GIS: A project by project prospective
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Transcript of GIS: A project by project prospective
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Improving health worldwide
www.lshtm.ac.uk
GIS: A project by project prospective
Chris Grundy
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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
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20mph zones in London
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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
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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
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20mph Zones & road casualties
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Results
All casualties Killed and seriously injured (KSI)
Child casualties Child KSI casualties0%
10%
20%
30%
40%
50%
60%
70%
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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
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Satellite imagery in surveys
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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
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Using imagery
• Satellite images increasingly available– Google earth– Commercial images– Commissioned images
• Structures visible
• On screen digitizing
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Am Timan town, Chad
Stratum 1
Stratum 2
Stratum 3
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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
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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
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Methods: Population estimation
• Using satellite images to estimate population:
Population = n structures x n people / structure
Manual counts Small structure occupancy survey
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Methods: random survey
• Select & visit random
structures
• Combine pre-located
structures and GPS
• Coordinates allow structure
to be revisited easily
Survey structure
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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)
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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%)
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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
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Summary
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Don’t forget
• Time: How current is data
• Preparation: Planning is everything
• Disk space
• Confidentiality & ethics: storage & publication
• Share data wherever possible
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Tip: Map your data
• Check data as it comes in
• Explore your data
• Use maps at every opportunity