Kriging, Inverse distance weighting and spatial autocorrelation

Question 1

Inverse distance weighting

Answer 1

Assumes that each measured point has a local influence that diminishes with distance. It gives greater weights to points closest to the prediction location, and the weights diminish as a function of distance

Question 2

Spatial autocorrelation

Answer 2

Related to Tobler’s First law of geography.
-States that pairs of subjects that are close to each other are more likely to have values that are more similar, and pairs of subjects far apart from each other are more likely to have values that are less similar.

Question 3

Spatial interpolation vs. Spatial Prediction

Answer 3

Spatial Interpolation- estimating the attribute values of locations that are within the range of available data using known data values.

Question 4

Modifiable Areal Unit Problem

Answer 4

MAUP- a source of statistical bias that can significantly impact the results of statistical hypothesis tests

Question 5

Semivariance

Answer 5

A measure of the degree of spatial dependence between samples.
-simply half the variance of the differences between all possible points spaced a constant distance apart.

Question 6

Spatial heterogeneity

Answer 6

A property of a spatial process whose mean (or “intensity”) varies from point to point.

Question 7

Tobler’s first law of geography

Answer 7

“Everything is related to everything else, but near things are more related than distant things.”

Question 8

Sampling Patterns: Positive spatial autocorrelation, Negative spatial autocorrelation, and Zero autocorrelation

Answer 8

• PSA- features that are similar in location are also similar in attributes
• NSA- exists when features that are close together in space tend to be more dissimilar in attributes than features that are further apart
• ZA- occurs when attributes are independent of location = random.