GISP COMPREHENSIVE TEST 2026
QUESTIONS WITH ANSWERS GRADED A+
⩥ Kernel density estimation. Answer: A local interpolation method that
associates each known point with a kernel function in the form of a
bivariate probability density function
⩥ Kriging. Answer: A stochastic interpolation method that assumes that
the spatial variation of an attribute includes a spatially correlated
component
⩥ Local interpolation. Answer: An interpolation method that uses a
sample of known points in estimating an unknown value
⩥ Local polynomial interpolation. Answer: A local interpolation method
that uses a sample of points with known values and a polynomial
equation to estimate the unknown value of a point
⩥ Local regression analysis. Answer: A local interpolation method that
uses the information for each known point to derive a local regression
model. Also called geographically weighted regression analysis
⩥ Nugget. Answer: The semivariance value at the distance 0 in a
semivariogram
,⩥ Ordinary kriging. Answer: A kriging method that assumes the absence
of a drift or trend and focuses on the spatially correlated component
⩥ Partial sill. Answer: The difference between the sill and the nugget in
a semivariogram
⩥ Radial basis functions (RBF). Answer: A diverse group of methods for
spatial interpolation including thin-plate splines, thin-plate splines with
tension, and regularized splines.
⩥ Range. Answer: The distance at which the semivariance starts to level
off in a semivariogram
⩥ Regression model. Answer: A global interpolation method that uses a
number of independent variables to estimate a dependent variable
⩥ Semivariance. Answer: A measure of the degree of spatial dependence
among points used in kriging
⩥ Semivariogram. Answer: A diagram relating the semivariance to the
distance between sample points used in kriging.
, ⩥ Sill. Answer: The semivariance at which the leveling starts in a
semivariogram
⩥ Spatial interpolation. Answer: The process of using points with known
values to estimate unknown values at other points
⩥ Stochastic interpolation. Answer: A spatial interpolation method that
offers assessment of prediction errors with estimated variances
⩥ Thiessen polygons. Answer: A local interpolation method that ensures
that every unsampled point within a polygon is closer to the polygon'&
known point than any other known points. Also called Voronoi polygons
⩥ Thin-plate splines. Answer: A local interpolation method that creates a
surface passing through points with the least possible change in slope at
all points.
⩥ Thin-plate splines with tension. Answer: A variation of thin-plate
splines for spatial interpolation
⩥ Trend surface analysis. Answer: A global interpolation method that
uses points with known values and a polynomial equation to
approximate a surface
QUESTIONS WITH ANSWERS GRADED A+
⩥ Kernel density estimation. Answer: A local interpolation method that
associates each known point with a kernel function in the form of a
bivariate probability density function
⩥ Kriging. Answer: A stochastic interpolation method that assumes that
the spatial variation of an attribute includes a spatially correlated
component
⩥ Local interpolation. Answer: An interpolation method that uses a
sample of known points in estimating an unknown value
⩥ Local polynomial interpolation. Answer: A local interpolation method
that uses a sample of points with known values and a polynomial
equation to estimate the unknown value of a point
⩥ Local regression analysis. Answer: A local interpolation method that
uses the information for each known point to derive a local regression
model. Also called geographically weighted regression analysis
⩥ Nugget. Answer: The semivariance value at the distance 0 in a
semivariogram
,⩥ Ordinary kriging. Answer: A kriging method that assumes the absence
of a drift or trend and focuses on the spatially correlated component
⩥ Partial sill. Answer: The difference between the sill and the nugget in
a semivariogram
⩥ Radial basis functions (RBF). Answer: A diverse group of methods for
spatial interpolation including thin-plate splines, thin-plate splines with
tension, and regularized splines.
⩥ Range. Answer: The distance at which the semivariance starts to level
off in a semivariogram
⩥ Regression model. Answer: A global interpolation method that uses a
number of independent variables to estimate a dependent variable
⩥ Semivariance. Answer: A measure of the degree of spatial dependence
among points used in kriging
⩥ Semivariogram. Answer: A diagram relating the semivariance to the
distance between sample points used in kriging.
, ⩥ Sill. Answer: The semivariance at which the leveling starts in a
semivariogram
⩥ Spatial interpolation. Answer: The process of using points with known
values to estimate unknown values at other points
⩥ Stochastic interpolation. Answer: A spatial interpolation method that
offers assessment of prediction errors with estimated variances
⩥ Thiessen polygons. Answer: A local interpolation method that ensures
that every unsampled point within a polygon is closer to the polygon'&
known point than any other known points. Also called Voronoi polygons
⩥ Thin-plate splines. Answer: A local interpolation method that creates a
surface passing through points with the least possible change in slope at
all points.
⩥ Thin-plate splines with tension. Answer: A variation of thin-plate
splines for spatial interpolation
⩥ Trend surface analysis. Answer: A global interpolation method that
uses points with known values and a polynomial equation to
approximate a surface
