This research will develop and design new data-driven risk prediction principles and management (DDRPM) tools that anticipate and manage a variety of community risks, which fire departments are increasingly required to respond to, including medical, fire, and safety emergencies. Today, much of their work focuses on community risk reduction (CRR), a paradigm that seeks to mitigate risks before they lead to emergencies in the first place. The CRR paradigm will leverage new data-driven risk prediction and management (DDRPM) tools to predict and respond to a variety of community risks.
Delaying routine health care has been prevalent during the COIVD-19 pandemic. Macro-level data from this period reveals that U.S. patients under-utilized routine health care services such as primary care visits, preventative tests, screenings, routine optometry care, dental appointments, and visits for chronic disease management. Yet, there is a gap in research on how and why patients understand risks associated with seeking or delaying routing health care during an infectious disease pandemic.
Many cities in the United States use civic technologies like 311 systems as part of their public service systems for monitoring non-emergency civic issues. These systems have enhanced the city's monitoring capability by diversifying communication channels. However, the data created through these systems is often biased because of differences in people's use of technology (i.e., digital divide) and individuals' behavioral patterns in providing types of information to the systems.
Independent business owners often prefer informal information sources to formal ones such as library collections. Part of these preferences is rational because contextual and application-oriented information is usually available from informal sources, which are theoretically the best matches for this kind of information. This suggests that, in addition to outreach strategies, efforts to integrate informal sources of business-relevant information can improve public libraries' ability to support independent business owners' information needs.
Cultural diversity has been conceptualized and studied in diverse ways. On the one hand, cultural diversity can be conceptualized based on people’s ethnic and national backgrounds. On the other hand, cultural dimensions are defined based on individuals' behaviors and traits. Sociologists further categorize the latter depending on the degree of typicality in cultural artifacts/activities and individuals’ omnivorousness over cultural tastes.
To understand issues about information accessibility within communities, research studies have examined human, social, and technical factors by taking a sociotechnical view. While this view provides a profound understanding of how people seek, use, and access information, this approach tends to overlook the impact of the larger structures of information landscapes that constantly shape peoples access to information.
KNEXT is a three-year collaborative project between Kent State University (KSU-SLIS) and the University of Maryland (UMD-CIS), which partners with local public libraries, small business development centers, economic development organizations, and community advocacy groups to bring advanced data analytics and business intelligence (DA&BI) services to public libraries in order to support small businesses, entrepreneurs, and community advocates within two recovering communities in Ohio and Maryland.
Today's digital revolution is fostering remarkable innovations. The landscape of innovation is rapidly changing and thus difficult to navigate or study. Understanding broader participation in innovation requires large datasets on the innovation participants and their activities. As organizational and industry boundaries become fuzzy in the digitized world, small data analysis cannot fully explain how boundaries shift and evolve. Open innovation leads to overlapping roles of designer and user, making it inadequate to examine innovation development and adoption separately.
This research will develop a foundational tool for understanding how civic technologies are used and how information inequalities manifest in a city. User data from new civic technologies that reveal inequalities in the information environments of citizens has only recently become available. Since a large portion of data is demographically or geospatially biased due to varying human-data relationships, computational social scientists have used data modeling and algorithmic techniques to adjust the data and remove biases during data-processing.
To understand issues about information accessibility within communities, research studies have examined human, social, and technical factors and contexts by taking a socio-technical view. While this view provides a profound understanding of how people seek, use, and access information, this approach tends to overlook the impact of the larger structures of information landscapes that shape people’s access to information.