Interpolation

Question 1

Inverse distance weighting

Answer 1

more weight to closer points
Good for continuous data that exhibits spatial autocorrelation

Question 2

Interpolation

Answer 2

estimate all the points between provided points to build a continuous surface

Question 3

nearest neighbors

Answer 3

Builds Thiessen polygons and assigns all values in that polygon the value of the point in that polygon

Question 4

Kriging (3 facts)

Answer 4

1. gives you estimates of error
2. models spatial variation
3. weights are based on distance between points and the overall spatial arrangement of the measured points

Question 5

Natural neighbors interpolation

Answer 5

smoothed version of triangulated irregular network

draws from Thiessen triangles

Question 6

Splines

Answer 6

adjusts the trends in values between points to smooth it out into a curve

Question 7

Trend analysis

Answer 7

builds a linear regression to predict the outcome north to south and east to west

Question 8

Triangulated irregular network (TIN)

Answer 8

builds deluaney triangles and estimates the values of the space based on linear/cubic interpolation

Question 9

Thiessen polygons/nearest neighbors

Answer 9

assigns all the points within a polygon the value of the point in that polygon

good for categorical data

Question 10

2 stages for Kriging

Answer 10

1. estimate the structure of the data including autocorrelation to choose the best model
2. predict the unknown values (interpolation)