BIO Web Conf.
Volume 86, 2024International Conference on Recent Trends in Biomedical Sciences (RTBS-2023)
|Number of page(s)
|12 January 2024
Leveraging Big Data Analytics for Urban Planning: A Study Using the Big Data Analytics Efficiency Test
1 Department of Management and Innovation, National Research University Moscow State University of Civil Engineering, 129337 Yaroslavskoe shosse, 26, Moscow, Russia
2 Uttaranchal University, Dehradun 248007, India
3 Lovely Professional University Phagwara, Punjab, India,
4 K R Mangalam University, Gurgaon, India,
5 GD Goenka University, Sohna, Haryana, India
6 GRIET, Bachupally, Hyderabad, Telangana, India
* Corresponding author: VasilievaEYU@gic.mgsu.ru
Data from a variety of sample cities was evaluated as part of a research looking into the integration of big data analytics into urban planning. The goals were to evaluate the impact of data analytics infrastructure, data volume and processing time, urban development initiatives, and data analytics efficiency. The results showed significant differences in data analytics resources across cities, indicating different levels of investment and preparedness for data-driven decision making. It was clear that cities could handle large amounts of data efficiently thanks to their strong data processing skills. Data analytics have an impact on urban development initiatives, highlighting the revolutionary potential of data-driven urban planning. The outcomes of efficiency tests demonstrated how data analytics procedures are useful for improving urban services and for making well-informed judgments. This study offers important new insights into the mechanics of data-driven urban planning and how it can influence how cities develop in the future.
Key words: Big data analytics / Urban planning / Data analytics infrastructure / Data volume / Processing time / Urban development projects / Efficiency test / Data-driven decision-making / Sustainable urban development
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.