智能城市大数据分析

Cassie Davies
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引用次数: 0

摘要

目的:本研究旨在探讨智慧城市的大数据分析。研究方法:本研究采用桌面研究方法。案头研究指的是二手数据或无需实地考察即可收集的数据。案头研究基本上是从现有资源中收集数据,因此与实地研究相比,案头研究通常被认为是一种低成本技术,因为主要成本涉及执行人员的时间、电话费和目录。因此,本研究依赖于已出版的研究、报告和统计数据。这些二手数据可通过在线期刊和图书馆轻松获取。研究结果:研究结果表明,在探索智慧城市大数据分析方面存在背景和方法上的差距。将大数据分析融入智慧城市运营,可显著提高城市管理效率、可持续性和居民生活质量。通过利用先进的分析技术,城市优化了交通流量,降低了能源消耗,加强了公共安全,改善了医疗服务,并对环境状况进行了实时监控。这些进步带来了更顺畅的服务、经济可持续性、更好的公共安全、有效的灾害管理以及积极的环境和健康干预,使城市更具响应性、复原力和可持续性。对理论、实践和政策的独特贡献:创新扩散理论、社会技术系统理论和行动者网络理论可用于未来智慧城市的大数据分析研究。本研究以经验证据扩展了创新扩散理论和社会-技术系统理论,建议建立强大的数据治理框架和熟练的分析部门以切实可行,并倡导制定全面的政策以确保数据隐私和安全,从而为理论、实践和政策做出了重大贡献。报告强调了利益相关者合作、技术基础设施投资以及未来对长期影响、伦理因素和新兴技术研究的重要性,以提高智慧城市的效率和可持续性。
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Big Data Analytics for Smart Cities
Purpose: This study sought to explore big data analytics for smart cities. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to exploring big data analytics for smart cities. The integration of big data analytics into smart city operations significantly improved urban management efficiency, sustainability, and residents' quality of life. By leveraging advanced analytics, cities optimized traffic flow, reduced energy consumption, enhanced public safety, improved healthcare delivery, and monitored environmental conditions in real-time. These advancements led to smoother services, economic sustainability, better public safety, effective disaster management, and proactive environmental and health interventions, making cities more responsive, resilient, and sustainable. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Socio-Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on big data analytics for smart cities. The study made significant contributions to theory, practice, and policy by extending the Diffusion of Innovations and Socio-Technical Systems theories with empirical evidence, recommending robust data governance frameworks and skilled analytics units for practical implementation, and advocating for comprehensive policies to ensure data privacy and security. It highlighted the importance of stakeholder collaboration, investment in technological infrastructure, and future research on long-term impacts, ethical considerations, and emerging technologies to enhance the efficiency and sustainability of smart cities.
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