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.

1. Geospatial Analysis
Innovation in GIS for better
decision making
John Spencer, MEASURE Evaluation
Mark Janko, University of North Carolina
December 10, 2015

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. Context
More tools
• Internet access
• Inexpensive computers/phones
• Excel
• Tableau
• GIS
• On-line tools

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. Global health case studies
Examples of use of geospatial tools in global
health
Innovative tools and use of non-traditional data

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. 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. 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. 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. 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. 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. Global health case studies
HIV estimates at
second subnational
level from national
population-based
surveys
Larmarange, Bendaud
AIDS
28 (Suppl 4):S469-S476

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. 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. 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. 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. 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. 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. 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. Modeling also allows us to
understand uncertainty

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. 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. 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