Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution

Friday, June 1, 2018, 11:00 am - 12:00 pm PSTiCal
6th floor large conference room
This event is open to the public.
AI Seminar
Yao-Yi Chiang, USC Spatial Computing Laboratory
Video Recording:

Air quality models are essential for studying the impact of air pollutant on health conditions. Existing air quality models typically rely on area-specific, expert-selected attributes of pollution emissions (e,g., traffic) and dispersion (e.g., meteorology). Moreover, these model attributes can vary significantly for air quality models of different study areas, pollutant types, and spatiotemporal scales. This talk presents a data-driven approach that utilizes publicly available data (e.g., OpenStreetMap) to automatically generate an air quality model for the concentrations of fine particulate matters less than 2.5 µm in aerodynamic diameter at various temporal scales. Our experiment shows that our (domain-) expert-free model could generate accurate PM2.5 concentration predictions, which can be used to improve air quality models that traditionally rely on expert-selected input. Our approach also quantifies the impact on air quality from a variety of geographic features (i.e., how various types of geographic features such as parking lots and commercial buildings affect air quality and from what distance) representing mobile, stationary and area natural and anthropogenic air pollution sources. This approach is particularly important for enabling the construction of context-specific spatiotemporal models of air pollution, allowing investigations of the impact of air pollution exposures on sensitive populations such as children with asthma at scale.


Yao-Yi Chiang is an Associate Professor (Research) in Spatial Sciences, the Director of the Spatial Computing Laboratory at the Spatial Sciences Institute, and the Associate Director of the NSF's Integrated Media Systems Center (IMSC) at the University of Southern California (USC). Dr. Chiang received his Ph.D. degree in Computer Science from the University of Southern California; his bachelor’s degree in information management from the National Taiwan University. His general area of research is information integration and data mining with a focus on spatiotemporal data and their applications. Dr. Chiang is also an expert on digital map processing and geospatial information system (GIS). His research interests further include computer vision, image processing, and semantic web. Dr. Chiang develops computer algorithms and intelligent systems that discover, collect, fuse, and analyze data from heterogeneous sources to solve real-world problems. Before USC, Dr. Chiang worked as a research scientist for Geosemble Technologies and Fetch Technologies in California. Geosemble Technologies was founded based on a patent on geospatial data fusion techniques, and he was a co-inventor.

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