1. GeoVisualisation of health data Prof Clive Sabel Geography, CLES and European Centre for Environment & Human Health

2. Spatial data mining case-studies - Childhood Obesity in Devon - Neurological disease in Finland - Road Traffic Accidents in New Zealand

3. Childhood Obesity in Devon

5. US election 2004 US election 2008 ‘Distorted’ maps: Cartograms

6. HIV Prevalance

7. Physicians working

8. Neurological disease in Finland

9. Cases MND 1000 cases 3113 case residences But population naturally clusters in cities So search for ‘excess’ over and above the background rate

10. Kernel (Density) Estimates of All places of residence Statistical Significance tested by Monte-Carlo Simulation Note large peak in SE Finland Residential Clustering of Motor Neurone Disease Finland

11. Space-Time Visualisation Residential Migration Animation Space: Finland Time: 1965 – 1990 Illustrates Spatial and Temporal variation Note: persistent dark areas in SE In SW, at varying times, areas switch from high to low rates of disease

12. MND Individual Migration Histories Same data as before, but now showing individual migration trajectories. The larger densities of colour identify the larger cities

13. Road Traffic Accidents in New Zealand

14. Christchurch, NZ City Centre Also a Kernel density estimate of RTAs. Compare model results (black polygon) with observations (red-blue image) Note more accidents than modelled in the city centre, and around junctions

16. Left: Temporal analysis of all road accidents - accidents reducing over time, due to increased safety measures introduced. Right: Temporal analysis of just accidents involving cyclists - accidents increasing in the circled area, why?

17. Drive Safely!