GeoVisualisation of health data

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

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

11. Space-TimeVisualisation • 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!