数据驱动的智能可持续城市中互联传感器网络和基于机器学习的分析

Q1 Arts and Humanities

Geopolitics, History, and International Relations Pub Date : 2021-01-01 DOI:10.22381/GHIR13120213

Jason Townsend

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引用次数: 12

摘要

利用最近的研究成果，包括在数据驱动的智能可持续城市中互联传感器网络和基于机器学习的分析，并通过从BDO、Capgemini、ICMA、KPMG、麦肯锡、飞利浦、SCC、SmartCitiesWorld和世界经济论坛收集的数据来构建我的论点，我对智能城市的可持续发展进展进行了分析和估计。在适当情况下，对已完成调查的汇编数据进行了描述性统计。

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Interconnected Sensor Networks and Machine Learning-based Analytics in Data-driven Smart Sustainable Cities

Employing recent research results covering interconnected sensor networks and machine learning-based analytics in data-driven smart sustainable cities, and building my argument by drawing on data collected from BDO, Capgemini, ICMA, KPMG, McKinsey, Philips, SCC, SmartCitiesWorld, and World Economic Forum, I performed analyses and made estimates regarding the progress of smart cities toward sustainability. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

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来源期刊

Geopolitics, History, and International Relations Arts and Humanities-History

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0.00%

发文量

期刊介绍： Geopolitics, History, and International Relations publishes mainly original empirical research and review articles focusing on hot emerging topics, e.g. digital diplomacy, online political participation, data activism, fake social media news, algorithmic governance, computational politics, Internet terrorism, autonomous weapons systems, virtual history, innovative data-driven smart urban ecosystems, etc. This journal considers only manuscripts having a high integrative value in the current Scopus- and Web of Science-indexed literature.