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.
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.
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.
Natural disasters such as hurricanes, floods or tornadoes affect millions of individuals every year. As a result, governments spend millions of dollars in emergency response allocating resources to mitigate the damages. Effective resource allocation requires a deep understanding of how humans react when a disaster takes place. Due to the multiplicity of human behavior, however, it is not trivial to understand human behaviors at large scale during and after the disaster.
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.
The Cycle Atlanta project aims at creating sensor systems that allow a bike to "see" its environment and collect data as a participatory effort so that we can help the City of Atlanta to make informed decisions about biking infrastructures. Specifically, a sensor box equipped with sonars, lidars, PM sensors, gas sensors, gyroscope, accelerometer, and others was developed to detect environmental factors that can give rise to cyclists' stress level. I participated in this project as a Data Science for Social Good (Atlanta's DSSG) Summer fellow in 2017.
Defining neighborhood boundaries within a city is a complex and often subjective task. Neighborhoods boundaries are defined by the people that visit and live in the region, and activities that occur within those boundaries. Depending on the individual or group activity being conducted, these boundaries can change substantially. Transportation and human mobility patterns offer a novel basis on which to explore and delineate neighborhoods.
"Poverty maps" are designed to simultaneously display the spatial distribution of welfare and different dimensions of poverty determinants. The plotting of such information on maps heavily relies on data that is collected through infrequent national household surveys and censuses. However, due to the high cost associated with this type of data collection process, poverty maps are often inaccurate in capturing the current deprivation status.
This project aims to build a map-based platform that presents historical documents of the nation-wide, urban renewal project in 1960's and 70's and to provide easy-to-use interfaces that can be used by former residents, archivists, researchers, and citizens. Ultimately, this platform aims to reconstruct a virtual neighborhood where people can share their memories by creating social networks of former residents.