2018, Volume 4, Issue 2
Engineering >> 2018, Volume 4, Issue 2 doi: 10.1016/j.eng.2018.03.010
Understanding Infrastructure Resiliency in Chennai, India Using Twitter’s Geotags and Texts: A Preliminary Study
a Del E. Webb School of Construction, School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, USA
b Department of Technology and Construction Management, Missouri State University, Springfield, MO 65897, USA
c School of Computing, Informatics and Decision Science Engineering, Arizona State University, Tempe, AZ 85287, USA
d Department of Civil Engineering, Construction Management Technology, Oklahoma State University, Stillwater, OK 74078, USA
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Abstract
Geotagging is the process of labeling data and information with geographical identification metadata, and text mining refers to the process of deriving information from text through data analytics. Geotagging and text mining are used to mine rich sources of social media data, such as video, website, text, and Quick Response (QR) code. They have been frequently used to model consumer behaviors and market trends. This study uses both techniques to understand the resilience of infrastructure in Chennai, India using data mined from the 2015 flood. This paper presents a conceptual study on the potential use of social media (Twitter in this case) to better understand infrastructure resiliency. Using featureextraction techniques, the research team extracted Twitter data from tweets generated by the Chennai population during the flood. First, this study shows that these techniques are useful in identifying locations, defects, and failure intensities of infrastructure using the location metadata from geotags, words containing the locations, and the frequencies of tweets from each location. However, more efforts are needed to better utilize the texts generated from the tweets, including a better understanding of the cultural contexts of the words used in the tweets, the contexts of the words used to describe the incidents, and the least frequently used words.
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