Measuring the Impact of Urban Innovation Districts

Fatime Barbara Hegyi (), Manran Zhu () and Milan Janosov ()
Additional contact information
Fatime Barbara Hegyi: European Commission - JRC, https://joint-research-centre.ec.europa.eu/index_en
Manran Zhu: Central European University
Milan Janosov: Datapolis

No JRC125559, JRC Research Reports from Joint Research Centre

Abstract: Despite their significant impact on social and economic development, innovation districts are facing challenges due to inadequacy of policies in terms of horizontal and vertical coordination or due to the lack of integrative policy approach. Strategic and targeted policy support leads to the acceleration of the growth of innovation districts, impacting the development of cities in general. To reach the potential of innovation districts in benefiting their local communities and in enabling greater collaboration, in creating jobs, and in promoting regional competitiveness, it is important to facilitate the positive externalities created by innovation districts through targeted policies. Hence the publication proposes a generic and algorithmic methodology to identify and measure the success of innovation districts. To achieve this, different sets of large-scale geospatial data have been combined with well-established machine learning methods and in-depth statistical analysis. As a result, a quantitative methodology is presented that can support the policy-making process in the identification of urban areas with a high concentration of innovation activities and with high potential for growth. First, this methodology allows the identification of such areas. Second, an evaluation framework is proposed that captures the success of these areas based on their economic performance. Third, these results are combined with descriptive statistical features to understand the main differentiators between successful and unsuccessful areas. This exploratory research aims at providing a set of methods and findings that heavily build on recent advances on using large-scale datasets and data science to understand social problems, and in particular, the key driving indicators of deprivation and success of various entities, such as urban areas with high concentration of innovation activities.

Keywords: innovation districts; cities; urban development; data science (search for similar items in EconPapers)
Pages: 65 pages
Date: 2021-09
New Economics Papers: this item is included in nep-big, nep-cse, nep-sbm and nep-ure
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Citations: View citations in EconPapers (1)

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