1. Performing Polygon-to-Polygon Predictions Using Areal Interpolation
Eric Krause
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2. Obesity surface and error surface
Areal Interpolation
Obesity by school zone
Obesity surface and error surface
Obesity by census block
Predict data in a different geometry
School zones to census tracts
Model and fill-in missing data
-Polygonal data often a mixture of administrative units (eg, some census blocks, some cities, some postal codes); Areal Interpolation can cast data onto same administrative units (eg, census blocks)
-Fill in missing data
-Create statistically accurate density surfaces
-Provides margins of error for predictions
What it does. (cast data)
Uses geostatistical methods.
Important for many analyses that need all of your data to be in the same geometry.
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3. Workflow for predicting to new polygons

4. Types of Areal Interpolation
Average (Gaussian) Areal Interpolation
Example:
Interpolate radiation levels from measurements averaged in polygons
Median age, average household income
Takes Gaussian data averaged over polygons
Variable of interest
Interpolate to predict value of Gaussian variable at individual point locations

5. Types of Areal Interpolation
Rate (Binomial) Areal Interpolation
Example:
Interpolate proportion of lung cancer cases
Takes two input fields:
Number of individuals randomly sampled from the population of a polygon
Number of individuals with a particular characteristic
Variable of interest
Proportion of individuals with the characteristic

6. Types of Areal Interpolation
Count (Overdispersed Poisson) Areal Interpolation
Example:
Counts of whales over polygons in the ocean
Takes two input fields:
Number of instances of a certain event counted within a polygon
Amount of time spent counting within the polygon
Variable of interest
Interpolate on density/risk of making an observation at a given location

7. Demo
Areal Interpolation
Eric Krause
Type Presentation Name Here