While civic technologies for public issues and services such as 311 systems are widely adopted in many U.S. cities, the impact of the emerging civic technologies and their datalevel dynamics are unclear. Because the provision patterns of civic issues to technological systems are different across neighborhoods and populations, it is difficult for city officials to understand whether the provided data itself reflects civic issues. Also, the disparities in the information provided to civic technologies in different neighborhoods may exacerbate the existing inequality.
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.
Neighborhoods are vaguely defined, localized regions that share similar characteristics. They are most often defined, delineated, and named by the citizens that inhabit them rather than municipal government or commercial agencies. The names of these neighborhoods play an important role as a basis for community and sociodemographic identity, geographic communication, and historical context.
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.
Urban renewal was a national initiative from the 1960s through 70s aimed at improving so-called “blighted” areas, and resulted in the displacement of many vibrant communities. While the underlying mechanisms of urban renewal have been examined, there have been very few data-driven, evidence-based studies that take into account the histories and interests of former residents. The “Human Face of Big Data” project started as a digital curation effort to design and develop a web-based, big data platform that provides insights and analytics into the mechanisms of this process.
Computational Archival Science (CAS) has been proposed as a trans-disciplinary field that combines computational and archival thinking. To provide grounded evidence, a foundational paper explored eight initial themes that constitute potential building blocks. In order for a CAS community to emerge, further studies are needed to test this framework. While the foundational paper for CAS provides a conceptual and theoretical basis of this new field, there is still a need to articulate useful guidelines and checkpoints that validate a CAS research agenda.