title: Using geographical information systems and cartograms as a health service quality improvement tool.
abstract: BACKGROUND
  Disease prevalence can be spatially analysed to provide support for service implementation and health care planning, these analyses often display geographic variation. A key challenge is to communicate these results to decision makers, with variable levels of Geographic Information Systems (GIS) knowledge, in a way that represents the data and allows for comprehension. The present research describes the combination of established GIS methods and software tools to produce a novel technique of visualising disease admissions and to help prevent misinterpretation of data and less optimal decision making. The aim of this paper is to provide a tool that supports the ability of decision makers and service teams within health care settings to develop services more efficiently and better cater to the population; this tool has the advantage of information on the position of populations, the size of populations and the severity of disease.
  
  
  METHODS
  A standard choropleth of the study region, London, is used to visualise total emergency admission values for Chronic Obstructive Pulmonary Disease and bronchiectasis using ESRI's ArcGIS software. Population estimates of the Lower Super Output Areas (LSOAs) are then used with the ScapeToad cartogram software tool, with the aim of visualising geography at uniform population density. An interpolation surface, in this case ArcGIS' spline tool, allows the creation of a smooth surface over the LSOA centroids for admission values on both standard and cartogram geographies. The final product of this research is the novel Cartogram Interpolation Surface (CartIS).
  
  
  RESULTS
  The method provides a series of outputs culminating in the CartIS, applying an interpolation surface to a uniform population density. The cartogram effectively equalises the population density to remove visual bias from areas with a smaller population, while maintaining contiguous borders. CartIS decreases the number of extreme positive values not present in the underlying data as can be found in interpolation surfaces.
  
  
  CONCLUSIONS
  This methodology provides a technique for combining simple GIS tools to create a novel output, CartIS, in a health service context with the key aim of improving visualisation communication techniques which highlight variation in small scale geographies across large regions. CartIS more faithfully represents the data than interpolation, and visually highlights areas of extreme value more than cartograms, when either is used in isolation.
authors.affiliation: NIHR CLAHRC for Northwest London, Imperial College London, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom. Electronic address: d.lovett@imperial.ac.uk.
authors.forename: Derryn A
authors.initials: DA
authors.lastname: Lovett
authors.affiliation: NIHR CLAHRC for Northwest London, Imperial College London, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom.
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Poots
authors.affiliation: NIHR CLAHRC for Northwest London, Imperial College London, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom.
authors.forename: Jake T C
authors.initials: JT
authors.lastname: Clements
authors.affiliation: NIHR CLAHRC for Northwest London, Imperial College London, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom.
authors.forename: Stuart A
authors.initials: SA
authors.lastname: Green
authors.affiliation: Department of Primary Care and Public Health, Imperial College London, Reynolds Building, St Dunstan's Road, London W6 8RP, United Kingdom.
authors.forename: Edgar
authors.initials: E
authors.lastname: Samarasundera
authors.affiliation: NIHR CLAHRC for Northwest London, Imperial College London, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom.
authors.forename: Derek
authors.initials: D
authors.lastname: Bell
mesh_terms: D000397:Air Pollution / Q000009:adverse effects; D001982:Bronchial Diseases / Q000453:epidemiology / Q000517:prevention & control; D004784:Environmental Monitoring / Q000379:methods; D040362:Geographic Information Systems; D062305:Geographic Mapping; D006801:Humans; D008131:London / Q000453:epidemiology; D015233:Models, Statistical; D011156:Population Density; D015995:Prevalence; D011787:Quality of Health Care
keywords: Cartography; Health Services; Visualisation