A case study of LSHTM projects that use GIS. Presented by Chris Grundy at a LSHTM Research Data seminar on GIS on 17th March 2014.

1. Improving health worldwide
www.lshtm.ac.uk
GIS: A project by project prospective
Chris Grundy
chris.grundy@lshtm.ac.uk

2. Why project by project
• Each project will have different data
requirements / issues
• Basic rules / considerations
– Size of dataset
– Confidentiality
– Accuracy: time & location
• Examples
– 20mph zones in London
– Use of satellite imagery in surveys

3. 20mph zones in London

4. 20mph zones on road injury
• What effect have implementation of 20mph
zones in London had on road injury between
1986 - 2006
• GIS used
– Locate 20mph zones
– Link road injury to roads
– Build dataset ready for analysis

5. Methods
• Controlled interrupted time series analysis
– Measures the change in the number of casualties on
each road in London from 1987-2006
– Control group is all “outside” roads in London
• 20 years of road casualty data (STATS19)
• Each road defined by year as
– Inside a 20mph zone
– Adjacent to a 20mph zone
– Outside 20mph zones

6. 20mph Zones & road casualties

7. Results
0%
10%
20%
30%
40%
50%
60%
70%
All casualties Killed and seriously
injured (KSI)
Child casualties Child KSI casualties

8. Data considerations
• Large dataset (6 million rows)
– Each stats run took 6 – 10 hours (20 runs)
• Accuracy
– Collisions not always accurate – lots of checks
– During 20 years roads physically changed
• Confidentiality
– Full road injury dataset confidential
– Confidential server too slow to handle size
– Data anonymised for use elsewhere

9. Satellite imagery in surveys

10. GIS and mapping in surveys
• Surveys vital part of public health studies
• GIS widely used
– Planning logistics
– Random selection of household
– Population estimation methods
– Locating house holds for return visits
– Mapping results

11. Using imagery
• Satellite images increasingly available
– Google earth
– Commercial images
– Commissioned images
• Structures visible
• On screen digitizing

12. Am Timan town, Chad
Stratum 1
Stratum 2
Stratum 3

13. Methods: quadrat survey
• Area split into grid
– 50 m2 grid defined
– Existing city street grid
• 15 “quadrats” (blocks) per
stratum
• Visit each structure
• Population = Population density x Area

14. Method: manual structure count
• Structures located by eye
• Type of structure determined by user
– Traditional hut
– Non residential building
• Grid used to ensure
systematic counting
• Count checked
– Missed features / errors

15. Methods: Population estimation
• Using satellite images to estimate population:
Population = n structures x n people / structure
Manual counts Small structure
occupancy survey

16. Methods: random survey
• Select & visit random
structures
• Combine pre-located
structures and GPS
• Coordinates allow structure
to be revisited easily
Survey structure

17. Results: Population estimates
Stratum
Quadrat
Survey
Imagery Method
Manual
Count
Automated
Count
1 14337 12996 12229
(10751 – 19117) (11655 – 14490) (10968 – 13635)
2 16877 16920 16802
(12581 – 22639) (15175 – 18866) (15069 – 18734)
3 25176 16709 16369
(10473 – 60523) (14986 – 18631) (14680 – 18251)
Total 49722 46625 45400
(29 431 – 84003) (41817 – 51987) (40718 – 50620)

18. Trial in different areas
Location Manual
estimate
Reference
Population
Difference
Kutupalong 12 058 11 047 +1011 (+9.2%)
Breidjing 34 896 26 770 +8126 (+30.4%)
Farchana 22 944 19 070 +3874 (+20.3%)
Bambu 7637 5871 +1766 (+30.1%)
Mugunga III 2986 1969 +1017 (+51.7%)
Sherkole 8355 13 958 -5603 (-40.1%)
Shimelba 11 994 13 043 -1049 (-8.0%)
Champs-de-Mars 12 513 23 214 -10 701 (-46.1%)
Delmas 24 20 612 39 349 -18 737 (-47.6%)
Kakuma 88 457 90 457 -2000 (-2.2%)
Bairro Esturro 8940 9523 -583 (-6.1%)

19. Data considerations
• Image data licensed
– Images licenses, not allowed to be shared
– Named users verses number of users
– Getting suitable image within time period an issue
– Not all locations have identifiable structures
• Confidentiality
– Main dataset not confidential
– Confidential survey data stored separately
• Ethics / Population security
– Dangers of mapping at risk populations

20. Summary

21. Don’t forget
• Time: How current is data
• Preparation: Planning is everything
• Disk space
• Confidentiality & ethics: storage & publication
• Share data wherever possible

22. Tip: Map your data
• Check data as it comes in
• Explore your data
• Use maps at every opportunity