Sebastián Baeza-González

Spatial patterns of innovation and inventive activities are a significant topic in economic geography, as many studies try to unveil the relationship between innovation and proximity. Understanding patterns of concentration and dispersion in space and the spatial relationship across technological categories becomes essential to reveal the characteristics of knowledge sources and flow. This study aims to characterize the spatial patterns of inventive activity in Japan. Using patent data, we identify concentration patterns using point data and a relative concentration (Kemp) measure. We also assess spatial co-localization and co-dispersion by using the same indicator. These results cross-classify technologies by their concentration and co-localization characteristics and potential knowledge sources and flows, questioning the relevance of national innovation policy in concentration and co-localization patterns. Therefore, the analysis focused on two periods, 1975–1994 and 1995–2014, related to major changes in the national innovation policy. Our analysis reveals that between the two periods, Japan’s inventive geography shifted from a highly integrated system to more specialized, isolated clusters. Specifically, the later period demonstrates an increase in spatial concentration within technologies while showing a decline in co-localization across different fields. The importance of this trend is evident, as regionalization policies appear to have successfully promoted local specialization, strengthening regional/local capabilities, but have also resulted in a reduction of regional technological diversity. This situation raises concerns about an increased risk of technological lock-in and the potential decline in breakthrough innovations that depend on broader knowledge flows.

This article presents a methodological approach employing Emerging Hot Spot Analysis (EHSA) to track the life cycles of clusters of inventive activity. Evolutionary Economic Geography seeks to elucidate the spatial development of economic activity by analyzing past formations and their impact on current and future trends. We enhance this literature by offering an explanation of cluster life cycles through EHSA. EHSA is a spatial statistical methodology that integrates Getis-Ord Gi* and Mann–Kendall trend assessments to examine spatiotemporal variability. We analyze inventive activity utilizing Japanese patent data from 1980 to 2020 across the chemical, mechanical, computers and communications, and electrical and electronics broad technological categories. EHSA provides a statistical test to analyze the stages of a cluster’s life (emergence, growth, maturity, and decline) by finding patterns like new, intensifying, persistent, or diminishing hot/cold spots. Results indicate varying trajectories among technology categories: chemicals demonstrate post-growth decline; mechanicals are stable and expand; computers and communications remain confined to metropolitan areas; and electrical and electronics sectors bifurcate. The research underscores EHSA’s efficacy in delivering a reproducible and flexible approach to visualize and comprehend clustering dynamics from spatiotemporal data, hence facilitating informed policymaking. Nonetheless, it recognizes constraints, as cluster evolution encompasses more than mere agglomeration, indicating the need for future integration with network analysis and qualitative data for a thorough comprehension.

The global innovation system (GIS) approach has until now focused on defining industries by a single GIS configuration at a given time. However, by analysing 739 innovation projects in the Norwegian global salmon farming industry, we find that the four ideal GIS configurations can be identified simultaneously. We argue that this is because we refer to an industry with a large diversity of innovation projects generating different resources along varied scales of interaction. Our analysis is based on text analysis of descriptive documents and interviews about innovation projects. With this information, we built four GIS configurations that illustrate how actors, networks and institutions form and are connected through different types of subsystems. The purpose is to understand why some actors have diverse priorities when participating in different types of innovation projects within the same industry and suggest relevant policy ideas.