LID Framework: A new method for geospatial and exploratory data analysis of potential innovation deter-minants at the neighborhood level

• URL: http://arxiv.org/abs/2602.04679v1
• Date: Wed, 04 Feb 2026 15:52:27 GMT
• Title: LID Framework: A new method for geospatial and exploratory data analysis of potential innovation deter-minants at the neighborhood level
• Authors: Eleni Oikonomaki, Belivanis Dimitris, Kakderi Christina,
• Abstract summary: We develop the Local Innovation Determinants database and framework to identify key enabling factors across regions.<n>We examine neighborhoods in New York and Massachusetts across four dimensions: social factors, economic characteristics, land use and mobility, morphology, and environment.<n>Results show that alternative data sources offer significant yet underexplored potential to enhance insights into innovation dynamics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The geography of innovation offers a framework to understand how territorial characteristics shape innovation, often via spatial and cognitive proximity. Empirical research has focused largely on national and regional scales, while urban and sub-regional geographies receive less attention. Local studies typically rely on limited indicators (e.g., firm-level data, patents, basic socioeconomic measures), with few offering a systematic framework integrating urban form, mobility, amenities, and human-capital proxies at the neighborhood scale. Our study investigates innovation at a finer spatial resolution, going beyond proprietary or static indicators. We develop the Local Innovation Determinants (LID) database and framework to identify key enabling factors across regions, combining traditional government data with publicly available data via APIs for a more granular understanding of spatial dynamics shaping innovation capacity. Using exploratory big and geospatial data analytics and random forest models, we examine neighborhoods in New York and Massachusetts across four dimensions: social factors, economic characteristics, land use and mobility, morphology, and environment. Results show that alternative data sources offer significant yet underexplored potential to enhance insights into innovation dynamics. City policymakers should consider neighborhood-specific determinants and characteristics when designing and implementing local innovation strategies.

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