For the past five years, Dr. Jason Ware has centered community-based research and service-learning courses around local community partners’ needs as they focused collectively on community well-being issues. The nature of their work has prioritized qualitative research methods such as narrative inquiry via in-depth interviews and ethnography via immersive observations within varying service-providing institutions such as the Hartford Hub and the Hanna Community Center. COVID-19 and the constant threat of its transmission meant that Dr. Ware, his students, and their community partners had to approach their work differently. They responded with a pivot. They turned to mining large publicly accessible and proprietary data sets, such as United States Census data, Home Mortgage Disclosure Act (HMDA) data, the Homeless Management Information System (HMIS) data, and the Polk Directory data. The pivot served as a direct response to the City of Lafayette’s need for useful data that could inform decision-making related to neighborhood revitalization, affordable housing, and homelessness intervention. This different approach impacted the co-authors’ learning and scholarly development and provided the community partners with useful data. The co-authors experienced increased autonomy in pursuing data-specific questions, extracting data, analyzing, and visualizing it. One of the co-authors taught himself Python to import, statistically analyze, and visualize the data, and then presented the findings to the City of Lafayette. The co-author’s initial work — a pilot study — led to a scaled-up project that resulted in five significant outputs for three different community partners with a direct impact on six neighborhoods in the north end of Lafayette. Another co-author, who focused on scholarship during the pandemic, led an effort to develop a comprehensive literature review focused on the effect of community-based robotics programs on minority youth. The co-author also had presentations accepted at the local, national, and international levels while working on multiple publications.

The third co-author is partnering with the other authors to create an automated system that will support the collection, extraction, and analysis of secondary data that will facilitate sustainable data analysis into the future.