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1.4 – Crowd-Sourced Micro-Processing of Mobile Photographs for Health-Related Field-Surveillance
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1.4 – Crowd-Sourced Micro-Processing of Mobile Photographs for Health-Related Field-Surveillance

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Wednesday, October 24, 2012 …

Wednesday, October 24, 2012
Late Breaking Research Abstract Presentations

Michael Tacelosky (Legacy Foundation, USA), Jennifer Pearson (Legacy Foundation, USA), Jennifer Cantrell (Legacy Foundation, USA), Ollie Ganz (Legacy Foundation, USA), Jennifer Kreslake (Legacy Foundation, USA), David Abrams (Legacy Foundation, USA), Donna Vallone (Legacy Foundation, USA), Thomas Kirchner (Legacy Foundation, USA)


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  • People's environment affects their healthParticulate matter, germsFood deserts /Alcohol / Tobacco AdsUbiquitous Photographers Camera-equipped cell phonesGeo-codedThe problem: who's going to look at all those photos?
  • Formative work being done in DC has already provide exterior and interior photos, along with detailed marketing assessment surveys, at all 1000+ tobacco retailers in the city.processing and coding thousands of information rich photos is another challenge that would have presented a prohibitive bottle-neck not long ago. This is now handled with relative ease via web-based crowd-sourcing tools like Amazon Mechanical Turk. What doesn't work:Train Interns Outsource to IndiaWrite a cool image recognition programOur solution: CrowdsourceAmazon's Mechanical Turk Customized (aka “External”) TasksCustomized Interface for Design and Analysis
  • Comprehensive, sustainable surveillance of the national policy-related marketing environment can be achieved with Internet-based tools that take advantage of opportunities to “crowd-source,” which is a method that engages large numbers of people working on small components of a large task.Multi-tiered Micro-taskingBreak problems down into very small unitsOutput of one task is input to anotherValidate with multiple workers per taskWorker Filters:By Region (e.g. US)By Experience (Completed Previous Tasks)By Approval Rate (% of Approved Tasks)
  • 15 ratings each for development of consensus on content and therefore reliability. Also supports canary in the coalmine detection of rare, hard to identify products.
  • Transcript

    • 1. Crowd-sourced Micro-processing of Mobile Phone Photographs for Health-related Field-surveillance M i c h a e l Ta c e l o s k y Jennifer Cantrell, DrPH, Andrew Anesetti-Rothermel, MPH, Jennifer Pearson, PhD, Sarah Cha, MSHP, CHES, Jennifer Kreslake, MSPH, CHES, Ollie Ganz, MSPH, CHES, Donna Vallone, PhD, David Abrams, PhD, Thomas Kirchner, PhD.
    • 2. Point-of-sale Images (thousands of them) 3
    • 3. Distributed human computation 4
    • 4. Fast, Scalable Image Processing 5
    • 5. THANK YOU