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.
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.
In a creative process, divergent thinking needs to be stimulated to generate novel ideas; yet these ideas must be synthesized to produce something valuable. Hence to foster creativity in developing IT products, creators need to manage the tension between novelty and value. Since the forces affecting the novelty-value tension often exist outside a creator's group or organization, we apply organizational ecology theory to propose an industry-level, ecological model for understanding the novelty of IT products.
This was a part of research projects conducted under the Basic Research Laboratory Grant from Ministry of Education, Science, and Technology in South Korea. This project is two folds: (1) simulating an application of swarm robot systems; (2) designing a software framework for the swarm robot systems to reduce the complexity of developing applications while minimizing the amount of transmitted data by adopting MapReduce paradigm. The video above is a simulation of a swarm robot system application that searches for red pillars (foraging).
Social media has provided a huge amount of user-generated data in capturing urban dynamics. Among them, place-level human behavior has been largely detected through people’s check-in records at certain places. Conventionally, places are characterized by a set of pre-defined features, often specified by the owner of the places. In this paper, we argue that capturing socially-meaningful features and dynamics of an urban place may also be done by analyzing human activity traces.
Information accessibility problems include diverse types of human- and system-driven barriers that make it difficult for individuals to access desired information. These issues have been studied in two main streams: (1) a human-centered view based on the understanding of individual-level characteristics such as physical impairment and economic status, and (2) a technology-focused view that emphasizes system-based factors such as the information filtering techniques and interface designs.
Hao Li (Ph.D. student from CS) and I conducted a big-data analysis project using the MapReduce framework (Hadoop) for the final project of INFM718G (Data-Intensive Computing with MapReduce, by Dr. Jimmy Lin). Targeting all the news images in April 2013, we tried to rank news images based on the importance and popularity level of each news image. To do that, we extracted image features using SIFT (Scale-invariant feature transform) and constructed a graph of images using LSH (Locality-sensitive Hashing) as a means to approximate the similarity of images.