Project Sweepers

 

Technical Lead: Bill Gray

Roles: 

• Data cleanup - to comb through the data that does not easily map to the service or should be flagged for evaluation based on the user stories below
• Data analyst - to comb through the resulting data to determine if there are incidents that are occurring and flag the appropriate people in the different offices

 

User stories: 

• What happens when we do not have an effective location match?
• What happens when we get too frequent data reports from a single user, SMS number, etc?
• What happens if the user submits inaccurate/misspelled location information? 
• What happens if one person is submitting reports from disparate locations?

 

Feature concepts:

• Table representing reports that have come in - and missing a location.  Interface to give best guess and up to the sweeper to fill in the blank.
• Verification of spam - how we will id script spam? - need to down-prioritize feeds coming from a particular individual if they are untrusted

 

 

To overcome the challenges/limitations of the service, we will require individuals to watch the data feeds/visualizations for the tweets that are coming from the various states.  Sweepers, with some volunteers to be provided by <TO BE FILLED IN>, will be responsible for diving into the data feeds and gathering information on the incidents that are reported on.

 

Challenge: how can we DM someone if they are not following the Sweeper's twitter account?

Challenge: how can we DM someone that sends a message via other channels (SMS/Mozes, IVR, etc)

 

The overall process is to capture the information geographicly, find out the issue that is being reported on, and then contact the appropriate authorities.