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
- URL: http://arxiv.org/abs/2304.06039v1
- Date: Tue, 4 Apr 2023 05:45:50 GMT
- Title: 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
- Authors: Eleni Oikonomaki, Dimitris Belivanis
- Abstract summary: The study uses a large geographically distributed dataset across Boston's 35 zip code areas.
In order to express the innovation performance of the zip code areas, new metrics are proposed connected to innovation locations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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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