GIS Spatial Statistics 3

Question 1

Ordinary Kriging differs from Simple Kriging because Ordinary Kriging:

Accepted Answer

Assumes an unknown, locally varying mean estimated from nearby data

Answer

Ordinary Kriging treats the mean as unknown and locally stationary, making it more widely applicable than Simple Kriging which assumes a known constant mean.

Question 2

In a variogram model, the nugget effect represents:

Accepted Answer

Microscale variation or measurement error at distances approaching zero

Answer

The nugget is the y-intercept of the variogram model, capturing measurement error and spatial variation occurring at scales finer than the sampling interval.

Question 3

The 'range' of a variogram model is defined as:

Accepted Answer

The distance beyond which pairs of points show no spatial autocorrelation

Answer

The range is the lag distance at which the variogram levels off at the sill, meaning that points separated by more than this distance are spatially independent.

Question 4

Geographically Weighted Regression (GWR) differs from ordinary least squares (OLS) regression in that GWR:

Accepted Answer

Produces regression coefficients that vary spatially across the study area

Answer

GWR fits a separate regression model at each location using nearby observations, allowing the relationships between variables to vary spatially rather than being globally constant.

Question 5

The Modifiable Areal Unit Problem (MAUP) in spatial analysis describes:

Accepted Answer

The sensitivity of statistical results to the choice of spatial units used for aggregation

Answer

MAUP occurs because the same underlying data can produce different statistical results depending on how areal units are defined, including both the scale effect and the zoning effect.

GIS - Geographic Information System Practice Test

GIS - Geographic Information System Practice Test

GIS Spatial Statistics 3