Demo session at the Canadian Geomatics Conference. Toronto, Canada. 17 May 2011. The use of geographic visualization (geovisualization) has increased over recent years with the prevalence of digital earths (e.g., Google Earth). We investigate how well applications of digital earths employ geovisualization principles using the example of health data. We utilized trauma data from Cape Town, South Africa. We created a number of geovisualizations at both the coarse level and fine grained level of resolution. These utilized various application programming interfaces (APIs), Google Earth, and scripting languages. We based our applications on evaluation of geovisualization design principles and extant examples in health and epidemiology

1. The use of geovisualization to
public health, in the context of
open source applications and
digital earths:
an effective representation?
Ana Brandusescu, Renee Sieber, Nadine Schuurman

2. Overview
• background information
• existing geovisualization in epidemiology
• relevance
• data
• methods (based on geoviz criteria)
• results
• limitations
• conclusion

3. Background and Relevance
• what is geovisualization (geoviz)?
• increased popularity of digital earths
• geoviz and health
• health data digital earths Web 2.0
• mashups in epidemiology
• traditional GIS vs new platforms
• innovative spatial data interaction

4. Existing geoviz applications in
epidemiology
•Avian influenza time series map
(Butler)
• Dengue surveillance system
(ArcGIS and Google Earth) (Chang et al.)
• Batch GeoCode and Mapalist
(Cinnamon & Schuurman)
• Pennsylvania Cancer Atlas, Health Map
and BioDiaspora, ESTAT, Health
GeoJunction

5. Data
• coarse and sensitive data from Cape Town,
South Africa
• 4,269 injuries
• 64% of data set
• top 20 aggregate injury prone suburbs

6. Methods
• Jamstec
• Google Earth (GE) Graph
(Ricardo Sgrillo)
• Google SketchUp
• geoviz principles:
interoperability
 modifiability and collaborative work
 cost
 interactivity
 quality of application user interfaces

7. GE Graph, Google Charts API and
Google Docs

9. Why GE Graph?
• high interoperability levels
• free
• highly modifiable and flexible
• polygon and point data
• 2D and 3D visualizations
• animation rendering
• time or 4D representation

10. Google SketchUp

12. Why Google SketchUp?
• connections between environmental form
and traumas
• Google Streetview and geocoded
photographs
• Google’s 3D warehouse
• 3D vs 2.5D
Limitations:
• number of objects
• problems with some computers and GE

13. More results
• data accuracy and data confidence
• misrepresentation
• well-suited for resource-poor environments
• learning curve
• time
• confidentiality issues in the Google cloud
• uncertainties in development vs
uncertainties in data

14. Conclusion
• an effective representation?
• data and misrepresentation
• we have to start somewhere
• importance of variety
• the future of geoviz criteria and public
health data

15. Thank You!
• GEOIDE Project 54
• Renee Sieber
• Nadine Schuurman
• Prestige Makanga