Knowledge Update

Urban Computing and Its Applications in Our Society

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The rapid development in population density in metropolitan cities has necessitates a reasonable provision of services and infrastructures to meet the needs of the residents. There is no doubt that the rapid urbanization has not only modernized many people lives and also improved their standard of living but also brought about major issues such as pollution, increased energy consumption, traffic congestion, natural disasters among others. Torres-Ruiz & Lytras [1] defined urban computing as the process of acquiring, integrating, and analyzing big and heterogeneous data generated by a variety of sources in urban spaces, such as sensors, devices, vehicles, buildings, and humans.

Urban computing brings together inconspicuous and ubiquitous sensing technologies, powerful data management and analytics models, and unique visualization approaches to provide win-win-win solutions that improve the urban environment, human life quality, and city operations systems. Urban computing also aids in the understanding of urban phenomena and even the forecasting of futures cities. Urban computing is an interdisciplinary field that combines computing science with traditional fields such as transportation, civil engineering, business, ecology, and sociology.

The application of urban computing in the management of transportation is one of the most significant application in urban computing. The amount of information generated today in the transport sector is massive which is where BigData, an aspect of urban computing comes into play. The data collected is analyzed and used to improve the different aspects in the transport sector which is the goal of having a smooth smart transport services with high efficiency, increased level of travel experience and cost effectiveness [2] [3].

Moreover, air pollution is one of the problems brought about by urbanization of our cities and there have been different proposals by governments on addressing the issue like building air quality stations but the problem still lingers because of factors like land uses, traffic volume and meteorology. The application of urban computing in addressing it is now getting attention with several studies being conducted in this area. An example of such studies is carried out by Zheng  et. al [4], where the authors introduced a cloud-based knowledge discovery system that infers real-time and fine-grained air quality information across a city based on historical and real-time air quality data given by existing monitor stations and a number of data sources observed around the city such as point of interest (POI), human mobility, traffic flow, meteorology, road network structures.

Other areas where urban computing have been applied is the housing and real estate sector as discussed in several studies [5], [6], health sector also as discussed in [7], [8], environment sector especially in disaster management as discussed in several studies [9], [10], [11] among others.

Recent advancements in wireless communications, smart gadgets, and social computing applications have enable new urban sensing and management opportunities. In summary, urban computing is an interdisciplinary discipline that investigates how real-time technology may help us better understand our cities while also imagining ways in which these technologies can improve them.


  • Torres-Ruiz, Miguel & Lytras, Miltiadis. (2016). Urban Computing and Smart Cities Applications for the Knowledge Society. International Journal of Knowledge Society Research. 7. 113-119. 10.4018/IJKSR.2016010108.
  • Nagy, Albert & József, Tick. (2016). Improving transport management with big data analytics. 199-204. 10.1109/SISY.2016.7601497.
  • Munizaga, Marcela. (2019). Big data and transport. 10.4337/9781788970204.00032.
  • Zheng, Y., Liu, F., & Hsieh, H.-P. (2013). U-Air: When urban air quality inference meets big data. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1436–1444). ACM. doi:10.1145/2487575.2488188
  • Mohammed, Jibrin & Bello, M. & Saidu, Umar & Mohammed, Maikudi. (2018). A MODEL FOR INTEGRATED SMART REAL ESTATE.
  • Barkham, Richard & Bokhari, Sheharyar & Saiz, Albert. (2022). Urban Big Data: City Management and Real Estate Markets. 10.1007/978-3-030-84459-2_10.
  • Cook, Diane & Duncan, Glen & Sprint, Gina & Fritz, Roschelle. (2018). Using Smart City Technology to Make Healthcare Smarter. Proceedings of the IEEE. PP. 1-15. 10.1109/JPROC.2017.2787688.
  • Cabo, Javier & Uceda, Jose & Muiños, Verónica & Lopez, Javier & De Castro Lozano, Carlos. (2018). Ubiquitous Computing and Its Applications in the Disease Management in a Ubiquitous City. Journal of Computer and Communications. 06. 19-42. 10.4236/jcc.2018.63002.
  • Chaudhuri, Neha & Bose, Indranil. (2019). Application of Image Analytics for Disaster Response in Smart Cities. 10.24251/HICSS.2019.367.
  • Hartama, Dedy & Zarlis, M & Sembiring, Rahmat Widia. (2017). Smart City: Utilization of IT resources to encounter natural disaster. Journal of Physics: Conference Series. 890. 012076. 10.1088/1742-6596/890/1/012076.
  • You, Jianyi & Muhammad, Auwal & He, Xin & Xie, Tianqi & Wang, Zhiyuan & Fan, Xiaoliang & Yu, Zhiyong & Chen, Longbiao & Wang, Cheng. (2022). PANDA: predicting road risks after natural disasters leveraging heterogeneous urban data. CCF Transactions on Pervasive Computing and Interaction. 10.1007/s42486-022-00095-5.


Mr Muhammad Auwal Sagir is a lecturer at the Department of Computer Science, School of Sciences and Information Technology at Skyline University Nigeria. He obtained his MSc. Cloud Computing from University of Essex (UK).

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