Presentation by Chris Grundy of LSHTM which describes his use of satellite images for population estimation and surveys, as well as mapping work performed by the online mapping community and NGOs to improve crowd sourced mapping data.

1. Crowd sourcing and high
resolution satellite
imagery in public health
Chris Grundy
chris.grundy@lshtm.ac.uk
Improving health worldwide
www.lshtm.ac.uk

2. Screen grab of Google maps around LSHTM

3. Screen grab of Google maps in Tanzania

4. Crowd source mapping
Using people around the world to collect
and map features of interest into a
central location
• OpenStreetMap (OSM) & HOT
• Missing maps

5. Haiti earthquake 2010

6. Ebola: BRC online ebola map
Source: simonbjohnson.github.io

7. Benefits of OpenStreetMap
• Free, simple software to map area
• Shared workload
• Speed
• Meeting “open data” requirements

8. Satellite imagery
• Increasing in resolution
– Very high resolution imagery (VHR) now 30cm
• Costs reducing – free in emergencies
• Widely available

9. It is only with local knowledge and
previous experience that we can fully
generate datasets from satellite images.

10. What are the features in this image

12. Estimating populations using
satellite images

13. Am Timan, Chad, 2012
Stratum 1
Stratum 2
Stratum 3

14. Manual structure count
• Structures located by eye
• Type of structure determined by user
– Traditional hut
– Small building
– Large building
• Grid used to ensure
systematic counting
• Count checked
– Missed features / errors

15. Population estimates

16. Experience and being systematic are
vital when producing dataset from
satellite images.

18. Good image

19. Poor image

20. Dispersed population

21. Landing site

22. Population Density
Area
X
Population
density

23. Example of sensitivity analysis
Density 1 Density 2 Variable
Uganda 370,803 311,812 316,301
Kenya 152,128 138,575 139,767
Tanzania 555,177 429,689 473,575
Total 1,078,108 880,076 929,643
Density 1: 33,874 people per km
Density 2: 28,895 people per km
High density villages: 35,598 people per km
Low density Villages: 19,533 people per km

24. How to avoid main problems
• Know your software
• Experience counts
• Factor problems into proposal
• Two heads are better than one
• Be systematic
• Validate the method

25. Thank you.