I-Corps: Advanced Analytics for Workforce Dynamics

这个I-Corps项目的更广泛的影响/商业潜力是，以一种减少他们在评估和预测区域劳动力需求上花费的时间和金钱的方式，改善劳动力规划人员可用的信息。在更准确的分析的帮助下，劳动力规划者将能够将更多的资源用于设计和开发培训计划，以填补近期到中期工人最可能出现的技能差距，从而使其所在地区在快速发展的劳动力市场中保持全球竞争力。这个项目的商业潜力来自于计算技术的进步，以经验确定行业特定技能的差距；绘制区域生产的相关结构图；并利用这些地图在区域经济中发现新的技能导向的职业路径。将这些技术进步带给劳动力分析师和政策制定者，将为他们提供最佳的可用工具，以适应其经济的创新需求。这个I-Corps项目使用先进的分析和大数据收集方法来评估城市工作的技术和空间分布，目标是在三个方面提供见解：技术扩散模式及其对劳动力的影响；基于人力资本的区域经济演化竞争优势研究以及区域“经济生态系统”的结构作为创业和创新的源泉。该算法搜索区域经济活动、能力和技术的参数空间，以描述给定区域内的实际工作结构。实际上，这种创新使劳动力规划策略的关键方面实现了自动化，这些方面通常既耗时又昂贵，因为它们通常依赖于调查。本文开发的方法将提高劳动力市场分析的速度和准确性。

The broader impact / commercial potential of this I-Corps project is to improve the information available to workforce planners in a way that reduces the time and money they spend in assessing and anticipating regional workforce needs. With the help of more accurate analytics, workforce planners will be able to shift more of their resources towards designing and developing training initiatives that fill the most likely skills-gaps for workers in the near- to mid-term, thus keeping their regions globally competitive in rapidly-evolving labor markets. The commercial potential of this project derives from advances in computational techniques to identify industry-specific skills gaps empirically; to map the related structure of regional production; and to uncover new skills-induced career pathways in regional economies using those maps. Bringing these technical advances to workforce analysts and policy makers will provide both with the best available tools to respond adaptively to the innovative needs of their economies.This I-Corps project uses advanced analytics and Big Data collection methods to assess the technical and spatial distribution of work in cities with the goal of providing insights along three dimensions: patterns of technology diffusion and their workforce implications; human capital-based evolutionary competitive advantages of regional economies; and the structure of regional "economic ecosystems" as a source of entrepreneurship and innovation. The algorithms search the parameter space of regional economic activities, capabilities, and technologies to describe the actual structure of work within a given region. In effect, this innovation enables the automation of key aspects of workforce planning strategy that are typically time-consuming and expensive because they typically rely on surveys. The approach developed here will enhance the speed and accuracy of labor market analysis.