This PhD dissertation aims to advance the understanding of the ‘geography of technological innovation and the resulting geographical divides in the capacity of a region to produce and adopt technology to build technologically resilient communities against urgent challenges such as climate change, healthcare crises, and other unforeseen events. Geography is considered a crucial axis of socioeconomic inequalities, manifesting as places that are innovators and others that are left behind. The dissertation aims to disentangle people, places, and their environments to understand the factors that enable both the adoption and generation of innovation. The work is organized around three empirical studies at the local (neighborhood) level using data from Massachusetts and New York state, each exploring different dimensions of technological innovation: a) adoption, b) generation, and c) local innovation potential, with the last two focusing on innovation-enabling factors with spatial dimensions. The empirical studies are complemented by qualitative fieldwork carried out in neighborhoods across California. The dissertation explores the complex territorial and geographical enabling factors of technological innovation, aiming to uncover hidden synergies through the lens of neighborhood-level transitions. The research begins with the TREnD project, which highlights the pressing issue of regional and urban disparities across Europe and serves as the foundation for both the theoretical and practical dimensions of the study. The study places special emphasis on innovation, presenting it as a unique means of bridging theory and practice by offering measurable insights into urban transitions. Using a data-driven method, the research develops a conceptual framework to visualize city neighborhoods as complex systems that evolve into new states during multifaceted urban transitions (e.g., green, digital, economic). Central to this framework is the concept of adoption-based technological resilience, which, when effectively implemented, can drive digital and green transitions by improving quality of life and building communities resilient to urgent challenges such as climate change, healthcare crises, and other unforeseen events. To quantify and validate the theoretical model, quantitative methods are employed, with large-scale urban data analysis illuminating the relationship between neighborhood characteristics and technological innovation resilience by assessing technological innovation adoption before and during the shock. Given their low levels of generation, growth, and income, low-growth, low-income localities should first focus on adopting technological innovations to avoid falling behind. Without deliberate technological adoption, these areas risk falling further behind economically advantaged regions where technology integration happens more rapidly, creating a self-reinforcing cycle of inequality. Therefore, identifying neighborhood characteristics associated with the absorptive capacity of technological innovation is pivotal for assessing regional economic growth and resilience. At the heart of the dissertation is the concept of technological resilience, the ability of a neighborhood to adapt, generate technological innovations, reorganize, and redefine its growth trajectory amid unforeseen events, and the concept of local innovation capacity, the ability of a neighborhood to make use of the resources and increase its innovation generation. This requires a nuanced understanding of how territorial contexts respond to shocks and how neighborhood assets can create windows of opportunity for reshaping development trajectories. To achieve this, the project integrates theoretical approaches from innovation studies, economic geography, and urban resilience, linking them to practical indicators and metrics. In order to gain an understanding of variations in these transitions among different neighborhoods across a city, with an assessment of a) the technological innovation adoption and b) the generation capacity levels, we conduct a practical investigation into the factors influencing these processes. While most of the bibliography focuses on national and regional innovation capacity, the current research acknowledges the importance of localities for the development of innovation activity. The major contribution of the work is the update of the notion of “location” in the contemporary technological innovation context. The research prompts discussion of the primary neighborhood assets that can constitute enabling factors of innovation. The work advocates a 'place-sensitive distributed development’ policy identified as a primary need by several scholars in the field. We understand neighborhoods’ evolutionary trajectories based on their innovative capacities, such as their ability to foster innovation, adapt to change, and leverage assets, as this dramatically influences their ability to improve their social, economic, and physical conditions. More specifically, the research applies predictive modeling using artificial intelligence algorithms to data from neighborhoods recognized as prominent global innovation hubs. However, within these states, there are also neighborhoods with lower performance and limited innovation, highlighting significant disparities. The dissertation, therefore, examines thriving ecosystems, aiming to provide a comprehensive understanding of the territorial factors associated with innovation capacity and the challenges faced by underperforming neighborhoods and areas left behind. These three case studies offer a practical lens for understanding how innovation and technological resilience shape urban transitions toward greater livability, inclusivity, and environmental sustainability. The dissertation concludes with a place-based assessment of technological innovation capacities (generation and adoption), highlighting the effects of key neighborhood characteristics and offering actionable insights for policymakers, communities, and researchers to better support inclusive transitions and urban regeneration efforts. In this way, the study introduces a series of algorithms, mapping techniques, and conceptual frameworks, while providing a new methodological framework and tool for assessing innovation activities at the level of localities and thereby contributing to existing innovation measurement tools.
Ελένη Ι. Οικονομάκη (Thu,) studied this question.