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UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data
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UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data

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  • 1. Reporting Data
    Eduardo Jezierski
    (Engineering)not a doctor!!
    edjez@instedd.org
  • 2. Objectives
    Reporting and collecting data:
    Collecting, balancing structured vs. un-structured (and semi-structured), formats, level of detail, tools for collecting, storing and visualizing
    Learning Objectives:
    Explain the difference between structured, semi-structured and un-structured data
    Design a simple structured data reporting format
    Understand the options for collecting, parsing, storing and analyzing and sharing structured data
  • 3. External data
    Information is power
    Relevance & Targeting
    Aggregation, Sharing, Authorizing
    Rapid Feedback
    Alerting
    Early analytics
    Getting information
  • 4.
  • 5. Example Technologies
    Desktop
    Epiinfo
    Groove
    Mobile
    Open Data Kit
    Epicollect
    Java ROSA
    Pendragon
    USSD
    Digital Pen
  • Emerging alternatives
    USSD
    Unstructured Supplementary Service Data
    Simple to use
    Simple to report structured information
    Requires collaboration with all phone companies
    Digital Pen
    Simple to use
    Simple to report much information
    Ongoing cost of ‘paper’/forms
    Requires separate mobile
  • 17. Why is SMS popular
    Low cost
    Needs little signal (1 bar enough)
    Uses little power
    Works on existing equipment
    Scales from few users to nationwide
  • 18. Ways of sharing information
    UNSTRUCTURED
    “Hello everyone we have meeting tomorrow”
    “We are dealing with cholera outbreak will call you later”
    SEMI-STRUCTURED
    “at Ratchaburi, we are seeing Cholera URGENT”
    “H5N1 Birds:200 should we call PHD?”
    STRUCTURED
    “H5N1, Birds:200, Lab: No, FollowUp: no”
    E.g TURTLE standard
    Simple, easy, flexible
    Simple, requires FEATURE EXTRACTION, some training
    Complex for human entry, hard to learn and to get right
  • 19. Feature Extraction
    Unstructured Data
    Feature Extraction
    Structured Data
    SMS Messages
    Time
    Data Records
    Algorithms & Databases
    Voice/Radio Calls
    Place
    Relationships
    Pictures and videos
    Person
    Metadata
    Event
    Experts
    Organization
    Sensor Readings
    Trustworthiness
    Image Recognition
    Crowds
    Closed Captioning
    Sensors
    Face Recognition
    Calibration
  • 20. SMS Syntax
  • 21.
  • 22. Feature Extraction Example: Places
    “At Stung Treng, seeing Cholera”
    Lat; Long
    1) FIND Feature
    2) Associate Metadata
    Stung Treng= Lat; Long
    OPEN DATABASES tend to be the richest sources of local data & provide a strong platform
    Google, Yahoo Geocoders
    Open Street Maps
    Your own database e.g. PCODES
    Humans are excellent at extracting features! Unless you need real-time; large volume geocoding, crowd sourcing is an excellent option
  • 23. A Haitian with a need sends an SMS to the 4636 shortcode
    The SMS goes through Nuntium and then onto Emergency Information System
    A Haitian volunteer or staff and translates, tags, geocodes
    The organized information is then dispatched to response or added to reports
  • 24. 27 letters
    8 buttons
  • 25. 50+ letters
    8 buttons
  • 26. New Project…
    syntax
    Simplify your users’ experience
    Machines adapt to the users
  • 27. Tombodu (Sierra Leone) traditional registration
  • 28.
  • 29.
  • 30. Other Considerations
    Scalability
    Logical vs Physical data paths
    Cloud Computing
    User Experience & Design
  • 31. Scaling & Reducing costs of SMS
    ?
    ?
    Operator Collaboration
    • Per country
    • 32. Per company
    High Scale
    Lowest Costs
    Long negotiations
    Complex connection
    Local Gateway
    • USB Modem
    • 33. Phone via USB
    Easy to get started
    No internet required
    Slow (1msg/6 second)
    Unreliable- Support
    Virtual Operators
    • Skype
    • 34. International #
    Easy to get started
    High Scale
    Spotty coverage
    Blocked in some countries
    Cost/msg
  • 35. Logical vs Physical data paths
    Pattern: Digitize existing reporting protocols
    Antipattern: implement in digital form the physical path of paper
    Complex-Expensive-Unreliable-Less Secure
  • 36. Cloud Computing
    “Cloud Computing”
    Not “IF”,
    but “HOW to do cloud computing”
    • Standard data download
    • 40. Can delete on demand
    • 41. Ownership?
    • 42. Privacy?
    • 43. External dependency?
  • User Experience Tips
    “Interact with the world through a 160 character browser”
    Context & Goals
    Wizard of Oz
    Start Small, Start simple
    Log all messages for retrospective, rinse, repeat
    SMS Challenges
    Forgiving Empowering
  • 44. Appropriate design is best done by locals
    Share your skills and get out of the way!
  • 45. Summary
    Data collection is only one link in the chain
    Simplicity and usability are key
    Unstructured and semi-structured data are a good balance of data quality and usability
  • 46. Thank You!
    Eduardo Jezierski
    (Engineering)not a doctor!!
    edjez@instedd.org
    @edjez