Big Data Analytics

The Geomatics lab analyses a wide range of big data sources, including social media, online reviews, citizen science platforms, news channels, online searches, crowdsourced drone and street imagery, and collaborative mapping platforms to explore and model human activity patterns (e.g., visitation of and activities in State parks), travel behavior (e.g., in response to natural disasters) and user sentiments (e.g., perception of locations or events). Social media networks provide an enormous volume of shared data with hundreds of millions (Twitter) or billions (Facebook) of daily active users. Despite this enormouse data volume research is needed to assess the data quality and potential bias of big data to better understand and capture its usability for geo-applications. Big data can be used for the spatio-temporal analysis of human activity patterns.

This includes a) the modeling of refugee migration patterns, b) the extraction of travel behavior (e.g., rescue teams being dispatched to locations of natural disasters), c) the identification of factors associated with increased levels of outdoor activities (i.e., hiking, running, cycling, kayaking) in recreational parks or urban areas, or d) the use of shared photographs to determine the impact areas of natural disasters, such as hurricanes or floods.

Selected publications: