创新绩效预测的新视角:一种数据驱动的方法,通过波士顿社区的比较研究确定创新指标

E. Oikonomaki, Dimitris Belivanis
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引用次数: 0

摘要

在知识经济、研究商业化和人才全球化竞争的时代,创新生态系统和创新网络的创建是城市努力的前沿。在这种背景下,政府当局、私人组织和学术界通过各种创新记分牌来回答最有希望预测创新的指标的问题。本文旨在利用来自非传统来源的大型数据集,加深对现有指标的理解,并补充各种创新评估工具包。自上而下实施的创新区和社区层面的创新生态系统的成功是复杂的,尚未得到很好的研究。然而,有限的数据揭示了邻里层面的指标与创新绩效之间的关系。为此,波士顿市被选为案例研究,以揭示其社区的不同特征对实现高创新绩效的重要性。该研究使用了波士顿35个邮政编码地区的大型地理分布数据集,其中包含各种商业、创业特定、社会经济数据和其他类型的数据,可以揭示城市背景维度。此外,为了表达邮政编码地区的创新绩效,提出了与创新地点相关的新指标。本分析的结果旨在引入“邻里创新指数”,该指数将产生新的规划模型,以提高创新绩效,这可以很容易地应用于其他案例。通过发布这一大规模的城市信息学数据集,目标是为创新话语做出贡献,并建立一个新的理论框架,以确定城市社会经济特征与创新绩效之间的联系。
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A new perspective on the prediction of the innovation performance: A data driven methodology to identify innovation indicators through a comparative study of Boston's neighborhoods
In an era of knowledge-based economy, commercialized research and globalized competition for talent, the creation of innovation ecosystems and innovation networks is at the forefront of efforts of cities. In this context, public authorities, private organizations, and academics respond to the question of the most promising indicators that can predict innovation with various innovation scoreboards. The current paper aims at increasing the understanding of the existing indicators and complementing the various innovation assessment toolkits, using large datasets from non-traditional sources. The success of both top down implemented innovation districts and community-level innovation ecosystems is complex and has not been well examined. Yet, limited data shed light on the association between indicators and innovation performance at the neighborhood level. For this purpose, the city of Boston has been selected as a case study to reveal the importance of its neighborhood's different characteristics in achieving high innovation performance. The study uses a large geographically distributed dataset across Boston's 35 zip code areas, which contains various business, entrepreneurial-specific, socio-economic data and other types of data that can reveal contextual urban dimensions. Furthermore, in order to express the innovation performance of the zip code areas, new metrics are proposed connected to innovation locations. The outcomes of this analysis aim to introduce a 'Neighborhood Innovation Index' that will generate new planning models for higher innovation performance, which can be easily applied in other cases. By publishing this large-scale dataset of urban informatics, the goal is to contribute to the innovation discourse and enable a new theoretical framework that identifies the linkages among cities' socio-economic characteristics and innovation performance.
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