Home // GEOProcessing 2019, The Eleventh International Conference on Advanced Geographic Information Systems, Applications, and Services // View article
Authors:
Bing-Si Ni
YuChieh Huang
Chun-Ming Huang
Keywords: spatial-temporal patterns analysis ； air pollution events ； Internet of Things; sensors ； industrial parks
Abstract:
To identify sources of pollution and predict future pollution events, the Environmental Protection Administration of Taiwan has deployed dense sensor networks in industrial districts. In face of overwhelming real-time data collected from the Internet of Things (IoT) applications for smart environmental sensing, no standard procedure based on the space-time statistical methods, such as Getis-Ord G* or Moran's I exist for defining and analyzing pollution events. This study used raw data generated from microsensors as the data source, adopted spatial statistics to perform hotspot analysis, and then define the event base on the result of statistical hypothesis and grid connectivity. This approach was also effective in distinguishing independent pollution events when two or more events occurred concurrently in the same region. Finally, spatial and temporal descriptive statistical analysis was performed on the targeted pollution events, including the identity of pollution events through spatial-temporal hotspot analysis integrated with the visualized display method.
Pages: 18 to 22
Copyright: Copyright (c) IARIA, 2019
Publication date: February 24, 2019
Published in: conference
ISSN: 2308-393X
ISBN: 978-1-61208-687-3
Location: Athens, Greece
Dates: from February 24, 2019 to February 28, 2019