UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data
•Als PPTX, PDF herunterladen•
0 gefällt mir•462 views
Empfohlen
Weitere ähnliche Inhalte
Ähnlich wie UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data
Ähnlich wie UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data (20)
Mehr von Eduardo Jezierski
Mehr von Eduardo Jezierski (13)
Kürzlich hochgeladen
Kürzlich hochgeladen (20)
UW InSTEDD Class: Experiences from the field: Reporting And Collecting Data
- 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
- 7. Google Spreadsheets & Form
- 9. SMS
- 10. Geochat
- 11. RapidSMS
- 12. Frontline SMS
- 13. Analysis
- 14. SPSS
- 15. STATA
- 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
- 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
- 30. Other Considerations Scalability Logical vs Physical data paths Cloud Computing User Experience & Design
- 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
- 37. Secure
- 38. Scalable
- 40. Can delete on demand
- 41. Ownership?
- 42. Privacy?
- 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