REU Site: Using Data Science Tools to Improve Neighborhoods

该项目由社会，行为和经济（SBE）科学理事会的本科生研究经验（REU）网站计划资助。该计划将利用公开的数据源，让学生参与德克萨斯州达拉斯的经济，环境和基础设施挑战的探索。为期八周的课程将从为期三周的社会科学理论和研究方法讲座开始，包括基于地点的经济学，数据收集方法和来源以及数据伦理。同时举办的技术研讨会将使学生掌握编码、数据处理和使用绘图软件的技能。然后，每个参与者将加入一个研究团队，专注于测量城市社区之间的差异或减轻差异。REU网站的目标是为学生提供数据科学方法和工具的专业知识，用于研究影响小地区的问题;提高对数据科学的兴趣，以及参与学生在相关领域进行研究生学习的可能性，特别是对于在这些领域代表性不足的学生;让学生参与需要跨学科技能和知识的研究;研究小组的课题包括：（a）评估郡投票站位置的公平性;（B）描述城市热岛的位置和强度随时间的变化，以及它与社区的接近程度;（b）评估郡投票站位置的公平性。（c）评估小城市地区的经济发展、劳动力发展和负担得起的住房干预措施;（d）利用数据驱动的调查结果，教育有毒化学废物场地附近的社区并赋予其权力。为了解决问题，每个团队将使用专门的方法和工具，学生将在他们的指导教师和研究生团队的监督下接触这些方法和工具。这些方法将因问题而异，但总体上包括因果推理，复杂的调查设计和分析，聚类和预测，数据可视化和讲故事，以及以人为本的设计。 研究结果将在全校范围内为所有夏季本科研究人员举办的三分钟论文竞赛中分享，并有机会在大学本科研究杂志上发表论文。除了参与者对这些研究的直接贡献外，他们通过检索、整理和管理公共数据所产生的数据产品将为有关达拉斯的集体数据资源做出宝贵贡献，这些数据资源可以与当地研究人员共享。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the Directorate for Social, Behavioral, and Economic (SBE) Sciences. This program will engage students in exploration of economic, environmental, and infrastructure challenges in Dallas, Texas, using publicly available data sources. The eight-week program will begin with a three-week period of lectures in social science theory and research methods, including place-based economics, data collection methodologies and sources, and data ethics. Simultaneous technical workshops will equip students with skills in coding, data wrangling, and use of mapping software. Each participant will then join a research team focused on measuring variability or mitigating disparities across urban neighborhoods. The objectives of the REU site are to provide students with expertise in data science methods and tools for studying problems affecting small geographies; to increase interest in data science and the likelihood that participating students will pursue graduate studies in a related field, especially for students underrepresented in these fields; to engage students in research that requires interdisciplinary skills and knowledge; and to increase collaboration across disciplinary boundaries for both faculty and students, and between university researchers and community partners.Examples of research team topics are: (a) assessing the fairness of county polling place locations; (b) describing the change in urban heat island locations and intensity over time, as well as its proximity to neighborhoods; (c) evaluating economic development, workforce development, and affordable housing interventions in small urban areas; and (d) educating and empowering neighborhoods near toxic chemical waste sites using data-driven findings. To address its questions, each team will use specialized methodologies and tools, which the students will be exposed to under the supervision of their mentoring faculty and graduate student team. The methodologies will vary by problem, but collectively include causal inference, complex survey design and analysis, clustering and prediction, data visualization and storytelling, and human-centered design. The research findings will be shared in a university-wide three-minute thesis contest for all summer undergraduate researchers, and an opportunity to publish a paper in the universities Journal of Undergraduate Research. Besides the participants’ direct contribution to these studies, the data products they produce by retrieving, wrangling, and curating the public data will make a valuable contribution to a collective data resource about Dallas, which can be shared with local researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.