Advertisement

Urban Chaos and the AI Messiah

  • Zaheer Allam
Chapter

Abstract

While there are predictions that the future will be highly urbanized, there are others stating that the urban world will be increasingly faced with the impacts of climate change, and cities are being pressured from various angles. Faced with this, the role of technology is being hailed and the possibilities that Artificial Intelligence (AI) brings are getting more pronounced as the technology gets more accurate and efficient. Indeed, its applicability in various fields is making a way and the results are promising. However, while AI stands as a potential saviour and as its role is being accentuated in urban planning, governance and management, there are increasing concerns that its practical implications are successful and its planning principles are disconnected with sensibilities linked to the dimensions of liveability.

Keywords

Cities Climate change Overpopulation Big Data Robots Internet of Things (IoT) 

References

  1. Abduljabbar, R., Dia, H., Liyanage, S., & Bogloee, S. A. (2019). Application of artificial intelligence in transport: An overview. Sustainability, 11(189), 1–24.Google Scholar
  2. Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mahamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4, e00938–e00979.CrossRefGoogle Scholar
  3. Abouelmehdi, K., Beni-Hessane, A., & Khaloufi, H. J. (2018). Big healthcare data: Preserving security and privacy. Journal of Big Data, 5(1), 1.Google Scholar
  4. Adelfio, M., Kain, J.-H., Thuvander, L., & Stenberg, J. (2018). Disentangling the compact city drivers and pressures: Barcelona as a case study. Norwegian Journal of Geography, 72(5), 287–304.Google Scholar
  5. Allam, M. Z. (2018). Redefining the smart city: Culture, metabolism and governance. Case study of Port Louis, Mauritius. Ph.D., Curtin University, Perth, Australia. Retrieved from https://espace.curtin.edu.au/handle/20.500.11937/70707.
  6. Allam, Z. (2017). Building a conceptual framework for smarting an existing city in Mauritius: The case of Port Louis. Journal of Biourbanism, 4(1&2), 103–121.Google Scholar
  7. Allam, Z. (2018). On smart contracts and organisational performance: A review of smart contracts through the blockchain technology. Review of Economic and Business Studies, 11(2), 137–156.CrossRefGoogle Scholar
  8. Allam, Z. (2019). Achieving neuroplasticity in artificial neural networks through smart cities. Smart Cities, 2(2), 118–134.CrossRefGoogle Scholar
  9. Allam, Z., & Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80–91.CrossRefGoogle Scholar
  10. Allam, Z., & Jones, D. S. (2019). The potential of blockchain within air rights development as a prevention measure against urban sprawl. Urban Science, 3(1), 38.CrossRefGoogle Scholar
  11. Allam, Z., & Newman, P. (2018). Redefining the smart city: Culture, metabolism & governance. Smart Cities, 1, 4–25.CrossRefGoogle Scholar
  12. Bačić, Ž., Jogun, T., & Majić, I. (2018). Integrated sensor systems for smart cities. Tehnički Vjesnik, 25(1), 277–284.Google Scholar
  13. Bavel, J. V. (2013). The world population: Causes, backgrounds and projections for the future. Facts, Views & Vision, 5(4), 281–291.Google Scholar
  14. Behzadfar, M., Ghalehnoee, M., Dadkhah, M., & Haghighi, N. M. (2017). International challenges of smart cities. Armanshahr Architecture & Urban Development, 10(20), 79–90.Google Scholar
  15. Berntzen, L., & Johannessen, M. R. (2016). The role of citizen participation in municipal smart city projects: Lessons learned from Norway. Smarter as the new urban agenda (pp. 299–314). Cham: Springer.Google Scholar
  16. Bhadani, A. J. (2016). Big data: Challenges, opportunities and realities. In M. K. Singh & D. G. Kumar (Eds.), Effective big data management and opportunities for implementation (pp. 1–24). Hershey, PA: IGI Global.Google Scholar
  17. Bini, S. A. (2018). Artificial intelligence, machine learning, deep learning, and cognitive computing: What do these terms mean and how will they impact health care? The Journal of Arthroplasty, 33(8), 2358–2361.CrossRefGoogle Scholar
  18. Bolivar, R. (2018). Smart technologies for building smart cities; A synthesis of the contributions; Smart technologies for smart governments. Berlin/Hiederberg, Germany: Springer International.CrossRefGoogle Scholar
  19. Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352, 1573–1576.CrossRefGoogle Scholar
  20. Bossman, J. (2016). Top 9 ethical issues in artificial intelligence. Artificial Intelligence and Robotics. Retrieved from https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/.
  21. Braun, T., Banjamin, C. M. F., Iqbal, F., & Shah, B. (2018). Security and privacy challenges in smart cities. Sustainable Cities and Society, 39, 499–507.CrossRefGoogle Scholar
  22. Bryson, J., & Winfield, A. F. (2017). Standardizing ethical design for artificial intelligence and autonomous systems. Computer, 50, 116–119.CrossRefGoogle Scholar
  23. Calo, R. (2017). Artificial intelligence policy: A roadmap. SSRN Electronic Journal, 1–28. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3015350.
  24. Cutter, S. L., Emrich, C. T., Gall, M., & Reeves, R. (2018). Flash flood risk and the paradox of urban development. Natural Hazards Review, 19(1), 05017005–05017012.CrossRefGoogle Scholar
  25. Davies, A. (2017, October 8). How Tesla’s self-driving truck scheme can dump human drivers. Transportation. Retrieved from https://www.wired.com/story/tesla-self-driving-truck-musk/.
  26. Deakin, M., & Reid, A. (2017). The embedded intelligence of smart cities: Urban life, citizenship, and community. International Journal of Public Administration in the Digital Age, 4(4), 62–74.CrossRefGoogle Scholar
  27. DeBrule, S. (2019, March 36). Facebook’s AI and far right terrorism in New Zealand. Retrieved from https://machinelearnings.co/facebooks-ai-and-far-right-terrorism-in-new-zealand-342bd5fbf499.
  28. Dignum, V. (2018). Ethics in artificial intelligence: Introduction to the special issue. Ethics and Information Technology, 20(2), 1–3.CrossRefGoogle Scholar
  29. Doward, J., & Gibbs, A. (2017, March 4). Did Cambridge analytica influence the Brexit vote and the US election? Retrieved from https://www.theguardian.com/politics/2017/mar/04/nigel-oakes-cambridge-analytica-what-role-brexit-trump.
  30. Elmaghraby, A. S., & Losavio, M. M. (2014). Cyber security challenges in smart cities: Safety, security and privacy. Journal of Advanced Research, 5(4), 491–497.CrossRefGoogle Scholar
  31. Estevez, E., Lopes, N. V., & Janowski, T. (2016). Smart sustainable cities: Reconnaissance study. Retrieved from Canada. http://collections.unu.edu/view/UNU:5825.
  32. European Commission. (2018). 2018-NW pacific typhoons: Past events, current situation and seasonal forecast. Retrieved from https://www.gdacs.org/Public/download.aspx?type=DC&id=161.
  33. Ford, M. (2015). Rise of the robots: Technology and the threat of a jobless future. New York: Basic Books.Google Scholar
  34. Ganor, B. (2018). Artificial or human: A new era of counterterrorism intelligence? Studies in Conflict & Terrorism, 1–20. https://www.tandfonline.com/doi/abs/10.1080/1057610X.2019.1568815.
  35. Goode, L. (2018). Life, but not as we know it: A. I. and popular imagination. Culture Unbound, 10(2), 185–207.CrossRefGoogle Scholar
  36. Gove, B., Williams, L. J., Beresford, A. E., Roddis, P., Campbell, C., & Teuten, E. (2016). Reconciling biodiversity conservation and widespread development of renewable energy technologies in the UK. PLoS One, 11(5), e0150956.CrossRefGoogle Scholar
  37. Guiling, P., Brorsen, W., & Doye, D. (2009). Effect of urban proximity on agricultural land values. Land Economics, 85, 252–264.CrossRefGoogle Scholar
  38. Gurditta, H., & Singh, G. (2016). Climate change, food and nutritional security: Issues and concerns in India. Journal of Climate Change, 2(1), 79–89.CrossRefGoogle Scholar
  39. Habibi, S., & Asadi, N. (2011). Causes, results and methods of controlling urban sprawl. Procedia Engineering, 21(133–141).Google Scholar
  40. Hagendorff, T. (2019). The ethics of AI ethics—An evaluation of guidelines.Google Scholar
  41. Hancock, P. A., Nourbakhsh, I., & Stewart, J. (2019). On the future of transportation in an era of automated and autonomous vehicles. PNAS, 116(16), 7684–7691.CrossRefGoogle Scholar
  42. Haub, C. (2010). World population data sheet. Retrieved from Washington, DC. https://www.prb.org/wp-content/uploads/2015/11/2014-world-population-data-sheet_eng.pdf.
  43. Hsiao, L.-F., Yang, M.-J., Lee, C.-S., Kuo, H.-C., Shih, D.-S., Tsai, C.-C., … Lin, G.-F. (2013). Ensemble forecasting of typhoon rainfall and floods over a mountainous watershed in Taiwan. Journal of Hydrology, 506, 55–68.CrossRefGoogle Scholar
  44. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.CrossRefGoogle Scholar
  45. Huang, T.-J. (2017). Imitating the brain with neurocomputer: A “new” way towards artificial general intelligence. International Journal of Automation and Computing, 14(5), 520–531.CrossRefGoogle Scholar
  46. IPCC. (2018). Summary for policymakers. In V. Masson-Delmotte, P. Zhai, H. O. Pörtner, D. Roberts, J. Skea, P. R. Shukla, … T. Waterfield (Eds.), Global warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. Geneva, Switzerland: World Meteorological Organization.Google Scholar
  47. IRENA. (2017). Rethinking energy 2017. Abu Dhabi: International Renewable Energy Agency.Google Scholar
  48. Jacobs, J. (1961). The death and life of great American cities. New York: Random House.Google Scholar
  49. Kaneda, T., Greenbaum, C., & Patierno, K. (2018). World population data sheet: With a special focus on changing age structures. Retrieved from Washington, DC. https://www.prb.org/wp-content/uploads/2018/08/2018_WPDS.pdf.
  50. Keenan, M. (2018). The future of data with the rise of the IoT. RFID Journal, 1–2. https://www.rfidjournal.com/articles/view?17954.
  51. Keskinbora, K. H. (2019). Medical ethics considerations on artificial intelligence. Journal of Clinical Neuroscience, 64, 277–282.CrossRefGoogle Scholar
  52. Klotzbach, P. J., Bowen, S. G., Pielke, R., Jr., & Bell, M. (2017). Continental U. S. hurricane landfall frequency and associated damage. Meteorological Society, 99(7), 1359–1376.Google Scholar
  53. Kumar, A., Singh, M. P., Ghosh, S., & Anand, A. (2012). Weather forecasting model using artificial neural network. Procedia Technology, 4, 311–318.CrossRefGoogle Scholar
  54. Landscope Mauritius. (2018). Cote d’Or City. Retrieved from http://landscopemauritius.com/cotedorcity/.
  55. Lee, M., Yun, J. J., Pyka, A., Won, D., Kodama, F., Schiuma, G., … Zhao, X. (2018). How to respond to the Fourth Industrial Revolution, or the Second Information Technology Revolution? Dynamic new combinations between technology, market, and society through open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 4(21), 1–24. Google Scholar
  56. Levitt, D., & Kommenda, N. (2018, October 10). Is climate change making hurricanes worse? Retrieved from https://www.theguardian.com/weather/ng-interactive/2018/sep/11/atlantic-hurricanes-are-storms-getting-worse.
  57. Liangyuan, N., Yang, C., Chi-Cheng, L., Zhao, F., Fukuoka, Y., & Aswani, A. (2018). Feasibility of reidentifying individuals in large national physical activity data sets from which protected health information has been removed with use of machine learning. JAMA Network Open, 1(8), e186040.CrossRefGoogle Scholar
  58. Lichtenthaler, U. (2018). Beyond artificial intelligence: Why companies need to go the extra step. Journal of Business Strategy, 1–9. https://www.emerald.com/insight/content/doi/10.1108/JBS-05-2018-0086/full/html.
  59. Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adabi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for Internet of Things data analysis: A survey. Digital Communications and Networks, 4(3), 161–175.CrossRefGoogle Scholar
  60. McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D., Lagerquist, R., & Williams, J. K. (2017). Using artificial intelligence to improve real-time decision-making for high-impact weather. Bulletin of the American Meteorological Society, 98(10), 2073–2090.Google Scholar
  61. Miller, J. D., & Hutchins, M. (2017). The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom. Journal of Hydrology: Regional Studies, 12, 345–362.Google Scholar
  62. Monfaredzadeh, T., & Krueger, R. (2015). Investigating social factors of sustainability in a smart city. Procedia Engineering, 118(2015), 1112–1118.CrossRefGoogle Scholar
  63. Montjoye, Y.-A. d., Farzanehfar, A., Hendrickx, J., & Rocher, L. (2017). Solving artificial intelligence’s privacy problem [Special issue]. The Journal of Field Actions, 17, 80–86.Google Scholar
  64. Mouratidis, K. (2018). Is compact city livable? The impact of compact versus sprawled neighbourhoods on neighbourhood satisfaction. Urban Studies, 55(11), 2408–2430.CrossRefGoogle Scholar
  65. Naganathan, V., & Rao, R. K. (2018). The evolution of Internet of Things: Bringing the power of Artificial intelligence to IoT, its opportunities and challenges. International Journal of Computer Science Trends and Technology, 6(3), 94–108.Google Scholar
  66. Niestadt, M., Debyser, A., Scordamaglia, D., & Pape, M. (2019). Artificial intelligence in transport: Current and future developments, opportunities and challenges.Google Scholar
  67. Niles, M. T., & Salerno, J. D. (2018). A cross-country analysis of climate shocks and smallholder food insecurity. PLoS One, 13(2), e0192928.CrossRefGoogle Scholar
  68. O’Connor, A. (2018, 20th November 2019). Florida picks up the pieces from powerful Hurricane Michael. Retrieved from https://www.insurancejournal.com/news/southeast/2018/11/20/509157.htm.
  69. Oldenborgh, G. J. v., Wie, K. v. d., Sebastian, A., Singh, R., Arrighi, J., Otto, F., … Cullen, H. (2017). Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environmental Research Letters, 13(12), 1–14.Google Scholar
  70. Özdemir, E., & Tasan-Kok, T. (2017). Planners’ role in accommodating citizen disagreement: The case of Dutch urban planning. Urban Studies, 56, 741–759.Google Scholar
  71. Payne, K. (2018). Artificial intelligence: A revolution in strategic affairs? Survival, 60(5), 7–32.CrossRefGoogle Scholar
  72. Polidoro, M., de Lollo, J. A., & Barros, M. V. F. (2012). Urban sprawl and the challenges for urban planning. Journal of Environmental Protection, 3(9), 1010–1019.CrossRefGoogle Scholar
  73. Prakash, P. (2019, January 11). Healthcare predictions for 2019. Retrieved from https://www.expresshealthcare.in/news/healthcare-predictions-for-2019/407990/.
  74. Pumo, D., Arnone, E., Francipane, A., Caracciolo, D., & Noto, L. V. (2017). Potential implications of climate change and urbanisation on watershed. Journal of Hydrology, 554, 80–99.CrossRefGoogle Scholar
  75. Russell, S. J., & Norvig, P. (2010). Artificial intelligence—A modern approach. London: Pearson Education.Google Scholar
  76. Scarcello, F. (2018). Artificial intelligence. Reference Module in Life Sciences, 1–7. https://www.researchgate.net/publication/323220519_Artificial_Intelligence.
  77. Scoones, I. (2016). The politics of sustainability and development. Annual Review of Environment and Resources, 41(1), 293–319.CrossRefGoogle Scholar
  78. Skougaard Kaspersen, P., Ravn, H., Arnbjerg-Nielsen, K., Madsen, H., & Drews, M. (2015). Influence of urban land cover changes and climate change for the exposure of European cities to flooding during high-intensity precipitation. Proceedings of the International Association of Hydrological Sciences (PIAHS), 370, 21–27.CrossRefGoogle Scholar
  79. Slavova, M., & Okwechime, E. (2016). African smart cities strategies for agenda 2063. Africa Journal of Management, 2(2), 210–229.CrossRefGoogle Scholar
  80. Smith, M. L., & Neupane, S. (2018). Artificial intelligence and human development: Toward a research agenda. Retrieved from Canada. https://idl-bnc-idrc.dspacedirect.org/handle/10625/56949.
  81. Sobash, R. A., Schwartz, C. S., Romine, G. S., Fossel, K., & Weisman, M. L. (2016). Severe weather prediction using storm surrogates from an ensemble forecasting system. Weather Forecasting, 31(1), 255–271.CrossRefGoogle Scholar
  82. Sorda, K. R. (2018, June 4). Artificial neural networks—The future of smart cities. Retrieved from http://www.itwebafrica.com/home-pagex/opinion/244318-artificial-neural-networks–the-future-of-smart-cities.
  83. Tai, A. P. K., Martin, M. V., & Heald, C. L. (2014). Threat to future global food security from climate change and ozone air pollution. Nature Climate Change, 4(9), 817–821.CrossRefGoogle Scholar
  84. Tajrin, S., & Hossain, B. (2018). Rural–urban migration and its causes and consequences on migrant street hawker in Khulna city. The International Journal of Humanities & Social Studies, 7(5), 223–236.Google Scholar
  85. Torresen, J. (2018). A review of future and ethical perspectives of robotics and AI. Frontiers in Robotics and AI, 4(75), 1–10.Google Scholar
  86. Trommetter, M. (2017). Climate and biodiversity: Reconciling renewable energy and biodiversity. Paris: Entreprises, Territoires et Environnement.Google Scholar
  87. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.CrossRefGoogle Scholar
  88. Tzafestas, S. G. (2018). Synergy of IoT and AI in modern society: The robotics and automation case. Robotics & Automation Engineering Journal, 31(5), 1–15.Google Scholar
  89. UN-Habitat. (2014). State of African cities 2014, re-imagining sustainable urban transitions. Retrieved from Nairobi. http://mirror.unhabitat.org/pmss/listItemDetails.aspx?publicationID=3528.
  90. UN-Habitat. (2015). Urbanization and climate change in small island developing states. Retrieved from Nairobi, Kenya https://unhabitat.org/wpdm-package/urbanization-and-climate-change-in-small-island-developing-states/?wpdmdl=114762.
  91. UNFCCC. (2018). COP 24 agenda as adopted. Retrieved from https://unfccc.int/node/185180.
  92. United Nations. (2016). The new urban agenda. Paper presented at the United Nations Conference on Housing and Sustainable Urban Development (Habitat III), Quito, Ecuador.Google Scholar
  93. United Nations. (2017a). World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. Retrieved from https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html.
  94. United Nations. (2017b). World population prospects: Key findings & advance tables. New York, NY: Department of Economic and Social Affairs, Population Division.Google Scholar
  95. United Nations. (2018). The world’s cities in 2018—Data booklet. Retrieved from New York, NY. https://www.un.org/en/events/citiesday/assets/pdf/the_worlds_cities_in_2018_data_booklet.pdf.
  96. Van Winden, W., & van den Buuse, D. (2017). Smart city pilot projects: Exploring the dimensions and conditions of scaling up. Journal of Urban Technology, 24(4), 51–72.CrossRefGoogle Scholar
  97. van Zoonen, L. (2016). Privacy concerns in smart cities. Government Information Quarterly, 33(3), 472–480.CrossRefGoogle Scholar
  98. Wang, Y., & Xiang, P. (2019). Urban sprawl sustainability of mountainous cities in the context of climate change adaptability using a coupled coordination model: A case study of Chongqing, China. Sustainability, 11(20), 1–20.Google Scholar
  99. Yairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N. (2017). A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Transactions on Aerospace and Electronic Systems, 53(3), 1384–1401.CrossRefGoogle Scholar
  100. Yigitcanlar, T., Kamruzzaman, M., Buys, L., Ioppolo, G., Sabatini-Marques, J., da Costa, E. M., & Yun, J. J. (2018). Understanding “smart cities”: Intertwining development drivers with desired outcomes in a multidimensional framework. Cities, 81, 145–160.Google Scholar
  101. Yudkowsky, E. (2008). Artificial intelligence as a positive and negative factor in global risk. In N. Bostrom & M. M. Ćirković (Eds.), Global catastrophic risks (pp. 308–345). New York: Oxford University Press.Google Scholar
  102. Zorins, A., & Grabusts, P. (2015). Artificial neural networks and human brain: Survey of improvement possibilities of learning. Paper presented at the 10th International Scientific and Practical Conference, Rezekne, Latvia.Google Scholar
  103. Zou, J., & Schiebinger, L. (2018, July 18). AI can be sexist and racist—It’s time to make it fair. Retrieved from https://www.nature.com/articles/d41586-018-05707-8.

Copyright information

© The Author(s) 2020

Authors and Affiliations

  • Zaheer Allam
    • 1
  1. 1.The Port Louis Development Initiative (PLDI)Port LouisMauritius

Personalised recommendations