Toward a network graph-based innovation cluster density index

[摘要] Innovation clusters have been studied and examined in many forms: ranging from qualitative observations to in-depth analytical models and frameworks to long-term studies tracking the actors and entities making up an innovation cluster;;s ecosystem. Economic development marketing in municipalities, regions, and countries often make representations about their cluster;;s strengths, yet rarely have strong empirical data to support and back their claims. A wide array of cluster mapping visualization tools exist, yet most focus on aggregate numbers of ;;nodes,;; and offer far less insights into their connectedness and relationships between the entities, the ;;edges;; or links. The focus for this thesis is a bottoms-up perspective, with people as the core drivers of innovation. This paper seeks to supplement existing methods, by implementing an innovation cluster density index (CDI) as an indicator, (;;Cluster Rank;;) enabling broader comparisons within clusters (intra-cluster), as well as the modeling of distributed virtual clusters (inter-cluster). This method proposes an empirical analytical approach, using complex network theory, commercially and open source available application program interfaces (APIs), and weighted network graphs as a framework, which integrates these elements to depict a new descriptor for clusters, the Cluster Rank. Implementation of the method in software is outside the scope of this thesis, but is separately being developed and is defined as a software platform using linked data technologies to build it (;;Cluster Rank Engine;;). The proposed Cluster Rank Engine is people-centric, and takes into account the embedded network effects, of people, derived from network graph analytics. It presents a bottoms-up view to intersect with the relatively top-down approaches currently in place. It identifies five key attributes, the ;;Penta Helix;; and uses these as the core variables in modeling. Development of such a model enables the use of big data methods and algorithmic tools on the Internet to interrogate large distributed economic global datasets, query and extract the relevant pre-defined cluster attribute data, filter and process it to present a deeper analytically comparative lens of innovation clusters; both in terms of urban innovation mapping, cluster heat maps, etc. This method would enable, for example, the comparison of a biotech cluster in Cambridge with that of one in San Francisco (;;Global biotechnology clusters map,;; n.d.), at discrete levels. The Cluster Rank for each discrete innovation cluster provides additional data beyond traditional graphical visualizations. Utilization of the Cluster Rank Engine across a wide range of clusters could then yield deeper statistically comparative data for a deeper understanding of cluster dynamics and cluster endurance over time, as well as serve as data input for a variety of graphical data visualizations. (Berkhin, 2002) Utility for such a solution is multi-fold: as an economic cluster modeling and tracking tool, an innovation lens on a given sector or geography, and as a tool for urban innovation mapping. At its fruition, it becomes a potentially predictive tool for network resilience and failure, to help better navigate decisions related to the growth of innovation clusters and/or the linking of remote clusters for a virtual cluster, to help make decisions for: resource allocations, partnership and contractual targets, angel and venture funding strategies.

[发布机构] Massachusetts Institute of Technology