用于预测智能城市局势发展的数据分析:评估用户感知
Alexander A Kharlamov1,2,3,4, Maria Pilgun5,6
1Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 117486 Moscow, Russia.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
这项研究开发了分析社交媒体数据的算法,评估城市项目的公民意见和社会紧张局势. 调查结果显示,对于运输项目,公众的看法是中立的,没有社会紧张.
科学领域:
- 城市规划 城市规划
- 社会科学 社会科学 社会科学
- 数据科学数据科学数据科学
背景情况:
- 对大型,多样化的数据集进行分析对于大城市发展和智慧城市倡议至关重要.
- 实时评估公民情绪和社会紧张关系对于城市项目成功至关重要.
- 现有的方法可能无法充分捕捉动态城市环境中的论细微差别.
研究的目的:
- 开发和测试用于分析社交媒体数据的算法,以衡量公民的意见.
- 验证和检查城市项目实施期间用于社会紧张分析和情况预测的数据.
- 为城市发展中避免冲突和缓解社会紧张局势提供可行的建议.
主要方法:
- 采用跨学科的方法,整合社交媒体,博客,论坛和评论网站.
- 使用了数据分析工具,包括品牌分析,TextAnalyst 2.32,GPT-3.5,GPT-4,GPT-4o和Tableau.
- 从运输系统开发项目的实时社交媒体数据中应用算法.
主要成果:
- 数据分析显示,公众对城市交通项目的看法中立.
- 多种分析工具的相同结果证实没有社会紧张.
- 该研究成功地预测了项目情况的平静发展轨迹.
结论:
- 开发的算法有效地实时评估公民情绪和社会紧张局势.
- 积极识别和缓解社会紧张局势是成功实施城市项目的关键.
- 数据驱动的洞察力支持知情决策,以提高城市生活质量.
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