Difference between revisions of "Linog"

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* direct XML parsing of seimic data
 
* direct XML parsing of seimic data
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* fail-over data feeds
 
* iOS version
 
* iOS version
 
* interactive infographics
 
* interactive infographics
* aggregation of crowd-sourced reports from social media using local keywords
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* aggregation of crowd-sourced reports from social media using keywords from local languages
* rescue beacon system that enables Linog users to register their last know location is an area where there's intense earthquake
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* rescue beacon system that enables Linog users to register their last known locations in an area where there's intense earthquake
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* user-fed seimic readings using phone's accelorometer
  
 
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[[Category:DILC Projects]]
 
[[Category:DILC Projects]]

Latest revision as of 07:33, 29 May 2014

Linog icon.png

Linog collects information on seismic events that took place in your area (100 km radius) in the last 24 hrs. Coverage is limited to Philippine territory. For this app to work, you need internet connection and your phone's location services activated.

Linog logo.jpg

This app aims to be a platform for learning about risks associated with earthquakes. Linog (Visayan for "earthquake") is produced by DILC (Diliman Interactive Learning Center, University of the Philippines), in cooperation with PHILVOCS (Philippine Institute of Volcanology and Seismology) and ASTI (Advanced Science and Technology Institute).


Shortcut to this page: dilc.info/linog


Linog is available on Google Play

Google play icon.png





Development Team


  • Mark Angelo Yadao (Android)
  • Dan Villareal
  • Darwin Gonzales
  • Kevin Aganus (graphics)
  • Peter A. Sy (consultant)



To Dos


  • direct XML parsing of seimic data
  • fail-over data feeds
  • iOS version
  • interactive infographics
  • aggregation of crowd-sourced reports from social media using keywords from local languages
  • rescue beacon system that enables Linog users to register their last known locations in an area where there's intense earthquake
  • user-fed seimic readings using phone's accelorometer