers, and the quest for a new utopia. W.W. Norton, New York

    Google Scholar 

  • von Thunen JH (1966) Von Thunen’s isolated state. Pergammon Press, Oxford

    Google Scholar 

  • Witten IH, Frank E, Hall MA, Pal C (2017) Data mining: practical machine learning tools and techniques, 3rd edn. Elsevier Science & Technology, San Diego

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Architecture, Design and Planning, The University of Sydney, Sydney, NSW, Australia

    Somwrita Sarkar

  2. Institute for Integrated and Intelligent Systems (IIIS), Griffith University, Mount Gravatt, QLD, Australia

    Reza Farid

Authors
  1. Somwrita Sarkar
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  2. Reza Farid
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Corresponding author

Correspondence to Somwrita Sarkar .

Editor information

Editors and Affiliations

  1. School of Architecture, Design and Planning, University of Sydney, Darlington, NSW, Australia

    Dallas Rogers , Adrienne Keane  & Tooran Alizadeh ,  & 

  2. Faculty of Arts and Social Sciences, University of Technology Sydney, Broadway, NSW, Australia

    Jacqueline Nelson

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Sarkar, S., Farid, R. (2020). Data, Science and Cities. In: Rogers, D., Keane, A., Alizadeh, T., Nelson, J. (eds) Understanding Urbanism. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-15-4386-9_11

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  • DOI: https://doi.org/10.1007/978-981-15-4386-9_11

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  • Publisher Name: Palgrave Macmillan, Singapore

  • Print ISBN: 978-981-15-4385-2

  • Online ISBN: 978-981-15-4386-9

  • eBook Packages: Social SciencesSocial Sciences (R0)

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