In his research, Kaiqun Fu uses spatial data mining, urban computing, and machine learning to infer crime rates/types from street view images, roadway networks, and criminal records. Applying deep learning methods also uncover hidden safety-related patterns from the physical appearance of street blocks that help address urban safety issues.
“My original intent was to improve on previous work done on crime type classification problems with spatiotemporal data such as criminal records and roadway networks,” said Fu, a Ph.D. student at the Discovery Analytics Center advised by Chang-Tien Lu. “But when we were able to access street view images from one of our sponsors, the District Department of Transportation, we saw a potential opportunity to explore crime rates and type prediction from street view images, as well.”
Next month at the 2018 ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Fu will present a paper that he has coauthored about this research: “StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception.”
Fu has also worked with the District Department of Transportation (DDOT) on a research project applying social media analysis to intelligent transportation systems.
“My team and I developed a social media-based transportation status monitoring and situation summarization system for DDOT,” he said. “The proposed system monitors and retrieves transportation-related tweets and, based on the retrieved Twitter data, the system is capable of detecting traffic incidents and highlight traffic status with text summarization techniques.”
Fu has presented two coauthored papers on the research for DDOT: “Steds: Social Media Based Transportation Event Detection with Text Summarization,” at the IEEE International Conference on Intelligent Transportation Systems (ITS); and “Social media data analysis for traffic incident detection and management” at the Transportation Research Board (TRB) conference, both in 2015.
Fu holds a master’s degree in computer science from Virginia Tech and is projected to graduate with a Ph.D. in computer science in 2019.
“High influence in my area of research is mainly what attracted me to the university and the Discovery Analytics Center,” said Fu. “As such an active player in the data mining, machine learning, and urban computing research fields, DAC has provided me great opportunities for working with interdisciplinary corporations.”