Transcript Document

Missing Spatial Data
Ran TAO
[email protected]
Examples
 Places cannot be reached
 E.g. Mountainous area
 Sample points
 E.g. Air pollution
 Damage of data
 E.g. historical data; falsely delete
Mecklenburg Population Density
How to deal with it
Use data of known places to predict unknown places
 Add hoc methods:
 replacement of the missing data by the mean or median value of
the spatial surface or by a local or regional mean
 discard the missing data altogether and work only with the
observed values.
 Statistical solutions
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Trend-surface models
Spatial filters and regression techniques
Random field models
Kriging interpolation
Example
Here are some sample elevation points from which surfaces were
derived using the three methods
Example: IDW
Done with P =2. Notice how it is not as smooth as Spline. This is
because of the weighting function introduced through P
Example: Spline
•Note how smooth the curves of the terrain are; this is because Spline is
fitting a simply polynomial equation through the points
Example: Kriging
This one is kind of in between—because it fits an equation through
point, but weights it based on probabilities
Theissen
Inverse Distance Weighting
Kriging