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.

翻译：创新地理学为理解地域特征如何塑造创新提供了理论框架，通常通过空间邻近性与认知邻近性实现。现有实证研究主要集中于国家与区域尺度，而城市及次区域尺度的地理研究则关注较少。地方性研究通常依赖有限指标（如企业层面数据、专利、基础社会经济指标），鲜有研究能在街区尺度提供整合城市形态、流动性、便利设施与人力资本代理变量的系统性框架。本研究以更精细的空间分辨率探究创新问题，超越了专有或静态指标的限制。我们构建了本地创新决定因素（LID）数据库与框架，通过整合传统政府数据与基于API获取的公开数据，识别跨区域的关键赋能因素，从而更精细地理解塑造创新能力的空间动态机制。运用探索性大数据与地理空间数据分析方法及随机森林模型，我们从四个维度考察纽约与马萨诸塞州的街区特征：社会因素、经济特征、土地利用与流动性、形态与环境。研究结果表明，替代性数据源为深化创新动态认知提供了重要但尚未充分开发的潜力。城市政策制定者在设计与实施地方创新战略时，应充分考虑街区特有的决定因素与特征。