智慧城市成熟度模型:多维综合方法

Sepehr Ghazinoory, Jinus Roshandel, Fatemeh Parvin, Shohreh Nasri, Mehdi Fatemi
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引用次数: 0

摘要

智慧城市是数字化转型的结果之一,人们已经尝试用各种框架来评估城市的智慧。在这些框架中,智慧城市成熟度模型(scmm)评估城市的现有条件,并为随后的成熟阶段提供指导。然而,大多数成熟度模型都遵循由International Data Corporation发布的第一个模型的说明,并且这些模型之间有许多相似之处。这些成熟度模型各有优缺点,而以往的研究并没有解决这些差异。因此,本文通过系统地回顾现有的scm来填补这一知识空白。研究结果表明,scmm忽略了一些趋势主题,例如与全球流行病和文化方面有关的弹性。此外,模型的验证技术也不合理。最后,考虑到大多数模型的理论性质,它们不能适用于多个地区。本文分类如下:数据和知识的基本概念>大数据挖掘技术;人工智能技术;机器学习
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Smart city maturity models: A multidimensional synthesized approach
Smart cities are one of the consequences of digital transformation, and there have been many attempts to assess the smartness of cities with various frameworks. Among these frameworks, smart city maturity models (SCMMs) evaluate the existing conditions of cities and provide guidelines for progressing through the subsequent stages of maturity. However, most maturity models follow the instructions of the first model, published by the International Data Corporation, and there are many similarities across the models. These maturity models have advantages and disadvantages, while previous studies have not addressed the differences. Therefore, this article fills this knowledge gap by systematically reviewing the existing SCMMs. The findings suggest that some trending topics, such as resiliency concerning global pandemics and cultural aspects are neglected in SCMMs. Moreover, the validation techniques of the models are not rational. Finally, given the theoretical nature of most models, they cannot be applied to multiple regions.This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence Technologies > Machine Learning
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