Smart City Gnosys
Smart city article details
| Title | Data Urbanity: Smart City Evolution Through Iot And Data Science |
|---|---|
| ID_Doc | 17372 |
| Authors | Singh A.; Kumar M. |
| Year | 2023 |
| Published | 3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023 - Proceedings |
| DOI | http://dx.doi.org/10.1109/ICIMIA60377.2023.10426499 |
| Abstract | The rapid increase in population density in urban cities has created a demand for suitable infrastructure and services to meet the needs of urban residents. To address this demand, urban planners and developers are turning to data science for assistance in creating smart city solutions. The Internet of Things (IoT) has played a significant role in the application of data science in urban planning, as IoT devices such as sensors and smart devices provide vast amounts of data that can be used to gain insights into the operations of the city and make data-driven decisions to enhance its productivity, efficiency, and overall quality of life. This paper provides a general overview of data science in urban planning for smart city development. It highlights the importance of data science in urban planning and the role it plays in creating smarter, more effective cities. By applying data analytics, machine learning, and other data science approaches, urban planners can gain insights into various aspects of urban life, including traffic patterns, energy usage, and public safety. These insights can then be used to develop strategies that improve traffic flow, reduce energy waste, and enhance overall quality of life in cities. The study aims to describe the function of data science in urban planning, examine case studies and best practices, discuss the challenges and limitations associated with applying data science in urban planning, and provide suggestions for further research and advancements in the field. By investigating how data science can be effectively used in urban planning for smart city development, this study contributes to the growing body of knowledge on creating more sustainable and livable cities. © 2023 IEEE. |
| Author Keywords | Artificial intelligence; Biomarkers; cardiovascular disease; Computed Tomography (CT); Data analysis; Deep learning; Diagnostic methods; Early detection; Electrocardiogram (ECG/EKG); Health informatics; heart disease; Machine learning; Magnetic Resonance Imaging (MRI); Medical imaging; Predictive modeling; Remote monitoring; Risk assessment; Signal processing; Ultrasound; Wearable devices |
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