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.
Social services, traditionally, have been organized around their missions, such as education or safety or health. A newer approach, called "wrap-around services" or "systems of care," organizes services around individuals and their specific context and needs. These systems face many challenges when applied in real-world settings. Application processes often focus more on the potential of technologies and less on the realities, histories, and needs of communities. The proposed research addresses this gap by evaluating the implementation of a system of care in a real-world setting.
Value sensitive design (VSD) is a methodology that focuses on examining potential stakeholders' values and establishing designers' values of ethical imports in designing a technological system. While this approach provides effective ways to incorporate users' values in technology design, understanding teachers' values in culturally responsive teaching (CRT) poses unique challenges due to their interactions with students' cultural identities, school environments, and community contexts.
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.
The 311 systems that city officials currently deploy can efficiently detect non-emergency civic issues such as potholes and trash. From a socio-technical perspective, residents can re-appropriate the technology for their own purpose adding new capacities and affordances not initially intended. For example, when Hurricane Irma hit Miami in 2017, residents used 311 systems to report disaster-related issues, which led city officials to adapt the system by creating a new category.
Urban renewal was a project of the American government that aimed to reconstruct poor urban neighborhoods. Because community-level data that shows the underlying mechanisms of urban renewal has not been curated in a systematic way, due to the complexity and volume of the relevant archival collections, we aim to digitally curate property acquisition documents from the urban renewal projects that affected the Southside neighborhood of the city of Asheville, North Carolina, in the form of a map-based, interactive web application. This paper reports early findings from interviews.
COVID-19 has killed hundreds of thousands, constituting a major global crisis. Laypeople bear a large burden of responsibility for perceiving risks associated with COVID-19 and taking action to manage risks in their everyday lives, yet epidemic-related information is characterized by uncertainty and ambiguity. People perceive risks based on partial, changing information.
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.
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.
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.