GIS: A project by project prospective
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GIS: A project by project prospective

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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.

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.

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    GIS: A project by project prospective GIS: A project by project prospective Presentation Transcript

    • Improving health worldwide www.lshtm.ac.uk GIS: A project by project prospective Chris Grundy chris.grundy@lshtm.ac.uk
    • 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
    • 20mph zones in London
    • 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
    • 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
    • 20mph Zones & road casualties
    • Results 0% 10% 20% 30% 40% 50% 60% 70% All casualties Killed and seriously injured (KSI) Child casualties Child KSI casualties
    • 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
    • Satellite imagery in surveys
    • 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
    • Using imagery • Satellite images increasingly available – Google earth – Commercial images – Commissioned images • Structures visible • On screen digitizing
    • Am Timan town, Chad Stratum 1 Stratum 2 Stratum 3
    • 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
    • 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
    • Methods: Population estimation • Using satellite images to estimate population: Population = n structures x n people / structure Manual counts Small structure occupancy survey
    • Methods: random survey • Select & visit random structures • Combine pre-located structures and GPS • Coordinates allow structure to be revisited easily Survey structure
    • 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)
    • 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%)
    • 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
    • Summary
    • Don’t forget • Time: How current is data • Preparation: Planning is everything • Disk space • Confidentiality & ethics: storage & publication • Share data wherever possible
    • Tip: Map your data • Check data as it comes in • Explore your data • Use maps at every opportunity