Big Data Analytics for Smart Cities
DOI:
https://doi.org/10.47941/ijce.2057Abstract
Purpose: This study sought to explore big data analytics for smart cities.
Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
Findings: The findings reveal that there exists a contextual and methodological gap relating to exploring big data analytics for smart cities. The integration of big data analytics into smart city operations significantly improved urban management efficiency, sustainability, and residents' quality of life. By leveraging advanced analytics, cities optimized traffic flow, reduced energy consumption, enhanced public safety, improved healthcare delivery, and monitored environmental conditions in real-time. These advancements led to smoother services, economic sustainability, better public safety, effective disaster management, and proactive environmental and health interventions, making cities more responsive, resilient, and sustainable.
Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Socio-Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on big data analytics for smart cities. The study made significant contributions to theory, practice, and policy by extending the Diffusion of Innovations and Socio-Technical Systems theories with empirical evidence, recommending robust data governance frameworks and skilled analytics units for practical implementation, and advocating for comprehensive policies to ensure data privacy and security. It highlighted the importance of stakeholder collaboration, investment in technological infrastructure, and future research on long-term impacts, ethical considerations, and emerging technologies to enhance the efficiency and sustainability of smart cities.
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References
Almeida, A. B., Neto, A. S., & Santos, E. M. (2017). The impact of smart public transport systems on urban mobility. Journal of Urban Technology, 24(1), 45-62. https://doi.org/10.1080/10630732.2017.1334861
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., ... & Portugali, Y. (2012). Smart cities of the future. European Physical Journal Special Topics, 214(1), 481-518. https://doi.org/10.1140/epjst/e2012-01703-3
Brantingham, P. J., Valasik, M., & Mohler, G. O. (2018). Does predictive policing lead to biased arrests? Results from a randomized controlled trial. Journal of Experimental Criminology, 14(3), 353-375. https://doi.org/10.1007/s11292-018-9321-4
Chen, C., Pan, S., Zhang, J., & Liao, S. (2017). Traffic speed prediction for intelligent transportation systems using deep learning. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1793-1802. https://doi.org/10.1109/TITS.2016.2619363
Chen, M., Zhang, Y., Li, Y., Mao, S., & Leung, V. C. M. (2018). AI-based healthcare systems: A survey. IEEE Access, 6, 64225-64237. https://doi.org/10.1109/ACCESS.2018.2878768
Cheng, L., Chen, X., de Vos, J., & Witlox, F. (2018). Applying a random forest method approach to model travel mode choice behavior. Travel Behaviour and Society, 14(1), 1-10. https://doi.org/10.1016/j.tbs.2018.05.002
Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J. R., Mellouli, S., Nahon, K., ... & Scholl, H. J. (2012). Understanding smart cities: An integrative framework. 2012 45th Hawaii International Conference on System Sciences, 4(1), 2289-2297. https://doi.org/10.1109/HICSS.2012.615
Daniel, B. K. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. https://doi.org/10.1111/bjet.12230
Dlamini, S., Mokwatlo, N., & Moyo, T. (2018). Implementing smart grid technology in African cities: A case study of Cape Town, South Africa. Journal of Renewable Energy and Smart Systems, 35(2), 75-88. https://doi.org/10.1007/s40899-018-0038-5
Fang, X., Misra, S., Xue, G., & Yang, D. (2012). Smart grid—The new and improved power grid: A survey. IEEE Communications Surveys & Tutorials, 14(4), 944-980. https://doi.org/10.1109/SURV.2011.101911.00087
Ferreira, J., Fernandes, C., & Cunha, J. (2016). Smart city planning using big data analytics: A case study of São Paulo, Brazil. International Journal of Urban Planning, 40(3), 215-230. https://doi.org/10.1177/0739456X16639160
Gill, M., Spriggs, A., & Allen, J. (2017). Evaluating CCTV in public spaces: A comprehensive analysis of the impact on crime and public safety. Security Journal, 30(2), 55-72. https://doi.org/10.1057/sj.2014.5
Goodman, A., Cheshire, J., & Mullen, C. (2019). The role of smart technologies in improving public transport efficiency: Insights from London. Transportation Research Part A: Policy and Practice, 124(3), 51-63. https://doi.org/10.1016/j.tra.2019.03.003
Jones, T., Karan, E., & Katsikeas, C. (2018). The impact of smart energy management on urban sustainability: A study of the London Underground. Journal of Cleaner Production, 180(1), 123-134. https://doi.org/10.1016/j.jclepro.2018.01.104
Kitchin, R. (2014). The real-time city? Big data and smart urbanism. GeoJournal, 79(1), 1-14. https://doi.org/10.1007/s10708-013-9516-8
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University Press.
Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute, 56(1), 1-137. https://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation
Morris, M., Jones, T., & Hogg, D. (2019). Smart street lighting and its impact on urban safety and energy efficiency: A case study of Glasgow. Energy Policy, 127(4), 382-390. https://doi.org/10.1016/j.enpol.2018.12.007
Munyaneza, F., Hirwa, C., & Niyonsaba, A. (2019). The impact of smart city initiatives on urban development in Kigali, Rwanda. Journal of African Development, 45(1), 89-102. https://doi.org/10.1080/19376812.2019.1556309
Musa, M. (2019). The role of smart waste management systems in enhancing urban sustainability: Evidence from New York City. Sustainable Cities and Society, 46(2), 101-112. https://doi.org/10.1016/j.scs.2018.12.007
Mwangi, J. N., Njeru, S., & Muchiri, M. (2020). Smart water management systems in Nairobi: Enhancing resource efficiency and service delivery. International Journal of Water Resources Development, 36(4), 702-716. https://doi.org/10.1080/07900627.2019.1590414
Nakamura, H., Hattori, T., & Nagai, Y. (2016). The role of smart grids in enhancing energy efficiency: A case study from Tokyo. Energy Efficiency, 9(2), 321-335. https://doi.org/10.1007/s12053-015-9360-9
Ni, M., Ma, L., Leung, H., & Fu, L. (2017). Smart environment monitoring system by employing wireless sensor networks on vehicles for pollution and noise control. Journal of Cleaner Production, 181(1), 857-868. https://doi.org/10.1016/j.jclepro.2018.01.106
Piza, E. L., Caplan, J. M., & Kennedy, L. W. (2019). Analyzing the impact of ShotSpotter on gun violence reduction in Chicago. Journal of Crime and Justice, 42(1), 71-87. https://doi.org/10.1080/0735648X.2018.1514264
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3-10. https://doi.org/10.1186/2047-2501-2-3
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Shimizu, H., Imanaka, M., & Tsujimoto, M. (2018). Smart healthcare systems for aging societies: Insights from Osaka. Journal of Gerontology and Geriatric Research, 7(2), 105-116. https://doi.org/10.4172/2167-7182.1000451
Shoup, D. (2018). The high cost of free parking: An analysis of smart parking initiatives in San Francisco. Journal of Urban Planning and Development, 144(3), 04018019. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000467
Siemens, G., & Long, P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 30-32. https://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education
Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting: An examination of the psychological situation and defences of a work group in relation to the social structure and technological content of the work system. Human Relations, 4(3), 3-38.
United Nations. (2019). World Urbanization Prospects: The 2018 Revision. Retrieved from https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf
Yamaguchi, K., Suzuki, T., & Yamashita, M. (2017). Smart city development in Tokyo: Integrating big data for urban resilience. Journal of Urban Technology, 24(3), 63-80. https://doi.org/10.1080/10630732.2017.1334863
Zhou, K., Fu, C., & Yang, S. (2016). Big data driven smart energy management: From big data to big insights. Renewable and Sustainable Energy Reviews, 56(4), 215-225. https://doi.org/10.1016/j.rser.2015.11.050
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