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Geospatial Analysis: Innovation in GIS for Better Decision Making

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Discussion led by John Spencer and Mark Janko. This webinar shared new techniques in geospatial analysis and how they have the potential to transform data-informed decision making.

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Geospatial Analysis: Innovation in GIS for Better Decision Making

  1. 1. Geospatial Analysis Innovation in GIS for better decision making John Spencer, MEASURE Evaluation Mark Janko, University of North Carolina December 10, 2015
  2. 2. Context • Improvements in national health information systems • Different types of data – e.g. cell phone data • Geo coded data • Open data More data
  3. 3. Context More tools • Internet access • Inexpensive computers/phones • Excel • Tableau • GIS • On-line tools
  4. 4. What does this mean? • Better understanding of maps & how to read them • Higher demand for geospatial analysis • Ability to answer more robust questions • Opportunity for increased use in decision making Increased access to data & tools
  5. 5. Global health case studies Examples of use of geospatial tools in global health Innovative tools and use of non-traditional data
  6. 6. Global health case studies Using Mobile Phone Data to Predict the Spatial Spread of Cholera Bengtsson, Gaudart, et al Scientific Reports March 2015
  7. 7. Mobile Phone and Cholera Spread • Used mobile phone data to test whether it’s possible to predict early spatial evolution of the 2010 Haiti Cholera epidemic • Daily case reports from health facilities and compared to phone data from 2.9 million users of Digicel Haiti • Key finding • Risk of epidemic onset in a given area and initial intensity of outbreaks could have been anticipated using case reports and mobility patterns taken using mobile phone data • Potential tool for containment of measles
  8. 8. Global health case studies Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data Wesolowski, Metcalf, et al PNAS Vol 112 no. 35 Sept 1 2015
  9. 9. Rubella transmission and mobile phones • Used mobile phone data to map human population fluxes across provinces in Kenya • Compared to rubella transmission to look at effects of human migration • Key finding • Were able to identify what areas were at risk during which times of year • Can be used for targeted vaccination campaigns
  10. 10. Global health case studies Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and birth Tatem, Campbell, et al International Journal of Health Geographics 2014, 13:2
  11. 11. Modeling MCH spatially • Detailed subnational maps using multiple sources of freely available data to estimate number of pregnancies and live births. • Created 100 x 100 m grid-cells of estimates and estimates of proximities to health facilities. • Key findings • Some data limitations but provide subnational estimates that can be useful for decision making • Important to be aware of data sources and inputs.
  12. 12. Global health case studies HIV estimates at second subnational level from national population-based surveys Larmarange, Bendaud AIDS 28 (Suppl 4):S469-S476
  13. 13. Subnational HIV Estimates • DHS data is not useful for estimating HIV estimates below first subnational level • Authors have developed a method that estimates HIV prevalence at second subnational level using DHS • Key finding • Kernel density estimation has potential as a method for estimating prevalence at second subnational level • Works better in some countries than others, still needs some refinement
  14. 14. Global health case studies Making the most of a brave new world: Opportunities and considerations for using Twitter as a public health monitoring tool. Stoove, Pedrana Preventive Medicine 63 (2014) 109-111
  15. 15. Use of Twitter for public health • Tweets related to HIV collected and then mapped based on the location of the tweeter. • Compared withAIDSVu.org and saw a significant positive relationship between HIV tweets and HIV prevalence • Key finding • Big data from social media can be used for remote monitoring and surveillance of HIV
  16. 16. Case study in integrating innovative data and modeling to support decision making Mark Jenko Shelah Bloom John Spencer MEASURE Evaluation University of North Carolina at Chapel Hill IPV in Rwanda: Photo by Kresta King: https://flic.kr/p/nMMip
  17. 17. Data 2010 Rwanda Demographic and Health survey: 492 clusters, ~3000 women participating in domestic violence module Armed Conflict and Location of Event Database (ACLED). Location and time of conflict events throughout our study area (Rwanda and immediate surrounding area.
  18. 18. Analysis and results Question of interest: Does effect of violent conflict on IPV vary across Rwanda. Method of choice: Bayesian hierarchical modeling Red areas indicate areas where exposure leads to higher than average risk; Blue areas indicate areas where exposure leads to lower than average risk
  19. 19. At a finer geographic scale Instead of districts (coarse geographic units), what if we imagine a continuous surface of the effect? Left shows the estimates of the effect from previous slide. Right shows effect if allowed to vary continuously across Rwanda.
  20. 20. Modeling also allows us to understand uncertainty
  21. 21. Applicability of approach 1) If planning interventions to address specific risk factors, it is useful to know where those risk factors exhibit the strongest effect. - Hopefully leads to more efficient use of resources 2) Also useful to consider what scale you want your information to be at. Do you only need information at coarser geographic scales (like provinces?), or do you want to understand heterogeneity at finer scales (like within provinces). 3) Can extend these approaches to other settings: HIV, malaria, TB. If data are spatially referenced, easy to merge and leverage datasets that otherwise couldn’t be combined. - Hopefully leads to more efficient use of resources
  22. 22. Conclusion • As always there is a symbiosis between data, analysis tools and decision makers. • In the past global health has had a reliance on specialized data from national surveys or other specialized data collection efforts • That’s changing with the growth of national health systems and emerging data sets • Global health professionals have more data and more tools available than ever before
  23. 23. MEASURE Evaluation is funded by the U.S. Agency for International Development (USAID) under terms of Cooperative Agreement AID-OAA-L-14-00004 and implemented by the Carolina Population Center, University of North Carolina at Chapel Hill in partnership with ICF International, John Snow, Inc., Management Sciences for Health, Palladium Group, and Tulane University. The views expressed in this presentation do not necessarily reflect the views of USAID or the United States government. www.measureevaluation.org

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