The Agglomeration of American Research and Development Labs

Kristy Buzard, Gerald A. Carlino, R. Hunt, Jake K. Carr, T. Smith
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引用次数: 2

Abstract

We employ a unique data set to examine the spatial clustering of about 1,700 private research and development (R&D) labs in California and across the Northeast corridor of the United States. Using these data, which contain the R&D labs? complete addresses, we are able to more precisely locate innovative activity than with patent data, which only contain zip codes for inventors? residential addresses. We avoid the problems of scale and borders associated with using fixed spatial boundaries, such as zip codes, by developing a new point pattern procedure. Our multiscale core-cluster approach identifies the location and size of significant R&D clusters at various scales, such as a half mile, one mile, five miles, and more. Our analysis identifies four major clusters in the Northeast corridor (one each in Boston, New York?Northern New Jersey, Philadelphia?Wilmington, and Washington, D.C.) and three major clusters in California (one each in the Bay Area, Los Angeles, and San Diego).
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美国研发实验室的集聚
我们采用了一套独特的数据集,对加州和美国东北走廊地区约1700个私人研发实验室的空间集群进行了研究。用这些包含研发实验室的数据吗?完整的地址,我们能够比专利数据更精确地定位创新活动,专利数据只包含发明人的邮政编码。住宅地址。我们通过开发一种新的点模式程序,避免了使用固定空间边界(如邮政编码)所带来的规模和边界问题。我们的多尺度核心集群方法确定了不同尺度(如半英里、一英里、五英里甚至更大)的重要研发集群的位置和规模。我们的分析确定了东北走廊的四个主要集群(波士顿、纽约和纽约各一个)。新泽西北部,费城?威尔明顿和华盛顿特区)和加州的三个主要集群(湾区、洛杉矶和圣地亚哥各一个)。
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