We wanted to create a solution which could predict health risks based on pollution metrics
What it does
AirCheck integrates pollution stats with crowd-sourced symptoms based on: - Location (City, Country) , Pollution metrics(Relative Humidity, Sulfur DiOxide ,Air quality), Occurrence and severity (Mild, Moderate, Severe) of symptoms of allergies and respiratory diseases.
The server database integrates symptoms with location based pollution information. Users are also given an approximate health hazard statistics of the current location or any other requested location.
Build the global user database on health data
Provide analyzed data on the occurance and severity of symptoms in areas of interest.
Provide scrutinized prediction on health risks in particular zones .The prediction algorithm uses NASA's real time WorldView data .
How we built it
Challenges we ran into
Analyzing NASA's worldview imagery
Retrieving location without GPS
Accomplishments that we're proud of
Retrieving Environmental Metrics from NASA's imagery
Finding Correlation between symptoms and pollution statistics
What's next for airCheck
Have substiantial impact on user perspective by using more adept prediction algorithm
Use IBM BlueMix to crowdsource related data from twitter.
This global database can be used for data mining in future researches for finding new correlations between air quality data and public health.
Provide custom-tailored data to users to better inform him about his health conditions from our web-server.