Exploration of the Application of Big Data Technology in Modern Smart City Planning
Authors
Jiuxiang Zhao1, *, Yan Meng2
1Qiqihar Institute of Engineering, Heilongjiang Province, Qiqihar City, 161003, China
2Daqing Oilfield Ecological Environment Management and Protection Company Ecological Technology Development Company, Heilongjiang Province, Daqing, 161000, China
*Corresponding author.
Email: 18946239592@163.com
Corresponding Author
Jiuxiang Zhao
Available Online 27 May 2025.
- DOI
- 10.2991/978-94-6463-734-2_36How to use a DOI?
- Keywords
- smart city; Big data technology; Urban spatial planning; Environmental monitoring; Intelligent Traffic Management
- Abstract
With the rapid development of technology, big data technology has become an indispensable part of modern smart city planning. This article delves into the various applications of big data technology in smart city planning, including urban spatial planning, environmental monitoring, intelligent transportation management, noise data monitoring, and urban safety management. Through big data technology, urban planners can manage urban resources more scientifically and efficiently, improve the quality of life of urban residents, and promote the comprehensive development of smart cities.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiuxiang Zhao AU - Yan Meng PY - 2025 DA - 2025/05/27 TI - Exploration of the Application of Big Data Technology in Modern Smart City Planning BT - Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025) PB - Atlantis Press SP - 299 EP - 305 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-734-2_36 DO - 10.2991/978-94-6463-734-2_36 ID - Zhao2025 ER -