I. Spatial data analysis & visualization
Crowd-sourced Cognitive Map
Reinventing cognitive maps by utilizing text information retrieved from crowd-sourced social media data in order to objectify city's image and identify spatio-temporal changes in place identity before/after urban regeneration projects.
Urban Green Accessibility
Developing a new "Urban Green Accessibility" index that measures people's accessibility to green space in real-access situations by integrating network analysis and remote sensing technology.
Mobility-on-Demand (MOD) Demand Estimation
Estimating and visualizing the transportation demand of mobility-on-demand (MOD) service by considering multisource data, such as travel behavior data, Google Map data ("Popular Times"), coefficient of traffic inducement, public transit-to-MOD conversion rate, etc.
Spatial Property-based Street Cluster
Suggesting three components that define characteristics of street segments - topology, size and function - to better explain and predict the patterns of human movement (e.g. annual pedestrian flow or geotagged social media data) by attempting to classify street segments via mixed-attribute clustering of network measures (e.g. betweenness or integration), segment length, segment width, slope, land use, etc.
II. Urban design research
Urban Design for Autonomous Vehicles
Redesigning urban elements and structure for autonomous vehicles and preparing design strategies for their wide-scale implementation in cities.
Airzone Classification for UAVs
Classifying urban airspace into different "Airzone"s, by constructing a three dimensional network and examining both spatial and network features of airspace for an unmanned aerial vehicle (UAV) flight environment
Suggesting improvement strategies for value creation with an Industry 4.0 perspective in Daejeon Industrial Complex, a decaying industrial area in Daehwa-dong, Daejeon,