Professor Wang studies the interplay between urban informatics and urban, infrastructure, and social resilience. His research focuses on two interrelated areas: geosocial networks in big cities (collaborating with Harvard University, and Microsoft Research), and human movement perturbation under the influence of natural and manmade disasters (collaborating with Georgia Tech, Berkeley, and Virginia Tech). The ‘big data’ of urban informatics used in his research are from Twitter, wireless providers, Google Maps, Google Places, Google StreetViews, Microsoft Bing Maps, Boston government, etc.
Areas of Expertise (5)
Virginia Tech: Ph. D. 2015
Michigan State University: M.S. 2012
Tianjin University: M.S. 2009
Tianjin University: B.S. 2007
Jiayu Chen, Ryan Qi Wang, Zhenghang Lin, Xiaoyang Guo
Many unsafe behaviors in construction are associated with workers' insufficient vigilance and misperception of risks. Safety signs are designed to provide warning and raise worker's attention in hazardous environments. Many researchers conducted interviews and questionnaires to assess the effectiveness of various safety sign designs; however, the results are deemed subjective and biased due to personal differences such as experience, age, attitude, and working environment...
Qi Wang, John E Taylor
Understanding of human movements in urban areas plays a key role in improving our disaster response, evacuation, and relief plans. However, there is a lack of research on human mobility perturbation under the influence of hurricanes. Furthermore, limited simulation studies have had access to empirical human travel data in urban areas during natural disasters...
Yan Wang, Qi Wang, John E Taylor
Increasing frequency of extreme winter storms has resulted in costly damages and a disruptive impact on the northeastern United States. It is important to understand human mobility patterns during such storms for disaster preparation and relief operations. We investigated the effects of severe winter storms on human mobility during a 2015 blizzard using 2.69 million Twitter geolocations...
Qi Wang, John E. Taylor
Human mobility is central to our understanding of design, planning, and development of civil infrastructure in urban areas. Although researchers have spent considerable effort in studying human mobility patterns, there is still a lack of human movement data with satisfactory quantity and accuracy. This paper introduces an approach to collecting human mobility data and discusses analyses conducted. A comprehensive process map was developed to collect human movement data from Twitter. The map included four steps and multiple programmed modules, processes, and databases. Via the process map, human mobility data was collected, and a one-month subset from New York City was retrieved to use in a case study. Results from the case study aligned with findings from existing human mobility research, and thus Twitter was confirmed to be a viable resource for studying city-scale human mobility. Large-scale human mobility data will allow researchers to study the interdependence of human activity and civil infrastructure as a way to deepen understanding of important city-scale phenomena such as evacuation during extreme events and the spread of epidemics.
Qi Wang, John E. Taylor
Natural disasters pose serious threats to large urban areas, therefore understanding and predicting human movements is critical for evaluating a population’s vulnerability and resilience and developing plans for disaster evacuation, response and relief. However, only limited research has been conducted into the effect of natural disasters on human mobility. This study examines how natural disasters influence human mobility patterns in urban populations using individuals’ movement data collected from Twitter. We selected fifteen destructive cases across five types of natural disaster and analyzed the human movement data before, during, and after each event, comparing the perturbed and steady state movement data. The results suggest that the power-law can describe human mobility in most cases and that human mobility patterns observed in steady states are often correlated with those in perturbed states, highlighting their inherent resilience. However, the quantitative analysis shows that this resilience has its limits and can fail in more powerful natural disasters. The findings from this study will deepen our understanding of the interaction between urban dwellers and civil infrastructure, improve our ability to predict human movement patterns during natural disasters, and facilitate contingency planning by policymakers.