The document discusses methods for mapping spatio-temporal data, highlighting advantages such as dynamic representation and interactivity, which enhance understanding. However, it also notes challenges like increased display complexity that may lead to misinterpretation. Examples illustrate varying effectiveness in visualizations of different datasets over time.

1. Methods for Mapping Temporal Data
Aileen Buckley

2. What are the advantages of these maps?
• It is often useful to map spatio-temporal
data dynamically
• Because the varying nature of the data is
intuitively expressed
in the changing display
Storms in the Atlantic, 1995

3. What are the challenges for these maps?
• This intuitive understanding is countered
by the increased complexity of the
display
• This can lead to lack of understanding or
misinterpretation
of the data

4. What we know
• Allow readers a level of interactivity
• The display should transition smoothly
- Show short contiguous time steps
- Show data with a “forced” neighborhood
• Limit the complexity
• Limit the length of time
These visualizations are already complex…

5. Short contiguous time steps
World Boundaries, 400 AD to Present (by Decade)
Poor example

6. Short contiguous time steps
World Boundaries, 400 AD to Present (In Sequence)
Good example

7. Forced neighborhood
U.S. County Population Density, 1800 to Present (by Decade)
Poor example

8. Forced neighborhood
NetCDF Air Temperature, 1948 to 2013
Good example

9. Complexity
Zebra Mussels, 1986 to 2011
Poor example

10. Complexity
Most Populated Cities 1790 - 2000
Good example

11. Length of time
World Earthquakes, 1970 to 1993 (Over Time)
Poor example

12. Good example
Length of time
World Earthquakes, 1970 to 1993 (By Magnitude)