Difference between revisions of "ISSS608 2017-18 T1 Assign WANG YUCHEN Data Processing"

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[[Image:EPIDEMIC.png|280px]]
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<font size = 5; color="#FFFFFF">Smartpolis Epidemic Outbreak </font>
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[[ISSS608 2017-18 T1 Assign WANG YUCHEN_Data Processing| <font color="#FFFFFF">Data Processing</font>]]
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[[ISSS608 2017-18 T1 Assign WANG YUCHEN_Epidemic Spread| <font color="#FFFFFF">Epidemic Spread</font>]]
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<font size="5">'''To be a Visual Detective'''</font>
 
<font size="5">'''To be a Visual Detective'''</font>
  
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=Geospatial and Microblogging - Characterization of an Epidemic Spread =
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=Geospatial and Text Data - Detecting an Epidemic Spread =
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The given datasests involved maps, weather and population information of smartpolis microblog
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===Datasets===
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characterization of the spread of an epidemic using given maps,
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The given datasets involved map, weather and population information of smartpolis, while the main dataset for our analysis is the microblog dataset, which contains
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geospatial and text data gathered from microblog tweets.  
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geospatial and text data gathered from tweets.
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All of the events in the scenario occurred
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in the fictional city of Vastopolis during the first half of 2011.
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Through mapping the tweets information with geospatial data in the map, we can figure out the symptom of the epidemic. The weather information also helps us detect the infection of the epidemic, whether it is airborne, waterborne,  person-­to­-person, or something else?
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MC1 consisted of text (tweets) which participants needed to
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process to identify the symptoms and details of an epidemic.
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===Process of study===
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There were two different sets of illnesses, a waterborne illness
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* Text explorer of the tweets by date to figure out symptoms and details which characterize the outbreaks
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and an airborne illness. The participants were asked to locate
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* Filter out unrelated tweets and map the informative tweets with geospatial data to trail the spread of epdemic
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and pinpoint the source of the epidemic, to describe the method
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* Drill down the trends of the texts through visualization tools to solve the illnesses
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of transmission of the epidemic, and determine if deployment of
 
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treatment resources outside of the affected area was necessary.
 

Revision as of 22:37, 15 October 2017

EPIDEMIC.png Smartpolis Epidemic Outbreak | Visual Detective

Background

Data Processing

Origin and Epidemic Spread

Epidemic Spread

 


To be a Visual Detective

Geospatial and Text Data - Detecting an Epidemic Spread

Datasets

The given datasets involved map, weather and population information of smartpolis, while the main dataset for our analysis is the microblog dataset, which contains geospatial and text data gathered from tweets.

Through mapping the tweets information with geospatial data in the map, we can figure out the symptom of the epidemic. The weather information also helps us detect the infection of the epidemic, whether it is airborne, waterborne, person-­to­-person, or something else?

Process of study

  • Text explorer of the tweets by date to figure out symptoms and details which characterize the outbreaks
  • Filter out unrelated tweets and map the informative tweets with geospatial data to trail the spread of epdemic
  • Drill down the trends of the texts through visualization tools to solve the illnesses
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