FluMapper: An Interactive cyberGIS environment for massive location-based social media data analysis

Anand Padmanabhan, Shaowen Wang, Guofeng Cao, Myunghwa Hwang, Yanli Zhao, Zhenhua Zhang, Yizhao Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Social media, such as social network (e.g., Facebook), microblogs (e.g. Twitter) have experienced a spectacular rise in popularity, and attracting hundreds of millions of users generating unprecedented amount of information. Twitter, for example, has rapidly gained approximately 500 million registered users as of 2012, generating 340 million tweets daily. Although each tweet is limited to only 140 characters, the aggregate of millions of tweets may provide a realistic representation of landscapes for a certain topic of interest. Furthermore, with widespread use of location aware mobile devices, users are sharing their whereabouts through social media services. This has resulted in a dramatic increase in volume of spatial data and they are becoming a crucial attribute of social media. These location-based social media thus could provide valuable insights to understanding many geographic phenomena. Recent studies capitalizing on social networking and media data show significant societal impacts, in many areas including infectious disease tracking [1].

Original languageEnglish
Title of host publicationProceedings of the XSEDE 2013 Conference
Subtitle of host publicationGateway to Discovery
DOIs
StatePublished - 2013
EventConference on Extreme Science and Engineering Discovery Environment, XSEDE 2013 - San Diego, CA, United States
Duration: Jul 22 2013Jul 25 2013

Publication series

NameACM International Conference Proceeding Series

Conference

ConferenceConference on Extreme Science and Engineering Discovery Environment, XSEDE 2013
Country/TerritoryUnited States
CitySan Diego, CA
Period07/22/1307/25/13

Keywords

  • CyberGIS
  • Exploratory spatial data analysis
  • Flow mapping
  • Flumapper
  • Kernel density estimation

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