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A SPATIAL METHODOLOGY FOR THE ESTIMATION

OF HOUSING PRICE IN ALBACETE.

Matías Gámez Martínez y José María Montero Lorenzo

Matías Gámez Martínez es profesor de Estadística en la Facultad de Ciencias Económicas

y Empresariales de Albacete (e-mail: [email protected]).

José María Montero Lorenzo es Catedrático de Estadística en la Facultad de Ciencias

Jurídicas y Sociales de Toledo (e-mail: [email protected]).

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A SPATIAL METHODOLOGY FOR THE ESTIMATION

OF HOUSING PRICE IN ALBACETE.

ABSTRACT.

The aim of this paper is to analyse the spatial behaviour of the free housing price in

Albacete. We decided to write it because of the socieconomic importance of the housing

subsector in the local, regional and national economy and its implications for Housing Policy.

To achieve this aim, we have used the models and estimators imported from Geology,

called kriging. In the geostatistics bibliography, the theoretical methodology is precise, since it

estimates a spatial process in a point or region in space as a linear combination of a part of the

spatial observations or all of them. In order to do this, it is neccesary to know the structure of

spatial dependence of the process which is shown in the variogram and, assuming that the process

verifies second order or intrinsic stationarity, then the ordinary or the universal kriging estimator

can be calculated respectively.

Before applying this procedure to study the housing price in Albacete, the factors which

settle their price are analysed (size, age, quality and with or without garage). By deleting such

effects and by reducing all the observations to a “class of equivalent housing”, four methods of

modelization and estimation are used: universal kriging and ordinary kriging on the residuals of

a generalized additive model for the point estimation, and universal kriging and median polish

kriging for the block estimation. We select the last one as the best one to model the spatial

behaviour of the housing price in Albacete.

Keywords: Kriging prediction, spatial model, housing price.

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1.- INTRODUCTION.

The importance of geographic space and its incorporation to economic analysis, both in

theoretical and empirical studies, is based on two main reasons:

- It is the natural support upon which a large of regional economic variables are measured.

- Its influence on these variables, whose values show a spatial pattern of behaviour.

Traditionally, spatial distribution has not been taken into account in regional economic

analysis. However, economic variables in time have been more studied, perhaps removing spatial

study due to its greater complexity (countless ways in space against only one way in time) and the

lack of specific computer programs.

Nowadays, both obstacles are being partially mitigated. Firstly by publication of studies

more rigorous and homogeneous, the efforts accomplished by Matheron (1970), Cliff and Ord

(1981), Anselin (1988, 1995) and Cressie (1993), who have carried Spatial Statistic taking entity

as a knowledge area. Second by the appearance of statistics computer programs (S-plus,

Variowin, etc.) which make working in Spatial Statistics easier. Finally, by the development of

Geographical Information Systems (G.I.S), which constitutes a powerful tool for the analysis of

georeferenced variables.

The aim of this paper is to model the free housing price in Albacete from a spatial

perspective, because it seems reasonable to consider that specific location of housing is

influenced, among other factors, by adjacent housing prices (spatial diffusion phenomenon). For

this purpose, we will use spatial linear models and the Best Linear Unbiased Estimators (BLUE),

called kriging estimators on Geostatistic field (Krige (1951), Goldberger (1962), Matheron

(1952,1963)).

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2.- SPATIAL DATA MODEL.

A spatial data collection can be considered as a stochastic process or random function

realization

where u is a location in D domain, contained in a d dimension space (generally 1,2 or 3). For each

location u, y(u) is a random variable. To carry out this analysis, we must model the spatial

stochastic process y(u). Generally, inference is not possible based on only one stochastic process

realization y(u). Therefore we will set some restrictions in terms of stationarity hypothesis. We

will assume a certain regular spatial behaviour for the first moments process or their increments.

We will suppose that, for each u, first and second order moment exist and they are finite,

then we have that E[y(u)] and var[y(u)]exist and y(u) can be separated into:

where m(u) is a function that represents y(u) mean

E[y(u)] = m(u),

and e(u) is a stochastic error process with

E[e(u)] = 0.

In particular, e(u) = y(u) - m(u). For our purposes, we must model m(u) and e(u).

More formally, if we assume a linear structure for mean m(u), we obtain what is known in

Geostatistic as “universal kriging model”. The existence of p known functions, x1(u),..., xp(u) is

supposed in such a way that

for certain unknown fixed parameters ß1,...,ßp.

Install Equation Editor and double-click here to view equation.



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We can distinguish two particular cases:

a) Ordinary kriging in which the mean of the process is constant.

b) Simple kriging in which the mean of the process is constant and known.

We will obtain the most general estimator (Universal kriging). Furthermore, some small

changes will provide us with the estimator in the other cases.

For modelling error process, we will be especially interested in second order error

properties. The covariance function is defined as:

Using the following notation

andInstall Equation Editor and double-click here to view equation.

the universal kriging model can be written on matrix form:

where S= (s ij) y s ij= s (ui,uj).

Generally, the matrix S is not singular and the functions xj(u) and locations u are taken

so that X has maximum range, by columns, with J ∈ C(X). Under maximum range hypothesis,

since ß can be estimated for any location u0, then x0'ß can also be estimated. Let

The best linear unbiased predictor for y0 is (Christensen, 1991):





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where Install Equation Editor and double-click here to view equation. y

Install Equation Editor and double-click here to view equation. , that can be written as:

where Install Equation Editor and double-click here to view equation.

The mean of the squared prediction errors (variance of prediction) is:




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Kriging estimator properties.

The most important properties of this estimator are:

1.- The kriging estimator is linear, unbiased and optimum (BLUE).

2.- The kriging estimator is exact. That is to say that if u0 coincides with the location

of some experimental points ui (points which we have information), then the estimated

value equals observed value and the estimation error variance is null.

3.- The kriging method as spatial estimation instrument is fundamentally characterized

by taking into account the geometry or spatial structure of data.

4.- The cross validation method allows us to evaluate experimental errors of

estimation. It consists of fictionally suppressing each observation yi and estimating it using

kriging. In this way we obtain the estimation of experimental errorsInstall Equation Editor and double-click here to view equation. The analysis of these errors through different statistics

provides a goodness-of-fit measure.

Covariance function and variogram estimation

All information concerning spatial dependency comes from covariance function or

variogram. Since both of them belong to universal kriging estimator expressions, we must

estimate these functions by fitting them to useful practical models. These models are spherical,

gaussian, linear, exponential and potential, which can be estimated by maximum likelihood,

restricted maximum likelihood, minimum quadratic norm or traditional geostatistics method

(method of moments). The first two methods can only be applied in the presence of normal

residuals.

In this way, we obtain an experimental estimated variogram, but only for a few distances.

Therefore, in order to predict we need a theoretical model for covariance function or variogram

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in such a way that we can evaluate these functions for distances on which we usually have no

information.

3.- METHODOLOGICAL CONSIDERATIONS.

The theoretical side of methodology is quite clear, since the aim is to estimate a spatial

process in a point or set of points of space as a linear combination of all (or part) of the

observations. So it is enough to know the spatial dependency structure of the process integrated

in the variogram and, if the process is stationary of second order or intrinsically stationary

(second order stationary of increments), then it is possible to calculate the ordinary or universal

kriging estimator, respectively, to obtain the prediction. But just at this point the main problems

of a practical type arise, due to difficulties in knowing the real structure of the spatial variability

of the process.

There are two main problems. First, how to estimate the parameters of a theoretical

variogram from empirical variogram, so that the fitting is accomplished in an optimum way and

taking into account that the usual fitting procedures are invalid. Second, how to do spatial

processes prediction in the presence of spatial drift or trend. In theory, if the process is

intrinsically stationary we should apply universal kriging, but it can not be done because it is

impossible to estimate directly the theoretical variogram in trend presence.

Theoretical variogram estimation.

The variogram appears in kriging equations whenever we make predictions or calculate

their variance.The variogram function is defined for any distance between locations.Since it may

be location pairs for which we have no information, we must estimate the theoretical model from

the empirical variogram.

When we have calculated the experimental variogram (having amended any possible

anisotropy), we decide the kind of theoretical model we are going to use. Then, the fitting can

be carried out in two ways:

1) By an “interactive way”. Programs such as Variowin or Splus Spatial Stats allow us an

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iterative estimation of theoretical variograms starting from some initial values and reducing the

error of estimation until it reaches a satisfactory level.

2) Estimating by “non-linear least squares”. We can fit the theoretical model by

optimization, usually through non-linear least squares. The usual statistical hypothesis for

non-linear regression models are invalid to fit variograms since the different values of variogram

function are not independent. Cressie (1985) describes procedures based on generalized least

squares and weighted least squares for variogram fitting, that take into account the particular

structure of the variogram values. Zimmerman and Zimmerman (1991) compares different

estimation methods and concludes that estimation by non-linear or weighted (through some

specific function) least squares can be considered valid for most of the more sophisticated and

computationally intensive methods.

Cressie (1985) suggests the following weighted non-linear least squares estimator:

where Install Equation Editor and double-click here to view equation. is the number of different pairs at distance h, and the sum

is computed for every distance in which the empirical variogram has been calculated.Install Equation Editor and double-click here to view equation. is the value for the empirical variogram on the distance h, andInstall Equation Editor and double-click here to view equation. is the theoretical variogram with unknown parameters. These

weights can be easily included in the non-linear least squares procedure (nls) through a function

for the residuals that incorporates the previous weights.

Estimation and prediction under spatial trend.

Under spatial trend, some directional variogram, and the omnidirectional, may be non-

stationary . Their analysis will show us the direction or directions in which some type of trend is

manifested. Since the process mean is not constant, we can not apply the ordinary kriging

procedure. In this case, we can follow two different ways. First, to eliminate the trend by

obtaining a stationary process. Second, to integrate the trend in the model and use it to make


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spatial predictions. We will call these procedures “kriging on the residuals” and “iterated universal

kriging”, respectively.

In the first case, there are some procedures to estimate the trend. It is worth pointing out

the median-polish procedure and other estimation methods related to polynomic regression and

generalized additive models.

Median-polish: This is a non-parametric procedure for estimation of the trend, that is to

say, it does not impose any restriction on its functional form. Starting from the spatial process

when its mean m(⋅) is unknown and it is not constant, it can be estimated supposing that it can be

broken down as the sum of its directional components. In the most usual case, i.e. in R2, this is

expressed as:

where a is the global effect, c(⋅) is the column effect, r(⋅) is the row effect and the points {ui:

i=1,...,n} are located on the nodes of a mesh or grid of dimension pxq (p rows and q columns):Install Equation Editor and double-click here to view equation. in such a way that

Install Equation Editor and double-click here to view equation. and


The row effect can be estimated through an analogous procedure by using medians instead

of means. The estimated principal effects, under very general conditions, minimize the norm L1

(absolute value norm)

and the procedure converges.

When calculating spatial trend in locations without observations, we will not have any

explicit formulation to calculate it, as we have not imposed any restriction on the functional form




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of the trend.In this case, we can estimate interpolation planes for each four points of the median-

polish trend or use other more sophisticated procedure called “Akima method” or “spline

surfaces method”. This method is valid for regular grids as well as for regularly distributed data

in the plane. It consists of fitting polynomic surfaces of small degree, the minimum possible, to

the regions obtained through the triangulation of Delaunnais, so that the surfaces will be

differentiable on edges (obtained from surfaces intersection). Thus, we get a soft surface from

composition of all the small surfaces, called “spline surfaces”. Since the form of each spline

surface is known, we can interpolate the trend in every point of the spatial domain. Extrapolation

by any one of the previous methods can not be done unless we make some hypothesis about the

behaviour of the surface at domain limit.

Generalized additive models (G.A.M.): When the data are not regularly distributed on

the plane, there are other procedures to estimate the trend.

These methods are: polinomic regression, generalized least squares and local regression

surfaces, that are particular cases of “generalized additive models”. In all of them it is

hypothesized that trend follows the form:

where fi can be any type of function, for example polynomic functions, trigonometrical functions,

etc. The main problem of this method is that it relies on the hypothesis of independence of the

data, therefore the results must be handled with care.

Using global trend surfaces implies the following difficulties: the variability of extrapolated

values of the trend depend critically on how far we have moved from the observational domain.

One solution consists of fitting a polynomic surface in each point using only the nearby points.

This is known as “local regression”, obtained, generally, by weighted least squares, giving more

weight to the nearest points.

Ordinary Kriging on the residuals.


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Once we have estimated the trend, we can express the spatial process as

where the residuals {R(ui): i=1,...,n} are a set of spatial data devoid of trend, and they are ready

to have ordinary kriging procedure applied to them.

We can fit a theoretical variogram with these residuals, thus applying ordinary kriging

equations we get:

This estimation approximates the unknown real errors {e(ui): i=1,...,n}. We can obtain an

estimation of the trend Install Equation Editor and double-click here to view equation. using any one of the previous procedures.

Thus, now for every point on the plane we have two estimations. One for the trend and the other

for the residual Install Equation Editor and double-click here to view equation. . Combining both, we get the kriging predictor

based on the residuals, y(u0), defined as follows:

This estimator is called “median-polish kriging” in the event that we have estimated trend

through median-polish.

This predictor is also an exact interpolator, i.e., Install Equation Editor and double-click here to view equation.

The variability of this estimator Install Equation Editor and double-click here to view equation. comes only from the

ordinary kriging predictor applied to the residuals. This variability is measured by the variance of

the ordinary kriging estimator and we denote it by Install Equation Editor and double-click here to view equation. .

Iterated Universal Kriging.




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The second method consists of applying universal kriging, that estimates both the trend,

generally polynomic, and the process in the point or points. The main problem of this method is

the impossibility of correctly estimating variogram unless we have eliminated trend previously.

And we can not estimate trend by usual procedures if the data are correlated. One solution is to

apply universal kriging iteratively following the next algorithm:

Step 1. Apply universal kriging using empirical variogram of the original data as first

approximation of the theoretical variogram.

Step 2. Once trend has been eliminated using estimations from step 1, we obtain the residuals.

Step 3. Compute empirical variogram using the residuals (step 2) and estimate the theoretical

variogram.

Step 4. Apply universal kriging using former estimation of theoretical variogram as input.

Step 5. Return to step 2 and repeat the process until the results do not vary substantially.

As this algorithm concerns only the estimation of theoretical variogram, the variance of

the error of estimation will be the one given by the universal kriging method.

There are no general results for comparing the precision of the two previous procedures.

In cases in which they have been applied (Cressie, 1993) the obtained predictors have been

almost identical, the mean of squared error being smaller in median-polish-kriging. The same

conclusion has been reached in the application to the case of the real-estate market in the case of

Albacete city.

4.- MODELIZATION OF HOUSING PRICE IN ALBACETE.

The importance of the housing sector in the Spanish Economy, and particularly in Castilla-

La Mancha, comes from its share in the GDP (more than 5 %). The economic and physical

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qualities of Albacete city make it a suitable case to apply a spatial study. It is worth pointing out

the geometric form of the city, practically circular, and the absence of relevant topographical

unevenness that allow us to consider the city as an ideal environment for the spread of the spatial

dependency equally in all directions (isotropy).

The empirical study starts with the description of the sampling. Then, the results obtained

from four different kriging methods are compared and, finally, they are analysed.

SAMPLING AND TREATMENT OF THE INFORMATION.

Available information was obtained by a sampling procedure over the data supplied by

Real-Estate Agencies, due to the lack of official information about housing prices related to the

spatial aspect. The sample contains very rich information covering a wide range which includes

houses of every type from the point of view of: age, quality, surface, and so forth. In other words,

the sample representativeness is in accordance with the housing-market in Albacete city in 1997.

The initial data base had 505 records with the following fields:

- street and number,

- zone,

- age, expressed in years,

- surface, in square meters,

- quality, different levels according to the Agencies,

- parking facilities, if it possesses it or not, and

- total price of sale, in current pesetas.

Once the information has been collected, the next sequence was followed:

a) Each house was exactly located on the city plan of Albacete.

b) The city expansion belt was delimited for predicting in the expansion zones of the city

contemplated in the future P.G.O.U. (General Town Planning).

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c) Age was recodified in a new ordinal variable (cod.age) with the following modalities:

- new, for the finished and delivered houses in the last year,

- from 1 to 5, for the houses that were delivered more than a year ago and less than five,

- from 6 to 10, for those six to ten years old,

- from 11 to 20, for those eleven to twenty years old, and

- more than 20.

d) Surface recoding. Surface variable was recodified in an ordinal variable, called

“cod.surf”, with five categories:

- very small, surface below 50 m2,

- small, 50 - 70 m2,

- medium, 70 - 90 m2,

- large, 90 - 125 m2, and

- very large, more than 125 m2.

e) Treatment of the quality. We have worked with five categories of quality: intermediate

level “good” corresponding to the standard quality; two categories above it, “very good”,

comparable to the new houses of first quality, and “luxury”; and two lower categories, “bad” for

old houses built with bad material and in very bad habitability and conservation conditions, and

“substandard” for new or semi-new houses that were built below the standards of quality of the

houses classified as good or houses of the previous type that have been renovated and improved.

f) Determination of square meter price, as the ratio between the total housing price and

its useful surface.

CONSTRUCTION OF THE EQUIVALENT HOUSING CLASS.

When we select the data set which will be used in the analysis, we have two options. The

first consists of filtering the available information, that is to say, to consider only those cases that

fulfil some given characteristics relating to age, surface and quality (Chica Olmo (1994)). In doing

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so, the data base used for the spatial modelling will be the most homogeneous possible. The

principal disadvantage of this alternative is that much information is lost, which is not advisable

if the size of the sample is not excessively large, as in our case.

The other solution which is adopted in this investigation, consists of referring all the house

prices to a sole support in order to use all the available information in the sample. For this

purpose, we have accomplished an analysis of the variance of the price by square meter using as

factors the surface, the age, the possession or not of parking facilities and the quality of the

housing. In this way we obtain the corresponding effects at different levels of the previous factors

referenced to a housing pattern. Eliminating these effects from the data, we obtained their

reduction to an “equivalent housing class”.

Analysis of variance. The aim here is to estimate the effects on housing sale price in

Albacete city of age, surface, quality and parking possesion. Therefore, we will be able to

eliminate them reducing data to homogeneous support. We used a multiplicative model assuming

that the effect of any one of the previous factors is proportional to housing price. The model used

was:

log(price) ~ cod.surf + cod.age + quality + parking + error

Once the effects have been estimated and the appropriate transformations applied, we will

obtain the following:

where k is the mean price of the square meter filtered of all effects and the remaining terms are

the error and the antilogarithms of the estimated effects expressed as indices.

The previous model does not include different order interactions among the factors

because the estimated effects were not, significantly, different from zero.

The following table shows the estimated effects for all factors as well as their

antilogarithms.The “reference price”, to which all the effects refer, is 72.078 ptas. and

corresponds to a very small house (up to 50 m2) more than twenty years old, of bad quality and

without parking.


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Factors Levels Estimated efects exp(efects)

Constant 11.18551 72078.1

very small 0 1

small -0.07724 0.92567

medium -0.16508 0.84782

big -0.15951 0.85256

Surface very big -0.2312 0.79358

more than 20 0 1

from 11 to 20 0.12353 1.13148

from 6 to 10 0.13373 1.14309

from 1 to 5 0.29364 1.3413

Age new 0.28124 1.32477

bad 0 1

substandard 0.30054 1.35055

good (standard) 0.46133 1.58618

very good 0.67931 1.97252

Quality luxury 0.83255 2.29918

no 0 1

Parking yes 0.15157 1.16365

Table 1: Housing price factors (analysis of variance).

From the table above we observe:

a) The effect of the surface on the price decreases the size of housing increases, being for

a very large house 79,36% of the price corresponding to a very small house.

b) The price of housing decreases as the age of housing increases. The maximum price

is reached in the group of houses from 1 to 5 years old with a very similar effect in the group of

newest ones.

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c) The quality is the most important factor. It explains 58,6% of the difference of prices

in the poor quality group of houses. For the luxury houses group, differences in prices due to

different qualities reach 130%. Surprisingly, this factor is omitted by Ministry and Valuation

Companies when they do valuations.

d) The scarcity of public parking in some city areas means that, with the same

characteristic of surface, age and quality, a house with parking space is 16,4% more expensive,

on average, than the one which does not possess it.

Once the previous effects have been estimated, they are eliminated from the observations

transforming them into a house of reference, that is, a very small house of more than 20 years,

poor quality and without parking space. We call it “equivalent house”.

Only 355 records of initial data had information about quality. In order to use all the

records for the spatial analysis we estimated the quality for the remaining cases through a

classification tree (Breiman et al.(1984), Clark & Pregibon (1992), Venables & Ripley (1996)),

a learning method used in expert systems. The predictors are location, age and surface:

Classification tree:tree(formula = quality ~ x + y + cod.age + cod.surf, data = datos,na.action = na.omit, mincut = 5, minsize = 10, mindev = 0.01)

Number of terminal nodes: 41Residual mean deviance: 0.8392 = 234.1/279Misclassification error rate: 0.1812

The proportion of misclassified cases is 18,12%, which seems acceptable.

Equivalent Information. Only 30 houses were rejected after the location process and

they were removed from the analysis, 475 valid cases remaining. All the efects were filtered in this

group. Thus we obtained an estimation of the logarithm of price and the residue of each case.

By manipulating them conveniently, we have:

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where we know the reference price (the constant in the multiplicative model: 72.078 ptas/m2) and

the residuals. The class of equivalent houses is obtained by multiplying the base price times the

residuals:

Then, the equivalent houses data base is ready for applying kriging techniques.

KRIGING ON HOUSING PRICE IN ALBACETE.

Our objective is to model the spatial behaviour of housing price in Albacete city. First,

we will carry out point kriging on the observations irregularly distributed on the plane.

Alternatively we also apply block kriging, where observations are taken as the mean price

calculated in different areas or blocks. Generally, these areas have an irregular form but we have

used squares from a regular grid. In particular, we will apply the following four methods:

universal kriging and ordinary kriging on the residuals of a generalized additive model (Hastie &

Tibshirany (1990), Venables & Ripley (1996) ) for the point estimation, and universal kriging and

median-polish kriging (Cressie (1993)) for the block estimation. The usual procedure can be

summarized as follows:

1) Estimate and eliminate the trend if the procedure requires it.

2) Estimate the empirical variogram, removing anisotropies, if they exist, from the original

data or from the residuals with respect to the trend.

3) Fit a theoretical variogram model from the empirical variogram.

4) Estimate the corresponding kriging model (ordinary or universal).

5) Predict the price and calculate the prediction error over 1330 nodes of a regular grid

which covers the city.

6) Analyse by cross validation the goodness-of-fit for each model.

MODEL SELECTION AND RESULTS.



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In this section, we will compare briefly the relative advantages of the four methods by

taking as criteria the means of the prediction results, cross validation and measure errors.

A) Selection of modelling and prediction method of the housing price. With regard

to the spatial dependency structure of housing prices, the four procedures give us similar results.

This structure is spherical, but show different range, sill and nugget effect. Thus, the price in a

particular location depends, mainly, on the prices of the nearest houses. This influence decreases

as the distance increases and vanishes when the distance range is reached. This kind of spatial

dependence is called “neighborhood effect”.

The four methods show similar price trends in Albacete. The highest prices are reached

in the downtown area (Paseo de la Libertad, Altozano, Catedral, Plaza de la Mancha, Calle

Ancha,...) decreasing gradually from there to peripheral areas (Barriada de la Seiscientas,

Carretera de Murcia, Alto de los Molinos, Hoya de San Ginés, Mortero de Pertusa, Carretera de

Jaén, Barrio de San Pablo, Campollano, end of Paseo de La Cuba,...). A quadratic function was

used to model the structure of the trend, previously explained, when applying universal kriging

methods (for point and block data).The same form (quadratic function) is revealed for trend by

kriging methods that eliminate this component, modelling it through a local regression surface

and median polish when we use point and block kriging respectively.

When we consider the prediction error, the apparent similarity between methods

disappears (table 2). When comparing prediction error from different methods, we must bear in

mind that e(u), can be broken down into the sum of two second order stationary and

incorrelated error processes,

where eM(u) is a measure error process (nugget effect) and e(u) is the prediction error process due

exclusively to the model. Furthermore, the process covariance function verifies:

for whatever u and w locations. According to this breakdown and using all of the above-

mentioned methods, we have obtained both the prediction of square meter price for equivalent

housing class and the error (total and free of measure error).



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Total prediction error (includes measure error)

Universal Point Kriging

Minimum Q1 Median Mean Q3 Maximum

10940 14760 16180 16390 18090 19250

G.A.M. Kriging


11030 14460 15350 15280 16170 16230

Universal Block Kriging


8315 12560 13310 14030 15010 22730

Median-Polish Kriging


6073 10290 11350 11270 12310 12320

Table 2: Prediction error for every estimation method.

The estimation of the measure error variance is different for each method and is equal to

the nugget effect corresponding to each one of the fitted theoretical models of variogram. These

effects, (expressed in 1010 pesetas) are:

Estimation of measure error

variance (nugget effect)

Universal Point Kriging 0.016075

G.A.M. Kriging 0.016896

Universal Block Kriging 0.012798

Median-Polish Kriging 0.00638

Table 3: Nugget effect estimation.

We see that all of them are very similar except from the median-polish kriging model.

Eliminating these effects, we can obtain the prediction errors due to the estimation method.A

summary of the statistics is shown in table 4.

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Prediction error due to the estimation method

(measure error free)

Universal Point Kriging Minimum Q1 Median Mean Q3 Maximum

5475 7572 10070 10170 12900 14490

G.A.M. Kriging Minimum Q1 Median Mean Q3 Maximum

4740 6338 8164 7922 9618 9726

Universal Block Kriging Minimum Q1 Median Mean Q3 Maximum

4945 5450 7017 7981 9863 19720

Median-Polish Kriging Minimum Q1 Median Mean Q3 Maximum

5411 6494 8069 7906 9370 9387

Table 4: Prediction error (measure error free) for every estimation method.

Comparing the four models through cross validation statistics (Table 5), or any of the

previous prediction errors, we can conclude that:

- In point kriging as well as in block kriging, the best result is provided when we

previously have eliminated the trend.

- If we compare the two models for deciding whether or not to group the observations in

blocks, we choose block methods because they provide better MQE and AMQE statistics.1

- Comparing kriging on residuals from generalized additive model with median-polish

kriging, the errors due exclusively to the model (table 4) are practically equal for both models, but

the cross-validation statistics favour the latter. The only disadvantage of this method is that its

empirical residuals tails are not well fitted to the normality hypothesis.

1 Cross-validation statistic are:


where ei = experimental error, s i = theoretic error due to the model.

23

From this discussion, we conclude that we prefer blocks as support to take the

observations, designing a regular grid that covers the city and averaging the observations in such

grid blocks. At the same time, we choose median-polish kriging to predict the square meter price

housing of Albacete because this method provides the best results in terms of cross validation and

total error of prediction.

Comparative table of cross-validation statistics

for all estimation methods.

Method ME MQE AMQE TME

Universal Point Kriging -0.00027 0.02117 0.999462 0.146492

G.A.M. Kriging -0.0001 0.02092 1.010452 0.143707

Universal Block Kriging -0.00074 0.01507 0.957974 0.12871

Median-Polish Kriging 4.21874e-13 0.01364 0.951739 0.122727

Table 5: Cross-validation statistics.

B) Results. The results obtained by this method are graphically shown below. The

estimated values and the estimated errors for the four methods can be seen in appendix.

Figure1 shows a non-stationary variogram with a “cyclical in space” structure, similar to

a quadratic trend. If we eliminate the trend, estimated by median-polish method, we can obtain

the experimental variogram of the median-polish residuals (Figure 2). This variogram is stationary

and follows a spherical variogram model with range 500 m., sill 0.009 and nugget effect 0.007.

If we take these values as initial parameters to fit the spheric model by non-linear least squares

(Cressie, 1985), the fitted values of the variogram parameters are: range 500.42 m., sill 0.008594

and nugget effect 0,006377. Figure 3 shows the median-polish experimental residuals and the

initial and fitted theoretical variograms:

24

Once we have estimated the theoretical variogram model, we can apply median-polish

kriging (Cressie, 1993). The housing price prediction will be obtained as the sum of trend,

25

estimated by Akima method (Akima, 1978), from the nodes in which the median-polish trend has

been obtained, and the ordinary kriging prediction of the residuals, in the 1330 nodes of a regular

grid whose squares have 100m. x 100m. Figures 4 to 11 show the aforementioned results.

26

27

28

29

30

31

32

33

Finally we analyse the goodness-of-fit of median-polish kriging model. In order to do that,

we summarize its position and cross-validation statistics in the following table.

Observed values Predicted values

Minimum 0,35100

Q1 0,61700

Median 0,67930

Mean 0,70060

Q3 0,78080

Maximum 1,19900

Minimum 0,42830

Q1 0,63890

Median 0,70190

Mean 0,70060

Q3 0,75820

Maximum 0,87790

Table 6: Statistics of the observed and predicted values (cross-validation).

Cross-validation statistics from the ordinary kriging on

median-polish residuals.

ME = 4,21874e-013 AMQE = 0,9517394

MQE = 0,013643235 TME = 0,1227271

Table 7: Cross-validation statistics from the median-polish kriging.

The following figure show the omnidirectional variogram of experimental errors. We can

observe that there is no kind of spatial dependence among residuals. So we conclude that the

theoretical model has successfully explained the spatial dependence structure.

34

REFERENCES.

Akima, H. (1978): “A method of bivariate interpolation and smooth surface fitting for

irregularly distributed data points”. ACM Transactions on Mathematical Software, 4, 148-159.

Anselin, L. (1988). “Spatial Econometrics: Methods and Models”. Dordrecht: Kluwer -

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Anselin, L. & Florax, R.J.G.M. (1995): “New Directions in Spatial Econometrics”.

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Bailly, A.S.; Coffey, W.J.; Paelinck, J.H.P. & Polèse, M. (1992): “Spatial Econometrics of

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35

Cressie, N.A.C. (1985). “Fitting variogram models by weighted least squares”. Journal of the

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Cressie, N.A.C. (1986). “Kriging nonstationary data”. Journal of the American Statistical

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36

Matheron, G. (1963): “Traité de Géostatistique Apliquée. Tome II: Le krigeage”. Memoires

du Bureau du Recherches Geologiques et Minieres. Nº 24. Paris: Ed. Bureau du Recherches

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Matheron, G. (1971): “The Theory of Regionalized Variables”. Paris: CCMM de Fontainebleau.

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Ripley, B.D. (1988). “Statistical inference for spatial processes”. Cambridge: C.U.P.

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hidrología subterránea”. Barcelona: CIMNE.

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Starks, T.H. & Fang, J.H. (1982): “The effect of drift on the experimental variogram”. Journal

of the International Association for Mathematical Geology, 14, 309-320.

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International Association for Mathematical Geology, 16, 601-615.

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variogram estimators and kriging predictors”. Technometrics.

37

APPENDIX 1: MAP OF ALBACETE.

38

APPENDIX 2: PREDICTED VALUES FOR HOUSING PRICE IN ALBACETE.

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal

Block Kriging Median-Polish Kriging

x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

1.964 -569 64.217 18.689 13.731 53.038 16.227 9.714 48.080 17.350 13.155 45.081 12.323 9.386

2.066 -569 64.382 18.766 13.835 54.093 16.234 9.725 47.780 17.621 13.510 47.410 12.324 9.387

2.168 -569 64.361 18.803 13.886 54.883 16.234 9.726 47.424 17.864 13.826 47.571 12.324 9.387

2.271 -569 64.300 18.836 13.930 54.780 16.234 9.726 47.046 18.074 14.096 46.456 12.324 9.387

2.373 -569 64.239 18.873 13.980 54.160 16.234 9.726 46.680 18.247 14.317 46.338 12.324 9.387

2.475 -569 64.178 18.914 14.035 52.976 16.234 9.726 46.337 18.382 14.488 46.197 12.324 9.387

2.577 -569 64.116 18.958 14.095 51.876 16.234 9.726 46.013 18.481 14.614 46.057 12.324 9.387

2.679 -569 64.055 19.006 14.159 50.588 16.234 9.726 45.630 18.551 14.703 45.917 12.324 9.387

2.781 -569 63.994 19.058 14.228 49.150 16.234 9.726 45.177 18.595 14.758 45.776 12.324 9.387

2.883 -569 63.933 19.113 14.302 47.473 16.234 9.726 44.667 18.625 14.796 45.636 12.324 9.387

2.985 -569 63.871 19.172 14.381 45.786 16.234 9.726 44.078 18.667 14.848 45.496 12.324 9.387

3.087 -569 63.810 19.234 14.464 44.074 16.234 9.726 43.410 18.725 14.921 44.717 12.324 9.387

1.556 -466 62.546 17.607 12.217 50.761 15.903 9.162 50.416 15.384 10.425 44.365 11.587 8.395

1.658 -466 62.687 17.776 12.460 51.522 15.966 9.271 50.724 15.516 10.619 47.062 11.740 8.605

1.760 -466 63.163 18.071 12.876 52.533 16.077 9.460 50.806 15.829 11.071 49.434 12.060 9.038

1.862 -466 63.691 18.339 13.250 53.477 16.166 9.612 50.775 16.183 11.571 51.808 12.256 9.298

1.964 -466 64.149 18.529 13.512 54.428 16.215 9.693 50.648 16.524 12.044 51.401 12.318 9.379

2.066 -466 64.450 18.642 13.667 55.460 16.232 9.722 50.415 16.833 12.465 50.296 12.324 9.387

2.168 -466 64.526 18.698 13.743 56.234 16.234 9.726 50.084 17.102 12.826 49.180 12.324 9.387

2.271 -466 64.465 18.730 13.787 56.132 16.234 9.726 49.754 17.329 13.127 48.378 12.324 9.387

2.373 -466 64.403 18.766 13.836 55.511 16.234 9.726 49.414 17.515 13.372 48.500 12.324 9.387

2.475 -466 64.342 18.806 13.889 54.328 16.234 9.726 49.094 17.659 13.559 48.360 12.324 9.387

2.577 -466 64.281 18.849 13.948 53.227 16.234 9.726 48.774 17.763 13.695 48.219 12.324 9.387

2.679 -466 64.220 18.896 14.011 51.940 16.234 9.726 48.419 17.834 13.787 48.079 12.324 9.387

2.781 -466 64.158 18.947 14.079 50.502 16.234 9.726 47.993 17.878 13.843 47.939 12.324 9.387

2.883 -466 64.097 19.001 14.153 48.825 16.234 9.726 47.486 17.903 13.876 47.799 12.324 9.387

2.985 -466 64.036 19.059 14.230 47.138 16.234 9.726 46.896 17.938 13.921 47.629 12.324 9.387

3.087 -466 63.975 19.121 14.313 45.426 16.234 9.726 46.230 17.988 13.986 46.107 12.324 9.387

3.189 -466 63.913 19.186 14.399 43.704 16.234 9.726 45.488 18.058 14.076 45.266 12.324 9.387

3.291 -466 63.852 19.254 14.491 42.198 16.234 9.726 44.666 18.153 14.196 44.425 12.324 9.387

39

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

1.352 -364 63.761 17.456 11.998 51.128 15.890 9.140 51.369 14.975 9.811 47.153 11.994 8.949

1.454 -364 62.894 17.001 11.326 52.404 15.688 8.784 52.290 14.623 9.266 50.271 11.423 8.168

1.556 -364 62.268 16.784 10.998 53.133 15.584 8.597 52.968 14.341 8.813 53.460 10.633 7.021

1.658 -364 62.379 17.187 11.603 53.485 15.755 8.902 53.095 14.622 9.264 56.092 11.198 7.850

1.760 -364 62.871 17.722 12.382 54.088 15.973 9.284 52.999 15.067 9.951 55.135 11.877 8.791

1.862 -364 63.542 18.125 12.953 54.920 16.123 9.540 52.858 15.483 10.571 54.079 12.208 9.233

1.964 -364 64.127 18.380 13.307 55.850 16.201 9.670 52.723 15.857 11.111 53.004 12.311 9.370

2.066 -364 64.525 18.525 13.506 56.865 16.229 9.717 52.580 16.186 11.576 51.905 12.324 9.387

2.168 -364 64.681 18.596 13.604 57.626 16.234 9.726 52.369 16.469 11.968 50.790 12.324 9.387

2.271 -364 64.629 18.629 13.649 57.522 16.234 9.726 52.125 16.705 12.292 50.509 12.324 9.387

2.373 -364 64.568 18.664 13.696 56.902 16.234 9.726 51.882 16.897 12.552 50.662 12.324 9.387

2.475 -364 64.506 18.702 13.749 55.718 16.234 9.726 51.607 17.046 12.751 50.522 12.324 9.387

2.577 -364 64.445 18.745 13.806 54.617 16.234 9.726 51.329 17.153 12.893 50.382 12.324 9.387

2.679 -364 64.384 18.791 13.869 53.330 16.234 9.726 50.992 17.223 12.986 50.242 12.324 9.387

2.781 -364 64.323 18.840 13.936 51.892 16.234 9.726 50.583 17.264 13.041 50.101 12.324 9.387

2.883 -364 64.261 18.894 14.008 50.215 16.234 9.726 50.089 17.286 13.069 49.961 12.324 9.387

2.985 -364 64.200 18.951 14.085 48.528 16.234 9.726 49.480 17.313 13.106 48.945 12.324 9.387

3.087 -364 64.139 19.011 14.166 46.816 16.234 9.726 48.813 17.356 13.162 47.496 12.324 9.387

3.189 -364 64.078 19.076 14.252 45.094 16.234 9.726 48.086 17.418 13.244 46.655 12.324 9.387

3.291 -364 64.016 19.143 14.343 43.588 16.234 9.726 47.287 17.505 13.358 45.814 12.324 9.387

3.394 -364 63.955 19.215 14.438 42.042 16.234 9.726 46.409 17.620 13.508 44.974 12.324 9.387

1.045 -261 65.219 18.130 12.960 47.474 16.156 9.595 49.744 15.369 10.402 47.020 12.322 9.384

1.148 -261 65.332 17.816 12.516 49.879 16.049 9.413 51.109 15.008 9.862 50.060 12.300 9.355

1.250 -261 65.274 17.410 11.932 52.152 15.888 9.136 52.433 14.663 9.328 52.707 12.191 9.211

1.352 -261 64.790 16.944 11.240 54.333 15.687 8.782 53.601 14.339 8.811 54.969 11.909 8.835

1.454 -261 63.701 16.377 10.366 55.533 15.430 8.314 54.491 14.051 8.334 57.316 11.391 8.123

1.556 -261 62.426 15.762 9.364 56.038 15.143 7.768 55.036 13.872 8.029 58.805 10.809 7.285

1.658 -261 62.794 16.769 10.975 55.872 15.608 8.640 55.053 14.136 8.475 57.621 11.281 7.967

1.760 -261 63.089 17.493 12.052 55.951 15.919 9.191 54.869 14.562 9.169 56.728 11.902 8.825

1.862 -261 63.475 17.969 12.734 56.312 16.101 9.502 54.694 14.970 9.805 55.690 12.214 9.242

1.964 -261 64.076 18.253 13.131 57.185 16.192 9.655 54.561 15.340 10.360 54.614 12.312 9.371

2.066 -261 64.574 18.415 13.355 58.233 16.226 9.712 54.460 15.666 10.838 53.514 12.324 9.387

2.168 -261 64.824 18.498 13.469 59.028 16.234 9.726 54.354 15.949 11.243 52.414 12.324 9.387

40

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.271 -261 64.793 18.532 13.516 58.930 16.234 9.726 54.187 16.187 11.577 52.640 12.324 9.387

2.373 -261 64.732 18.566 13.562 58.309 16.234 9.726 54.011 16.380 11.845 52.825 12.324 9.387

2.475 -261 64.671 18.603 13.614 57.125 16.234 9.726 53.809 16.529 12.050 52.685 12.324 9.387

2.577 -261 64.610 18.645 13.670 56.025 16.234 9.726 53.585 16.635 12.197 52.544 12.324 9.387

2.679 -261 64.548 18.690 13.731 54.738 16.234 9.726 53.283 16.704 12.290 52.404 12.324 9.387

2.781 -261 64.487 18.738 13.798 53.299 16.234 9.726 52.877 16.743 12.343 52.264 12.324 9.387

2.883 -261 64.426 18.791 13.869 51.623 16.234 9.726 52.392 16.762 12.368 52.123 12.324 9.387

2.985 -261 64.365 18.847 13.945 49.936 16.234 9.726 51.792 16.785 12.399 50.262 12.324 9.387

3.087 -261 64.303 18.907 14.025 48.223 16.234 9.726 51.098 16.820 12.448 48.886 12.324 9.387

3.189 -261 64.242 18.970 14.111 46.502 16.234 9.726 50.382 16.875 12.522 48.045 12.324 9.387

3.291 -261 64.181 19.037 14.200 44.996 16.234 9.726 49.649 16.954 12.628 47.204 12.324 9.387

3.394 -261 64.120 19.107 14.295 43.449 16.234 9.726 48.838 17.062 12.772 46.363 12.324 9.387

3.496 -261 64.058 19.181 14.393 41.878 16.234 9.726 47.915 17.203 12.960 45.279 12.324 9.387

739 -158 65.081 18.334 13.244 43.080 16.218 9.699 47.754 15.930 11.215 45.045 12.324 9.387

841 -158 64.477 18.236 13.108 44.676 16.201 9.670 48.945 15.505 10.603 47.719 12.318 9.378

943 -158 64.349 18.073 12.880 46.805 16.154 9.591 50.152 15.113 10.021 49.628 12.302 9.358

1.045 -158 64.654 17.784 12.471 49.355 16.049 9.413 51.468 14.753 9.470 51.662 12.281 9.331

1.148 -158 65.205 17.348 11.841 52.185 15.862 9.092 52.917 14.420 8.942 54.177 12.214 9.242

1.250 -158 65.634 16.821 11.054 54.902 15.617 8.657 54.391 14.107 8.428 57.135 12.023 8.988

1.352 -158 65.594 16.392 10.389 57.343 15.419 8.294 55.677 13.842 7.976 59.975 11.718 8.575

1.454 -158 64.947 16.177 10.047 58.442 15.335 8.137 56.517 13.690 7.709 62.377 11.493 8.265

1.556 -158 63.957 16.257 10.175 58.439 15.395 8.249 56.857 13.708 7.741 60.600 11.499 8.274

1.658 -158 63.508 16.820 11.053 57.915 15.671 8.753 56.855 13.920 8.112 59.199 11.776 8.655

1.760 -158 63.428 17.416 11.940 57.651 15.934 9.216 56.697 14.249 8.664 58.266 12.095 9.083

1.862 -158 63.514 17.839 12.549 57.658 16.094 9.490 56.532 14.601 9.231 57.316 12.267 9.312

1.964 -158 63.866 18.105 12.925 58.285 16.175 9.627 56.413 14.937 9.754 56.230 12.319 9.380

2.066 -158 64.369 18.270 13.155 59.371 16.212 9.689 56.326 15.245 10.219 55.123 12.324 9.387

2.168 -158 64.750 18.373 13.297 60.278 16.228 9.715 56.217 15.518 10.622 54.545 12.324 9.387

2.271 -158 64.893 18.431 13.378 60.297 16.233 9.724 56.077 15.751 10.960 54.771 12.324 9.387

2.373 -158 64.897 18.472 13.434 59.712 16.234 9.726 55.952 15.941 11.231 54.987 12.324 9.387

2.475 -158 64.835 18.509 13.484 58.529 16.234 9.726 55.726 16.088 11.439 54.847 12.324 9.387

2.577 -158 64.774 18.549 13.539 57.428 16.234 9.726 55.430 16.194 11.587 54.707 12.324 9.387

41

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.679 -158 64.713 18.593 13.600 56.141 16.234 9.726 55.118 16.261 11.681 54.566 12.324 9.387

2.781 -158 64.652 18.641 13.665 54.703 16.234 9.726 54.698 16.298 11.732 54.426 12.324 9.387

2.883 -158 64.590 18.692 13.735 53.026 16.234 9.726 54.240 16.316 11.757 53.583 12.324 9.387

2.985 -158 64.529 18.747 13.810 51.339 16.234 9.726 53.682 16.337 11.786 51.578 12.324 9.387

3.087 -158 64.468 18.806 13.890 49.627 16.234 9.726 53.023 16.368 11.830 50.275 12.324 9.387

3.189 -158 64.407 18.869 13.974 47.905 16.234 9.726 52.324 16.418 11.898 49.434 12.324 9.387

3.291 -158 64.345 18.935 14.063 46.399 16.234 9.726 51.615 16.491 11.998 48.593 12.324 9.387

3.394 -158 64.284 19.004 14.157 44.852 16.234 9.726 50.895 16.592 12.137 47.709 12.324 9.387

3.496 -158 64.223 19.077 14.255 43.281 16.234 9.726 50.116 16.727 12.321 46.493 12.324 9.387

3.598 -158 64.162 19.154 14.357 41.795 16.234 9.726 49.210 16.901 12.556 45.041 12.324 9.387

637 -55 65.249 18.222 13.088 43.117 16.194 9.659 48.539 15.825 11.066 44.635 12.324 9.387

739 -55 64.029 18.087 12.899 44.339 16.159 9.600 49.583 15.360 10.390 45.338 12.313 9.372

841 -55 62.983 17.919 12.663 45.861 16.112 9.521 50.578 14.929 9.742 46.206 12.261 9.304

943 -55 62.833 17.694 12.342 48.167 16.033 9.387 51.618 14.540 9.134 49.347 12.170 9.184

1.045 -55 63.406 17.341 11.831 51.018 15.882 9.127 52.876 14.202 8.586 53.089 12.091 9.079

1.148 -55 64.404 16.779 10.990 54.227 15.606 8.637 54.434 13.906 8.087 57.756 11.988 8.941

1.250 -55 65.412 16.006 9.770 57.404 15.200 7.879 56.173 13.624 7.592 61.748 11.667 8.506

1.352 -55 65.990 15.544 8.992 60.085 14.957 7.400 57.747 13.371 7.127 65.347 11.111 7.726

1.454 -55 65.597 15.849 9.511 60.703 15.145 7.772 58.509 13.325 7.040 67.068 11.114 7.730

1.556 -55 64.553 16.347 10.318 60.082 15.436 8.326 58.547 13.484 7.337 62.961 11.634 8.460

1.658 -55 63.551 16.871 11.130 59.084 15.717 8.835 58.421 13.720 7.762 60.004 12.028 8.994

1.760 -55 63.178 17.312 11.788 58.643 15.921 9.194 58.293 14.000 8.248 60.037 12.235 9.270

1.862 -55 63.205 17.627 12.246 58.603 16.038 9.395 58.184 14.298 8.744 58.879 12.307 9.364

1.964 -55 63.481 17.858 12.576 59.153 16.106 9.510 58.102 14.596 9.223 57.689 12.321 9.382

2.066 -55 64.018 18.042 12.835 60.333 16.157 9.597 58.055 14.882 9.669 56.658 12.323 9.386

2.168 -55 64.437 18.192 13.047 61.337 16.199 9.667 57.991 15.143 10.067 56.676 12.324 9.387

2.271 -55 64.784 18.301 13.198 61.500 16.224 9.709 57.780 15.371 10.406 56.902 12.324 9.387

2.373 -55 65.001 18.375 13.300 61.056 16.233 9.724 57.495 15.558 10.680 57.128 12.324 9.387

2.475 -55 65.000 18.419 13.361 59.906 16.234 9.726 57.195 15.703 10.891 57.009 12.324 9.387

2.577 -55 64.939 18.458 13.414 58.806 16.234 9.726 56.782 15.807 11.040 56.869 12.324 9.387

2.679 -55 64.877 18.501 13.474 57.518 16.234 9.726 56.377 15.873 11.134 56.729 12.324 9.387

2.781 -55 64.816 18.548 13.538 56.080 16.234 9.726 55.917 15.909 11.186 56.589 12.324 9.387

2.883 -55 64.755 18.598 13.607 54.403 16.234 9.726 55.455 15.929 11.214 54.900 12.324 9.387

42

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.985 -55 64.694 18.652 13.681 52.716 16.234 9.726 54.985 15.950 11.244 52.894 12.324 9.387

3.087 -55 64.632 18.710 13.760 51.004 16.234 9.726 54.429 15.982 11.288 51.664 12.324 9.387

3.189 -55 64.571 18.772 13.843 49.282 16.234 9.726 53.813 16.029 11.355 50.824 12.324 9.387

3.291 -55 64.510 18.837 13.931 47.776 16.234 9.726 53.193 16.099 11.454 49.983 12.324 9.387

3.394 -55 64.449 18.906 14.024 46.230 16.234 9.726 52.575 16.197 11.591 48.923 12.324 9.387

3.496 -55 64.387 18.978 14.121 44.658 16.234 9.726 51.921 16.328 11.774 47.674 12.324 9.387

3.598 -55 64.326 19.054 14.223 43.173 16.234 9.726 51.173 16.498 12.008 45.845 12.324 9.387

637 48 64.471 17.956 12.714 44.483 16.117 9.529 50.417 15.331 10.347 45.549 12.313 9.372

739 48 62.578 17.703 12.354 45.498 16.026 9.375 51.318 14.849 9.619 46.315 12.253 9.294

841 48 61.189 17.440 11.976 47.036 15.931 9.211 52.098 14.403 8.914 45.964 12.086 9.072

943 48 60.798 17.169 11.577 49.373 15.823 9.023 52.847 14.004 8.253 46.391 11.818 8.712

1.045 48 61.380 16.828 11.064 52.199 15.662 8.737 53.919 13.683 7.696 50.208 11.625 8.449

1.148 48 62.797 16.273 10.201 55.572 15.365 8.192 55.631 13.453 7.281 56.196 11.587 8.395

1.250 48 64.579 15.306 8.574 59.212 14.822 7.123 57.626 13.230 6.859 63.202 11.280 7.966

1.352 48 66.033 14.455 6.943 62.470 14.343 6.063 59.472 12.969 6.341 69.104 10.434 6.716

1.454 48 65.301 15.339 8.634 61.998 14.847 7.175 60.060 12.994 6.392 69.642 10.603 6.976

1.556 48 63.893 16.103 9.927 60.658 15.295 8.061 59.776 13.247 6.892 65.142 11.513 8.294

1.658 48 62.639 16.651 10.794 59.311 15.605 8.635 59.553 13.495 7.357 60.558 12.019 8.983

1.760 48 62.091 16.979 11.293 58.791 15.768 8.926 59.458 13.742 7.802 61.478 12.210 9.237

1.862 48 62.236 17.187 11.604 58.921 15.846 9.062 59.429 13.998 8.244 60.108 12.248 9.287

1.964 48 62.756 17.411 11.933 59.714 15.923 9.198 59.444 14.267 8.692 58.828 12.264 9.308

2.066 48 63.442 17.680 12.321 61.089 16.029 9.379 59.462 14.539 9.132 57.923 12.293 9.345

2.168 48 64.060 17.938 12.689 62.304 16.130 9.551 59.350 14.796 9.536 58.516 12.317 9.377

2.271 48 64.564 18.139 12.972 62.578 16.199 9.666 59.019 15.021 9.882 59.018 12.324 9.387

2.373 48 64.988 18.264 13.146 62.273 16.228 9.715 58.532 15.206 10.161 59.259 12.324 9.387

2.475 48 65.153 18.332 13.240 61.231 16.234 9.726 58.077 15.350 10.374 59.172 12.324 9.387

2.577 48 65.103 18.372 13.295 60.136 16.234 9.726 57.571 15.453 10.527 59.032 12.324 9.387

2.679 48 65.042 18.414 13.353 58.849 16.234 9.726 57.047 15.519 10.624 58.891 12.324 9.387

2.781 48 64.980 18.459 13.416 57.410 16.234 9.726 56.495 15.556 10.677 58.221 12.324 9.387

2.883 48 64.919 18.509 13.484 55.734 16.234 9.726 55.995 15.579 10.711 56.216 12.324 9.387

2.985 48 64.858 18.562 13.557 54.046 16.234 9.726 55.573 15.604 10.747 54.211 12.324 9.387

3.087 48 64.797 18.619 13.635 52.334 16.234 9.726 55.176 15.638 10.796 53.054 12.324 9.387

43

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.189 48 64.735 18.679 13.718 50.613 16.234 9.726 54.729 15.688 10.868 52.213 12.324 9.387

3.291 48 64.674 18.744 13.805 49.106 16.234 9.726 54.278 15.760 10.973 51.353 12.324 9.387

3.394 48 64.613 18.812 13.897 47.560 16.234 9.726 53.808 15.860 11.116 50.137 12.324 9.387

3.496 48 64.552 18.883 13.993 45.989 16.234 9.726 53.331 15.992 11.303 48.479 12.324 9.387

3.598 48 64.490 18.958 14.094 44.503 16.234 9.726 52.774 16.161 11.541 46.650 12.324 9.387

3.700 48 64.429 19.036 14.199 43.088 16.234 9.726 52.069 16.373 11.836 44.821 12.324 9.387

535 150 65.576 17.974 12.740 44.374 16.130 9.550 51.344 15.426 10.487 44.925 12.315 9.375

637 150 63.638 17.617 12.232 45.798 15.995 9.322 52.260 14.899 9.695 46.532 12.255 9.296

739 150 61.399 17.175 11.585 46.897 15.808 8.996 53.066 14.397 8.904 47.264 12.081 9.065

841 150 59.707 16.771 10.979 48.508 15.631 8.682 53.645 13.931 8.130 47.144 11.749 8.618

943 150 58.564 16.459 10.494 50.432 15.488 8.422 53.982 13.510 7.386 46.342 11.234 7.901

1.045 150 58.796 16.190 10.068 52.885 15.350 8.164 54.530 13.164 6.732 47.666 10.682 7.095

1.148 150 60.671 15.912 9.615 56.117 15.182 7.844 56.613 13.068 6.541 54.925 11.039 7.621

1.250 150 62.889 15.359 8.669 59.368 14.848 7.177 58.735 12.944 6.291 59.157 10.992 7.554

1.352 150 64.127 14.795 7.626 61.754 14.519 6.468 60.278 12.770 5.923 62.471 10.386 6.640

1.454 150 63.356 14.859 7.748 61.451 14.578 6.599 60.659 12.806 6.001 66.153 10.525 6.855

1.556 150 61.647 15.705 9.268 59.776 15.064 7.614 60.322 13.028 6.460 66.430 11.296 7.989

1.658 150 60.555 16.175 10.044 58.496 15.340 8.145 60.166 13.241 6.880 61.140 11.743 8.610

1.760 150 60.214 16.356 10.332 58.188 15.439 8.330 60.194 13.445 7.264 60.771 11.897 8.819

1.862 150 60.755 16.422 10.436 58.744 15.460 8.370 60.298 13.663 7.661 60.717 11.939 8.875

1.964 150 61.784 16.703 10.875 60.045 15.589 8.606 60.460 13.914 8.100 59.651 12.016 8.979

2.066 150 62.869 17.185 11.601 61.807 15.821 9.020 60.522 14.189 8.565 59.291 12.155 9.163

2.168 150 63.742 17.633 12.255 63.228 16.027 9.376 60.364 14.454 8.996 59.981 12.269 9.314

2.271 150 64.432 17.952 12.709 63.628 16.157 9.596 59.933 14.682 9.358 60.900 12.318 9.379

2.373 150 65.141 18.135 12.967 63.519 16.215 9.693 59.336 14.866 9.644 61.390 12.324 9.387

2.475 150 65.580 18.228 13.096 62.660 16.230 9.719 58.792 15.008 9.861 61.334 12.324 9.387

2.577 150 65.593 18.277 13.164 61.604 16.232 9.722 58.244 15.111 10.018 61.194 12.324 9.387

2.679 150 65.426 18.324 13.229 60.246 16.233 9.724 57.661 15.179 10.120 61.054 12.324 9.387

2.781 150 65.209 18.374 13.298 58.706 16.234 9.726 57.029 15.219 10.180 59.538 12.324 9.387

2.883 150 65.084 18.424 13.368 56.995 16.234 9.726 56.400 15.247 10.222 57.532 12.324 9.387

2.985 150 65.022 18.476 13.440 55.308 16.234 9.726 55.957 15.276 10.265 55.527 12.324 9.387

3.087 150 64.961 18.532 13.516 53.596 16.234 9.726 55.620 15.315 10.324 54.443 12.324 9.387

3.189 150 64.900 18.592 13.598 51.874 16.234 9.726 55.299 15.373 10.409 53.602 12.324 9.387

44

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.291 150 64.839 18.655 13.684 50.368 16.234 9.726 54.982 15.454 10.528 52.567 12.324 9.387

3.394 150 64.777 18.722 13.775 48.821 16.234 9.726 54.684 15.562 10.686 51.112 12.324 9.387

3.496 150 64.716 18.792 13.871 47.250 16.234 9.726 54.374 15.701 10.888 49.283 12.324 9.387

3.598 150 64.655 18.866 13.971 45.764 16.234 9.726 54.019 15.876 11.138 47.455 12.324 9.387

3.700 150 64.594 18.944 14.076 44.349 16.234 9.726 53.521 16.091 11.443 45.626 12.324 9.387

535 253 65.305 17.794 12.485 45.726 16.078 9.463 53.231 15.062 9.944 45.825 12.291 9.343

637 253 63.152 17.290 11.755 47.303 15.871 9.107 54.100 14.525 9.111 47.871 12.137 9.140

739 253 60.884 16.574 10.674 48.824 15.541 8.518 54.851 14.002 8.250 49.048 11.773 8.650

841 253 58.877 15.926 9.639 50.511 15.222 7.922 55.381 13.522 7.407 49.537 11.252 7.927

943 253 56.769 15.659 9.190 51.591 15.076 7.638 55.667 13.156 6.716 48.876 10.823 7.305

1.045 253 55.998 15.295 8.555 53.196 14.874 7.230 56.147 12.882 6.162 49.896 10.402 6.665

1.148 253 59.401 15.518 8.947 56.854 14.957 7.399 58.163 12.818 6.027 54.607 10.713 7.141

1.250 253 61.426 15.346 8.647 59.273 14.829 7.138 59.910 12.732 5.841 57.710 10.683 7.096

1.352 253 61.127 14.951 7.924 60.053 14.592 6.631 60.738 12.613 5.577 59.448 10.258 6.438

1.454 253 59.544 14.813 7.660 59.107 14.525 6.483 60.672 12.644 5.647 58.437 10.358 6.596

1.556 253 58.546 15.269 8.508 58.045 14.799 7.076 60.307 12.799 5.986 58.715 10.852 7.348

1.658 253 58.200 15.497 8.911 57.336 14.951 7.387 60.443 12.950 6.303 60.297 11.120 7.738

1.760 253 58.204 15.580 9.055 57.417 15.011 7.509 60.748 13.097 6.600 59.347 11.181 7.825

1.862 253 58.723 15.337 8.630 58.149 14.891 7.266 60.952 13.278 6.951 60.872 11.234 7.901

1.964 253 60.575 15.811 9.447 60.195 15.145 7.772 61.180 13.521 7.405 59.984 11.438 8.188

2.066 253 62.290 16.675 10.830 62.414 15.595 8.617 61.317 13.829 7.953 60.585 11.863 8.773

2.168 253 63.583 17.323 11.804 64.098 15.917 9.187 61.188 14.112 8.435 61.472 12.169 9.182

2.271 253 64.777 17.725 12.386 64.837 16.097 9.495 60.789 14.341 8.814 62.676 12.299 9.354

2.373 253 65.877 17.945 12.699 65.017 16.175 9.628 60.225 14.522 9.105 63.487 12.324 9.386

2.475 253 66.510 18.057 12.857 64.322 16.199 9.668 59.692 14.661 9.326 63.503 12.324 9.387

2.577 253 66.645 18.127 12.956 63.373 16.209 9.684 59.123 14.765 9.488 63.357 12.324 9.387

2.679 253 66.393 18.198 13.055 61.961 16.220 9.702 58.466 14.834 9.596 62.859 12.324 9.387

2.781 253 65.917 18.268 13.152 60.242 16.228 9.716 57.674 14.878 9.663 60.854 12.324 9.387

2.883 253 65.388 18.328 13.235 58.256 16.232 9.722 56.883 14.910 9.713 58.844 12.324 9.387

2.985 253 65.049 18.382 13.310 56.402 16.232 9.723 56.317 14.945 9.765 56.834 12.324 9.387

3.087 253 64.978 18.439 13.389 54.690 16.233 9.724 55.976 14.994 9.840 55.831 12.324 9.387

3.189 253 64.986 18.503 13.477 53.006 16.234 9.725 55.738 15.065 9.948 54.992 12.324 9.387

45

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.291 253 64.992 18.570 13.568 51.535 16.234 9.726 55.549 15.161 10.093 53.746 12.324 9.387

3.394 253 64.942 18.637 13.660 49.992 16.234 9.726 55.366 15.285 10.279 51.917 12.324 9.387

3.496 253 64.881 18.706 13.754 48.421 16.234 9.726 55.179 15.439 10.507 50.088 12.324 9.387

3.598 253 64.819 18.780 13.854 46.935 16.234 9.726 54.999 15.627 10.781 48.259 12.324 9.387

3.700 253 64.758 18.856 13.957 45.520 16.234 9.726 54.705 15.853 11.106 46.430 12.324 9.387

3.802 253 64.697 18.936 14.065 44.161 16.234 9.726 54.209 16.121 11.485 44.602 12.324 9.387

433 356 66.625 18.021 12.807 44.900 16.170 9.619 53.982 15.295 10.294 44.738 12.320 9.382

535 356 65.134 17.672 12.311 47.002 16.050 9.415 54.848 14.748 9.461 48.180 12.261 9.304

637 356 63.020 17.122 11.506 48.850 15.819 9.016 55.655 14.218 8.612 50.066 12.026 8.992

739 356 60.944 16.313 10.264 50.790 15.434 8.323 56.416 13.690 7.708 51.453 11.481 8.249

841 356 59.035 15.502 8.920 52.677 15.014 7.514 57.107 13.151 6.705 53.119 10.468 6.768

943 356 57.492 15.378 8.702 53.846 14.914 7.312 57.685 12.934 6.270 53.811 10.556 6.904

1.045 356 57.661 15.102 8.205 55.793 14.732 6.934 58.609 12.815 6.019 56.026 10.731 7.168

1.148 356 60.506 15.064 8.135 58.874 14.673 6.808 60.184 12.668 5.700 60.624 10.464 6.762

1.250 356 62.100 14.916 7.857 60.614 14.561 6.562 61.353 12.531 5.388 63.197 10.059 6.116

1.352 356 60.167 14.689 7.418 59.829 14.414 6.228 61.086 12.491 5.296 58.329 10.035 6.077

1.454 356 56.889 14.469 6.972 57.577 14.301 5.964 60.689 12.510 5.341 56.261 10.101 6.185

1.556 356 56.395 14.835 7.703 56.931 14.532 6.499 60.067 12.539 5.409 55.183 9.983 5.990

1.658 356 56.646 14.662 7.364 56.747 14.481 6.383 60.780 12.677 5.721 55.473 10.330 6.553

1.760 356 57.239 15.168 8.326 57.368 14.780 7.034 61.465 12.734 5.845 58.593 10.092 6.171

1.862 356 58.241 15.121 8.240 58.576 14.775 7.024 61.613 12.912 6.223 59.729 10.373 6.620

1.964 356 60.477 15.431 8.795 60.931 14.959 7.404 61.739 13.125 6.654 60.604 10.629 7.015

2.066 356 62.637 16.380 10.371 63.372 15.467 8.382 62.070 13.498 7.362 62.041 11.584 8.392

2.168 356 64.109 17.026 11.364 65.146 15.800 8.982 62.001 13.781 7.869 63.084 12.065 9.044

2.271 356 65.466 17.408 11.928 66.009 15.980 9.295 61.629 13.996 8.240 64.530 12.254 9.295

2.373 356 66.791 17.615 12.228 66.397 16.059 9.430 61.125 14.163 8.522 65.647 12.293 9.347

2.475 356 67.853 17.738 12.405 66.055 16.092 9.486 60.655 14.298 8.744 65.795 12.300 9.355

2.577 356 68.152 17.850 12.565 65.258 16.125 9.542 60.130 14.402 8.913 65.626 12.312 9.370

2.679 356 68.009 17.973 12.739 63.976 16.165 9.610 59.430 14.474 9.028 64.203 12.322 9.383

2.781 356 67.249 18.082 12.893 62.103 16.195 9.660 58.429 14.519 9.100 62.082 12.318 9.379

2.883 356 66.185 18.163 13.006 59.769 16.207 9.681 57.338 14.552 9.152 59.991 12.305 9.362

2.985 356 65.298 18.226 13.093 57.549 16.211 9.687 56.555 14.592 9.216 57.957 12.300 9.355

3.087 356 64.920 18.290 13.183 55.669 16.215 9.694 56.149 14.656 9.317 57.070 12.309 9.367

46

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.189 356 64.879 18.367 13.289 53.975 16.222 9.705 55.962 14.748 9.461 56.150 12.321 9.382

3.291 356 64.967 18.452 13.406 52.557 16.228 9.716 55.910 14.868 9.648 54.549 12.324 9.387

3.394 356 65.081 18.537 13.524 51.087 16.232 9.722 55.890 15.016 9.875 52.722 12.324 9.387

3.496 356 65.056 18.619 13.635 49.500 16.234 9.725 55.834 15.194 10.143 50.893 12.324 9.387

3.598 356 64.984 18.697 13.742 47.992 16.234 9.726 55.757 15.403 10.453 49.064 12.324 9.387

3.700 356 64.922 18.773 13.845 46.577 16.234 9.726 55.633 15.647 10.809 47.235 12.324 9.387

3.802 356 64.861 18.852 13.952 45.217 16.234 9.726 55.335 15.928 11.212 45.406 12.324 9.387

433 459 66.536 17.931 12.680 45.918 16.151 9.587 55.392 15.009 9.863 48.863 12.301 9.356

535 459 65.232 17.571 12.165 48.278 16.026 9.374 56.199 14.472 9.026 51.080 12.198 9.221

637 459 63.363 17.077 11.440 50.441 15.821 9.020 56.956 13.972 8.199 53.300 11.940 8.876

739 459 61.562 16.496 10.552 52.499 15.542 8.520 57.693 13.509 7.384 55.126 11.486 8.256

841 459 60.319 15.946 9.672 54.521 15.241 7.958 58.449 13.106 6.617 56.636 10.884 7.395

943 459 59.858 15.424 8.784 56.575 14.928 7.341 59.292 12.876 6.150 57.923 10.692 7.110

1.045 459 60.513 14.726 7.490 58.852 14.511 6.451 60.411 12.726 5.829 60.223 10.552 6.897

1.148 459 63.936 14.458 6.949 62.158 14.318 6.005 61.757 12.605 5.558 64.309 10.357 6.595

1.250 459 64.846 14.308 6.630 63.154 14.203 5.723 62.585 12.506 5.330 66.169 10.136 6.242

1.352 459 61.911 14.338 6.695 61.420 14.190 5.692 62.305 12.470 5.246 60.738 10.117 6.211

1.454 459 58.563 14.416 6.862 59.076 14.237 5.807 61.836 12.473 5.252 58.247 10.134 6.239

1.556 459 57.355 14.861 7.752 58.031 14.525 6.483 61.569 12.505 5.330 58.430 10.122 6.219

1.658 459 57.114 15.110 8.219 57.613 14.715 6.898 62.013 12.616 5.585 58.654 10.395 6.655

1.760 459 58.133 15.484 8.889 58.603 14.957 7.399 62.606 12.723 5.822 58.786 10.561 6.912

1.862 459 59.792 15.650 9.174 60.300 15.066 7.617 62.845 12.841 6.076 60.511 10.700 7.122

1.964 459 62.126 15.826 9.471 62.606 15.168 7.818 62.973 13.003 6.411 62.188 10.928 7.459

2.066 459 64.251 16.240 10.148 64.912 15.392 8.244 63.082 13.251 6.900 64.228 11.521 8.304

2.168 459 65.602 16.646 10.787 66.538 15.606 8.636 62.923 13.464 7.300 65.059 11.901 8.824

2.271 459 66.727 16.911 11.191 67.228 15.735 8.868 62.506 13.635 7.610 66.498 12.055 9.030

2.373 459 67.684 17.058 11.412 67.412 15.796 8.975 62.050 13.778 7.865 67.785 12.093 9.082

2.475 459 68.902 17.188 11.605 67.265 15.846 9.064 61.641 13.906 8.087 68.130 12.134 9.136

2.577 459 69.664 17.367 11.868 66.935 15.928 9.205 61.142 14.016 8.274 67.678 12.216 9.244

2.679 459 69.129 17.579 12.177 65.504 16.024 9.371 60.323 14.093 8.403 65.508 12.276 9.324

2.781 459 68.058 17.753 12.427 63.460 16.092 9.487 59.161 14.134 8.472 63.274 12.256 9.297

2.883 459 66.370 17.857 12.574 60.752 16.119 9.533 57.790 14.157 8.512 60.754 12.180 9.197

47

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.985 459 64.981 17.926 12.672 58.274 16.129 9.550 56.696 14.199 8.581 58.461 12.147 9.153

3.087 459 64.330 18.009 12.790 56.268 16.145 9.575 56.159 14.285 8.722 57.876 12.202 9.226

3.189 459 64.454 18.120 12.945 54.728 16.169 9.616 56.042 14.409 8.924 56.916 12.279 9.327

3.291 459 64.772 18.249 13.125 53.457 16.193 9.658 56.125 14.562 9.169 55.314 12.316 9.377

3.394 459 65.090 18.379 13.305 52.082 16.213 9.691 56.261 14.743 9.454 53.564 12.323 9.385

3.496 459 65.240 18.499 13.471 50.544 16.226 9.712 56.368 14.953 9.778 51.705 12.324 9.387

3.598 459 65.176 18.603 13.613 48.968 16.232 9.723 56.412 15.193 10.141 49.869 12.324 9.387

3.700 459 65.089 18.694 13.737 47.489 16.234 9.726 56.392 15.463 10.541 48.040 12.324 9.387

3.802 459 65.026 18.773 13.845 46.127 16.234 9.726 56.236 15.765 10.979 46.211 12.324 9.387

3.904 459 64.964 18.855 13.955 44.809 16.234 9.726 55.871 16.103 11.459 44.371 12.324 9.387

433 562 66.548 17.802 12.496 46.952 16.108 9.514 56.635 14.745 9.456 51.551 12.235 9.270

535 562 65.492 17.393 11.907 49.667 15.956 9.254 57.406 14.217 8.611 53.525 12.017 8.979

637 562 63.824 16.910 11.189 52.069 15.748 8.890 58.151 13.739 7.797 55.919 11.686 8.531

739 562 62.334 16.457 10.493 54.250 15.521 8.483 58.837 13.366 7.118 57.821 11.430 8.178

841 562 61.995 16.038 9.822 56.734 15.280 8.032 59.603 13.102 6.609 59.649 11.266 7.947

943 562 62.571 15.549 9.001 59.323 14.985 7.456 60.543 12.881 6.159 61.208 10.897 7.414

1.045 562 64.576 14.976 7.970 62.200 14.644 6.745 61.810 12.617 5.587 62.779 9.948 5.933

1.148 562 66.963 14.780 7.596 64.557 14.506 6.439 63.141 12.598 5.543 67.527 10.255 6.433

1.250 562 67.170 14.834 7.700 64.840 14.502 6.431 64.109 12.496 5.307 69.383 9.963 5.957

1.352 562 65.068 14.555 7.148 63.815 14.316 5.998 63.891 12.462 5.226 64.826 9.991 6.003

1.454 562 61.678 14.306 6.627 61.610 14.169 5.640 63.643 12.455 5.210 62.660 10.028 6.065

1.556 562 59.731 14.691 7.421 60.125 14.429 6.264 63.450 12.445 5.187 63.173 9.815 5.706

1.658 562 59.269 15.408 8.756 59.634 14.883 7.249 63.714 12.629 5.612 62.567 10.601 6.971

1.760 562 60.205 15.797 9.423 60.619 15.136 7.755 64.137 12.767 5.916 62.248 11.068 7.663

1.862 562 61.926 15.829 9.476 62.240 15.161 7.804 64.417 12.836 6.064 62.600 11.116 7.733

1.964 562 63.956 15.716 9.287 64.169 15.086 7.658 64.441 12.908 6.216 64.375 11.138 7.764

2.066 562 65.954 15.789 9.410 66.256 15.111 7.706 64.234 13.015 6.436 62.890 11.302 7.998

2.168 562 66.806 16.018 9.789 67.485 15.230 7.937 63.818 13.132 6.668 65.461 11.462 8.222

2.271 562 67.310 16.158 10.016 67.711 15.305 8.080 63.325 13.242 6.883 68.046 11.521 8.304

2.373 562 68.033 16.218 10.113 67.717 15.339 8.145 63.006 13.355 7.098 69.106 11.556 8.353

2.475 562 69.017 16.357 10.334 67.535 15.421 8.298 62.733 13.480 7.330 69.502 11.679 8.522

2.577 562 69.057 16.650 10.793 66.896 15.586 8.601 62.155 13.609 7.565 68.638 11.932 8.866

2.679 562 68.007 17.014 11.346 65.254 15.776 8.941 61.140 13.698 7.723 66.416 12.103 9.095

48

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.781 562 66.606 17.248 11.694 63.208 15.885 9.131 59.728 13.726 7.773 63.248 12.058 9.035

2.883 562 65.316 17.345 11.836 60.897 15.916 9.185 58.110 13.720 7.763 59.867 11.839 8.740

2.985 562 63.649 17.409 11.930 58.321 15.929 9.207 56.781 13.751 7.818 57.131 11.728 8.589

3.087 562 62.897 17.531 12.107 56.327 15.969 9.277 56.103 13.873 8.030 56.597 11.911 8.837

3.189 562 63.292 17.710 12.366 55.030 16.033 9.386 56.086 14.045 8.324 56.456 12.139 9.143

3.291 562 64.140 17.915 12.656 54.125 16.096 9.494 56.340 14.238 8.644 56.127 12.247 9.285

3.394 562 64.905 18.127 12.956 53.009 16.153 9.589 56.628 14.457 9.001 54.617 12.281 9.331

3.496 562 65.359 18.323 13.228 51.589 16.196 9.662 56.830 14.709 9.401 52.693 12.306 9.363

3.598 562 65.403 18.483 13.448 49.996 16.222 9.705 56.962 14.991 9.836 50.841 12.322 9.383

3.700 562 65.279 18.604 13.615 48.419 16.232 9.723 57.051 15.297 10.296 49.102 12.324 9.387

3.802 562 65.190 18.698 13.743 46.994 16.234 9.726 56.986 15.625 10.778 47.211 12.324 9.387

3.904 562 65.129 18.779 13.853 45.676 16.234 9.726 56.756 15.981 11.288 45.079 12.324 9.387

331 664 66.954 17.976 12.743 45.326 16.165 9.610 56.957 15.043 9.915 49.908 12.299 9.353

433 664 66.610 17.639 12.263 48.206 16.046 9.408 57.740 14.505 9.078 54.296 12.152 9.159

535 664 65.689 17.114 11.495 51.097 15.825 9.026 58.442 13.981 8.215 56.057 11.759 8.631

637 664 64.220 16.458 10.494 53.801 15.513 8.467 59.114 13.474 7.318 58.388 11.082 7.683

739 664 62.961 15.995 9.751 56.087 15.258 7.991 59.755 13.155 6.713 60.006 10.873 7.379

841 664 63.080 15.698 9.256 58.642 15.063 7.611 60.485 13.037 6.478 61.616 11.212 7.870

943 664 64.354 15.396 8.734 61.455 14.875 7.233 61.396 12.917 6.234 61.309 11.169 7.809

1.045 664 66.824 15.163 8.317 64.267 14.749 6.969 62.605 12.809 6.007 62.934 10.938 7.474

1.148 664 69.089 15.184 8.356 66.239 14.759 6.991 64.011 12.750 5.881 68.435 10.868 7.371

1.250 664 69.925 15.203 8.390 66.875 14.748 6.967 65.096 12.677 5.720 67.912 10.702 7.125

1.352 664 69.250 14.703 7.445 66.996 14.435 6.277 65.685 12.563 5.462 65.651 10.295 6.497

1.454 664 65.506 14.186 6.364 64.732 14.119 5.513 65.634 12.509 5.338 64.352 10.146 6.258

1.556 664 62.904 14.647 7.334 62.832 14.405 6.208 65.239 12.513 5.348 64.682 10.138 6.245

1.658 664 62.037 15.443 8.817 62.121 14.904 7.292 65.151 12.643 5.645 65.016 10.707 7.132

1.760 664 62.305 15.819 9.461 62.586 15.144 7.771 65.452 12.746 5.872 66.261 11.104 7.716

1.862 664 63.454 15.624 9.131 63.672 15.028 7.543 65.810 12.722 5.820 66.086 10.811 7.288

1.964 664 65.511 15.001 8.018 65.433 14.648 6.753 65.791 12.699 5.769 66.235 10.567 6.921

2.066 664 66.550 14.811 7.657 66.729 14.505 6.438 65.326 12.717 5.808 65.097 10.529 6.862

2.168 664 66.807 15.131 8.258 67.394 14.673 6.808 64.712 12.774 5.932 62.139 10.624 7.007

2.271 664 66.320 15.172 8.333 66.893 14.702 6.869 64.181 12.819 6.028 64.937 10.537 6.874

49

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.373 664 66.959 15.081 8.166 66.891 14.676 6.815 64.213 12.919 6.238 69.565 10.668 7.073

2.475 664 68.285 15.310 8.581 67.063 14.842 7.164 64.130 13.029 6.462 70.318 10.809 7.284

2.577 664 67.648 15.765 9.370 66.111 15.122 7.728 63.347 13.212 6.825 68.854 11.427 8.173

2.679 664 65.889 16.359 10.337 64.181 15.449 8.349 62.127 13.311 7.014 65.976 11.726 8.586

2.781 664 63.947 16.595 10.707 62.011 15.570 8.571 60.448 13.319 7.029 61.838 11.645 8.476

2.883 664 62.558 16.601 10.716 59.931 15.560 8.552 58.460 13.255 6.908 57.310 11.185 7.832

2.985 664 61.154 16.611 10.731 57.684 15.553 8.540 56.840 13.236 6.871 54.281 10.836 7.324

3.087 664 60.558 16.814 11.044 55.918 15.648 8.712 56.332 13.445 7.265 53.988 11.424 8.169

3.189 664 61.545 17.115 11.497 55.096 15.786 8.957 56.457 13.672 7.678 55.030 11.857 8.765

3.291 664 63.053 17.427 11.956 54.647 15.912 9.179 56.888 13.894 8.065 56.391 12.009 8.969

3.394 664 64.428 17.767 12.447 53.894 16.033 9.387 57.269 14.152 8.503 56.152 12.090 9.077

3.496 664 65.404 18.088 12.900 52.737 16.134 9.558 57.478 14.461 9.007 54.302 12.209 9.235

3.598 664 65.618 18.335 13.245 51.152 16.198 9.665 57.609 14.796 9.537 52.318 12.296 9.349

3.700 664 65.486 18.505 13.479 49.492 16.226 9.713 57.691 15.144 10.067 50.091 12.322 9.385

3.802 664 65.365 18.622 13.640 47.982 16.234 9.725 57.680 15.501 10.597 47.919 12.324 9.387

3.904 664 65.293 18.708 13.757 46.642 16.234 9.726 57.528 15.875 11.138 45.787 12.324 9.387

331 767 67.051 17.850 12.565 47.106 16.133 9.556 57.951 14.793 9.531 55.594 12.297 9.351

433 767 66.579 17.495 12.056 49.840 15.997 9.325 58.599 14.300 8.747 57.068 12.141 9.145

535 767 65.701 16.881 11.146 52.655 15.716 8.834 59.186 13.817 7.933 58.846 11.717 8.573

637 767 64.531 15.910 9.612 55.591 15.222 7.922 59.830 13.326 7.043 60.755 10.887 7.400

739 767 63.479 15.316 8.592 57.962 14.868 7.217 60.476 13.025 6.456 60.888 10.552 6.898

841 767 63.385 14.992 8.001 60.112 14.623 6.699 61.125 12.947 6.296 60.565 10.911 7.435

943 767 64.307 14.675 7.390 62.445 14.429 6.264 61.858 12.885 6.168 59.606 11.010 7.579

1.045 767 66.823 15.129 8.254 64.771 14.713 6.894 62.965 12.855 6.105 61.917 11.038 7.621

1.148 767 69.577 15.568 9.035 66.807 14.989 7.465 64.340 12.862 6.119 65.637 11.192 7.841

1.250 767 71.388 15.573 9.043 68.155 14.994 7.474 65.587 12.824 6.039 65.428 11.161 7.797

1.352 767 71.746 15.046 8.101 69.083 14.675 6.812 66.599 12.678 5.722 66.839 10.564 6.916

1.454 767 68.368 14.641 7.322 67.069 14.409 6.218 66.736 12.582 5.507 67.835 10.265 6.450

1.556 767 65.682 14.601 7.242 65.251 14.401 6.199 66.166 12.526 5.378 68.226 10.057 6.114

1.658 767 63.876 15.392 8.728 63.904 14.870 7.222 65.860 12.636 5.629 68.260 10.660 7.062

1.760 767 63.755 15.683 9.231 64.039 15.047 7.580 66.191 12.702 5.776 68.985 10.996 7.559

1.862 767 65.160 15.378 8.702 65.157 14.855 7.191 66.822 12.614 5.579 69.882 10.489 6.801

1.964 767 66.740 14.563 7.164 66.500 14.349 6.076 67.054 12.538 5.405 69.756 10.124 6.223

50

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.066 767 64.959 14.098 6.164 65.802 14.040 5.308 66.593 12.514 5.350 68.383 10.025 6.061

2.168 767 67.831 14.269 6.547 67.993 14.128 5.537 65.881 12.549 5.430 65.580 10.152 6.269

2.271 767 65.108 14.243 6.490 65.897 14.110 5.488 65.252 12.554 5.442 62.226 9.991 6.004

2.373 767 65.513 14.302 6.619 65.828 14.173 5.649 65.399 12.660 5.682 67.585 10.284 6.480

2.475 767 66.454 14.842 7.717 65.959 14.532 6.497 65.436 12.736 5.851 70.263 10.355 6.592

2.577 767 65.478 15.297 8.559 64.924 14.836 7.152 64.625 12.892 6.182 69.105 10.923 7.452

2.679 767 63.835 15.775 9.386 63.283 15.121 7.726 63.557 12.958 6.318 65.916 11.132 7.755

2.781 767 62.058 15.866 9.538 61.425 15.174 7.830 61.935 12.950 6.302 61.534 11.039 7.622

2.883 767 60.255 15.664 9.198 59.307 15.058 7.602 59.503 12.880 6.158 55.720 10.619 6.999

2.985 767 58.431 15.508 8.930 57.092 14.978 7.442 57.415 12.844 6.082 51.999 10.230 6.394

3.087 767 58.614 15.958 9.691 55.968 15.220 7.918 57.098 13.096 6.598 51.867 11.049 7.635

3.189 767 60.164 16.383 10.375 55.647 15.443 8.338 57.419 13.325 7.040 54.637 11.463 8.224

3.291 767 62.229 16.783 10.996 55.601 15.632 8.684 57.955 13.529 7.420 57.285 11.478 8.245

3.394 767 64.063 17.330 11.814 55.052 15.864 9.095 58.348 13.828 7.951 57.944 11.645 8.475

3.496 767 65.336 17.828 12.534 53.986 16.052 9.419 58.474 14.215 8.607 56.018 11.993 8.948

3.598 767 65.814 18.183 13.033 52.482 16.165 9.610 58.466 14.615 9.253 53.378 12.232 9.265

3.700 767 65.692 18.407 13.344 50.761 16.217 9.697 58.466 15.005 9.857 50.894 12.314 9.373

3.802 767 65.545 18.548 13.538 49.185 16.233 9.723 58.417 15.389 10.432 48.627 12.324 9.387

3.904 767 65.458 18.642 13.667 47.807 16.234 9.726 58.237 15.778 10.999 46.495 12.324 9.387

4.006 767 65.396 18.725 13.779 46.515 16.234 9.726 57.991 16.188 11.579 44.364 12.324 9.387

229 870 67.108 17.838 12.548 46.370 16.122 9.537 58.100 15.020 9.880 47.050 12.308 9.365

331 870 66.862 17.671 12.309 48.960 16.080 9.466 58.718 14.541 9.135 58.300 12.303 9.359

433 870 66.569 17.384 11.894 51.695 15.971 9.281 59.260 14.119 8.448 59.747 12.214 9.242

535 870 65.973 16.869 11.127 54.355 15.725 8.850 59.836 13.724 7.770 61.346 11.947 8.886

637 870 65.069 16.112 9.942 56.883 15.316 8.101 60.394 13.358 7.103 62.719 11.483 8.251

739 870 64.148 15.385 8.715 58.932 14.871 7.224 60.965 13.067 6.540 62.649 11.007 7.575

841 870 63.897 14.548 7.134 61.076 14.338 6.052 61.569 12.824 6.039 60.631 10.364 6.607

943 870 63.487 14.272 6.553 62.331 14.169 5.639 62.107 12.775 5.935 59.995 10.420 6.694

1.045 870 65.124 14.985 7.988 63.766 14.633 6.720 62.819 12.749 5.878 60.789 10.301 6.506

1.148 870 68.616 15.810 9.445 66.104 15.141 7.764 64.261 12.895 6.189 62.842 11.120 7.738

1.250 870 70.654 15.991 9.745 67.610 15.256 7.988 65.424 12.940 6.283 67.812 11.495 8.268

1.352 870 70.606 15.664 9.198 68.264 15.050 7.586 66.157 12.861 6.117 71.949 11.284 7.972

51

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

1.454 870 68.614 15.148 8.290 67.221 14.723 6.914 66.371 12.718 5.810 73.648 10.767 7.222

1.556 870 65.785 14.933 7.890 65.375 14.583 6.612 66.255 12.555 5.445 73.717 10.082 6.155

1.658 870 63.988 15.200 8.384 64.105 14.739 6.949 65.828 12.618 5.588 73.882 10.589 6.953

1.760 870 63.982 15.402 8.745 64.305 14.854 7.189 66.308 12.654 5.668 74.420 10.864 7.366

1.862 870 65.446 15.146 8.286 65.402 14.683 6.828 67.508 12.556 5.447 75.569 10.376 6.624

1.964 870 67.524 14.534 7.105 67.051 14.297 5.955 68.251 12.480 5.270 76.166 10.060 6.118

2.066 870 67.563 14.294 6.601 67.562 14.130 5.542 68.159 12.456 5.211 75.261 9.981 5.988

2.168 870 66.995 14.287 6.585 67.514 14.111 5.492 67.545 12.481 5.272 72.026 10.119 6.214

2.271 870 65.699 13.956 5.832 66.284 13.915 4.968 66.863 12.473 5.253 67.173 9.948 5.932

2.373 870 65.209 14.312 6.640 65.723 14.132 5.547 66.765 12.599 5.546 68.484 10.489 6.800

2.475 870 64.292 14.737 7.511 64.669 14.418 6.240 66.576 12.666 5.696 68.873 10.662 7.064

2.577 870 63.054 14.848 7.727 63.573 14.546 6.530 65.915 12.642 5.642 68.119 10.346 6.578

2.679 870 62.796 15.272 8.513 63.081 14.813 7.104 65.356 12.653 5.666 68.025 10.341 6.570

2.781 870 62.138 15.258 8.488 62.189 14.816 7.110 64.457 12.620 5.594 66.754 10.146 6.259

2.883 870 60.317 14.734 7.506 60.190 14.528 6.489 61.453 12.636 5.629 59.269 10.239 6.408

2.985 870 56.740 14.929 7.882 57.019 14.638 6.731 58.486 12.637 5.632 53.111 10.070 6.134

3.087 870 58.192 15.460 8.847 56.935 14.948 7.382 58.541 12.859 6.114 54.816 10.814 7.292

3.189 870 60.166 15.681 9.227 57.074 15.094 7.674 58.956 13.038 6.482 57.062 11.018 7.592

3.291 870 62.115 16.015 9.784 56.996 15.273 8.019 59.382 13.142 6.687 59.198 10.541 6.880

3.394 870 64.011 16.947 11.245 56.294 15.708 8.820 59.573 13.517 7.397 59.871 11.030 7.609

3.496 870 65.522 17.628 12.247 55.276 15.990 9.312 59.489 14.012 8.267 57.238 11.791 8.675

3.598 870 66.094 18.068 12.873 53.736 16.143 9.573 59.315 14.465 9.015 54.235 12.173 9.188

3.700 870 65.878 18.328 13.235 51.919 16.211 9.687 59.192 14.883 9.671 51.667 12.305 9.361

3.802 870 65.719 18.482 13.447 50.310 16.232 9.721 59.077 15.280 10.271 49.340 12.324 9.387

3.904 870 65.622 18.581 13.583 48.907 16.234 9.726 58.892 15.673 10.847 47.203 12.324 9.387

4.006 870 65.561 18.662 13.694 47.615 16.234 9.726 58.685 16.082 11.430 45.072 12.324 9.387

229 973 66.751 17.458 12.001 48.663 15.966 9.272 58.801 14.678 9.352 55.083 12.190 9.210

331 973 66.598 17.353 11.847 51.268 15.951 9.246 59.354 14.266 8.691 60.901 12.250 9.289

433 973 66.570 17.199 11.621 53.923 15.902 9.160 59.897 13.927 8.122 61.917 12.255 9.296

535 973 66.018 16.858 11.111 56.209 15.734 8.865 60.450 13.618 7.580 62.947 12.109 9.102

637 973 64.877 16.314 10.266 57.905 15.425 8.305 60.889 13.332 7.054 64.392 11.797 8.683

739 973 63.598 15.616 9.117 59.170 14.998 7.483 61.333 13.079 6.564 64.411 11.344 8.056

841 973 62.815 14.680 7.400 60.598 14.432 6.270 61.832 12.867 6.130 62.107 10.746 7.191

52

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

943 973 61.905 14.632 7.303 61.232 14.400 6.196 62.339 12.808 6.004 60.825 10.630 7.016

1.045 973 63.280 15.449 8.828 62.441 14.908 7.299 62.997 12.827 6.046 61.822 10.672 7.079

1.148 973 65.968 16.065 9.866 64.327 15.302 8.074 64.034 12.956 6.315 61.774 11.314 8.014

1.250 973 68.107 16.241 10.150 65.927 15.416 8.288 64.898 13.023 6.451 62.918 11.753 8.623

1.352 973 68.726 15.912 9.615 67.069 15.207 7.892 65.380 12.962 6.328 66.480 11.641 8.471

1.454 973 67.691 15.138 8.270 66.715 14.728 6.925 65.537 12.793 5.973 68.405 11.024 7.599

1.556 973 65.399 14.447 6.926 65.291 14.299 5.959 65.371 12.596 5.538 68.790 10.273 6.462

1.658 973 63.260 14.542 7.121 63.798 14.337 6.049 65.133 12.573 5.486 67.422 10.425 6.702

1.760 973 62.827 14.929 7.881 63.664 14.543 6.522 65.730 12.570 5.479 67.767 10.547 6.889

1.862 973 63.405 14.645 7.329 64.114 14.355 6.093 67.156 12.515 5.351 69.829 10.314 6.528

1.964 973 65.585 14.290 6.591 65.908 14.134 5.551 68.304 12.487 5.286 72.144 10.208 6.359

2.066 973 68.422 14.410 6.849 68.479 14.208 5.736 68.796 12.506 5.331 76.009 10.323 6.541

2.168 973 67.615 14.539 7.116 68.453 14.277 5.905 68.850 12.510 5.341 70.541 10.332 6.556

2.271 973 67.838 14.271 6.550 68.400 14.109 5.487 68.707 12.487 5.286 66.500 10.153 6.270

2.373 973 67.001 14.212 6.420 67.553 14.062 5.365 68.730 12.547 5.426 68.171 10.431 6.710

2.475 973 63.114 14.309 6.634 64.520 14.129 5.539 68.620 12.571 5.482 67.741 10.500 6.818

2.577 973 63.174 14.585 7.209 64.501 14.341 6.059 68.185 12.538 5.406 66.809 10.259 6.439

2.679 973 64.337 14.986 7.990 65.150 14.615 6.682 67.910 12.548 5.428 65.354 10.267 6.453

2.781 973 63.617 15.077 8.158 64.362 14.687 6.838 67.217 12.562 5.461 65.133 10.330 6.553

2.883 973 60.093 14.657 7.353 61.255 14.460 6.334 64.637 12.559 5.453 59.997 10.318 6.534

2.985 973 57.829 14.874 7.777 58.978 14.587 6.620 61.886 12.554 5.443 55.399 10.176 6.307

3.087 973 59.501 15.364 8.677 59.116 14.881 7.245 61.170 12.689 5.746 56.941 10.531 6.865

3.189 973 60.772 15.578 9.051 58.711 15.032 7.550 61.053 12.849 6.093 57.819 10.690 7.106

3.291 973 61.927 15.991 9.745 58.041 15.265 8.004 60.960 13.044 6.492 60.036 10.675 7.083

3.394 973 63.498 16.833 11.073 57.182 15.673 8.757 60.745 13.421 7.221 60.910 11.174 7.815

3.496 973 65.092 17.529 12.104 56.232 15.971 9.280 60.433 13.904 8.083 58.149 11.846 8.750

3.598 973 65.979 18.001 12.778 54.918 16.140 9.568 60.103 14.357 8.840 54.949 12.189 9.209

3.700 973 66.060 18.277 13.164 53.272 16.212 9.688 59.877 14.775 9.503 52.360 12.307 9.364

3.802 973 65.882 18.428 13.374 51.654 16.232 9.722 59.655 15.167 10.102 50.046 12.324 9.387

3.904 973 65.787 18.525 13.506 50.256 16.234 9.726 59.414 15.552 10.671 47.912 12.324 9.387

4.006 973 65.725 18.605 13.616 48.964 16.234 9.726 59.228 15.951 11.245 45.780 12.324 9.387

127 1.076 66.863 17.154 11.555 48.362 15.802 8.985 58.778 14.805 9.551 44.129 11.816 8.709

53

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

229 1.076 66.330 16.823 11.057 51.155 15.675 8.760 59.486 14.285 8.722 62.226 11.838 8.739

331 1.076 66.397 16.813 11.042 53.719 15.697 8.800 60.016 13.956 8.173 63.236 12.044 9.016

433 1.076 66.605 16.819 11.051 56.134 15.715 8.832 60.484 13.701 7.729 63.546 12.148 9.154

535 1.076 65.894 16.615 10.738 57.821 15.605 8.635 60.944 13.455 7.284 64.338 12.016 8.979

637 1.076 64.304 16.198 10.081 58.660 15.360 8.183 61.312 13.214 6.828 65.258 11.684 8.529

739 1.076 62.418 15.703 9.265 58.904 15.057 7.599 61.597 13.014 6.433 64.774 11.355 8.072

841 1.076 60.695 15.302 8.567 59.022 14.811 7.099 61.935 12.888 6.175 61.054 11.106 7.719

943 1.076 59.984 15.329 8.616 59.484 14.832 7.144 62.432 12.855 6.105 60.678 11.017 7.589

1.045 1.076 61.069 15.735 9.320 60.624 15.096 7.677 63.048 12.903 6.205 62.622 11.133 7.757

1.148 1.076 63.260 16.134 9.978 62.348 15.356 8.176 63.795 13.002 6.409 62.214 11.539 8.329

1.250 1.076 65.538 16.269 10.195 64.244 15.439 8.331 64.388 13.060 6.525 62.226 11.887 8.805

1.352 1.076 67.220 15.948 9.674 66.191 15.227 7.931 64.896 13.001 6.406 62.327 11.775 8.654

1.454 1.076 67.078 15.227 8.433 66.489 14.765 7.003 65.153 12.813 6.016 61.424 11.093 7.700

1.556 1.076 66.094 14.570 7.178 65.981 14.337 6.050 64.864 12.526 5.377 59.307 9.904 5.859

1.658 1.076 63.874 14.329 6.676 64.419 14.182 5.672 64.803 12.496 5.308 56.865 10.130 6.233

1.760 1.076 63.339 14.542 7.121 64.117 14.288 5.931 65.162 12.413 5.108 56.447 9.794 5.670

1.862 1.076 64.117 14.155 6.294 64.638 14.047 5.324 66.750 12.448 5.193 59.424 10.068 6.132

1.964 1.076 64.522 14.176 6.342 65.365 14.054 5.344 68.172 12.463 5.230 65.651 10.111 6.202

2.066 1.076 68.076 14.696 7.432 68.657 14.377 6.144 69.632 12.560 5.456 76.499 10.627 7.012

2.168 1.076 69.428 14.755 7.546 70.290 14.430 6.266 70.508 12.555 5.444 72.714 10.540 6.879

2.271 1.076 70.951 14.447 6.925 71.335 14.243 5.822 68.294 8.315 65.844 6.073

2.373 1.076 72.469 14.161 6.307 72.185 14.037 5.298 71.549 12.483 5.277 64.929 10.155 6.273

2.475 1.076 67.054 13.842 5.554 68.187 13.838 4.748 71.684 12.439 5.172 66.369 9.922 5.889

2.577 1.076 68.341 14.347 6.715 69.235 14.171 5.644 71.557 12.452 5.204 62.844 10.000 6.019

2.679 1.076 69.698 14.602 7.243 70.143 14.381 6.153 71.703 12.503 5.324 62.568 10.229 6.392

2.781 1.076 70.091 15.033 8.077 70.061 14.652 6.762 71.398 12.577 5.494 62.576 10.625 7.008

2.883 1.076 66.637 14.996 8.009 66.992 14.645 6.746 69.249 12.539 5.407 59.445 10.419 6.692

2.985 1.076 63.674 15.073 8.152 64.155 14.687 6.838 66.244 12.441 5.175 56.364 9.790 5.663

3.087 1.076 62.700 15.119 8.236 62.383 14.728 6.926 64.927 12.544 5.420 56.921 10.110 6.199

3.189 1.076 62.017 15.503 8.922 60.433 14.962 7.409 63.789 12.701 5.772 58.811 10.286 6.483

3.291 1.076 61.873 16.113 9.944 58.782 15.307 8.084 62.831 13.049 6.504 61.343 11.104 7.715

3.394 1.076 62.632 16.815 11.044 57.500 15.666 8.744 62.066 13.431 7.239 60.888 11.642 8.471

3.496 1.076 64.110 17.458 12.001 56.568 15.955 9.252 61.433 13.854 7.997 57.999 12.055 9.031

54

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.598 1.076 65.035 17.936 12.687 55.359 16.135 9.560 60.899 14.273 8.703 55.357 12.257 9.298

3.700 1.076 65.572 18.229 13.098 54.112 16.214 9.692 60.520 14.668 9.337 52.984 12.318 9.378

3.802 1.076 66.005 18.385 13.314 52.923 16.233 9.724 60.184 15.041 9.912 50.751 12.324 9.387

3.904 1.076 65.951 18.473 13.435 51.572 16.234 9.726 59.858 15.408 10.460 48.620 12.324 9.387

4.006 1.076 65.890 18.553 13.544 50.280 16.234 9.726 59.628 15.789 11.015 46.488 12.324 9.387

4.108 1.076 65.828 18.636 13.658 49.000 16.234 9.726 59.343 16.212 11.613 44.357 12.324 9.387

127 1.178 66.547 16.647 10.787 50.821 15.565 8.562 59.494 14.348 8.825 52.085 11.097 7.706

229 1.178 66.165 15.886 9.572 54.317 15.221 7.919 60.170 13.849 7.989 64.780 11.134 7.759

331 1.178 66.377 16.139 9.986 56.500 15.356 8.176 60.575 13.639 7.618 65.300 11.645 8.476

433 1.178 66.491 16.265 10.188 58.387 15.417 8.291 60.947 13.460 7.293 65.409 11.870 8.782

535 1.178 65.817 16.110 9.939 59.623 15.316 8.101 61.351 13.250 6.898 65.421 11.682 8.526

637 1.178 64.065 15.652 9.177 59.904 15.038 7.563 61.629 12.994 6.392 65.048 11.084 7.687

739 1.178 61.888 15.254 8.481 59.457 14.794 7.065 61.783 12.809 6.007 63.947 10.706 7.131

841 1.178 60.083 15.238 8.453 58.958 14.779 7.033 62.038 12.727 5.831 59.699 10.588 6.952

943 1.178 58.976 15.380 8.706 58.885 14.874 7.231 62.393 12.737 5.852 59.868 10.630 7.016

1.045 1.178 59.869 15.503 8.921 59.913 14.978 7.443 62.925 12.800 5.987 62.301 10.714 7.143

1.148 1.178 62.432 15.824 9.468 62.088 15.186 7.853 63.682 12.953 6.308 62.307 11.352 8.069

1.250 1.178 65.125 16.068 9.870 64.445 15.320 8.108 64.485 13.042 6.490 62.403 11.844 8.747

1.352 1.178 67.260 15.792 9.415 66.832 15.127 7.738 65.175 12.994 6.391 64.353 11.772 8.650

1.454 1.178 67.742 15.012 8.037 67.678 14.625 6.703 65.694 12.809 6.006 66.033 11.117 7.733

1.556 1.178 68.391 14.315 6.646 68.189 14.171 5.645 65.877 12.571 5.482 66.039 10.257 6.437

1.658 1.178 66.539 14.406 6.839 66.832 14.200 5.717 65.978 12.478 5.265 64.522 10.155 6.273

1.760 1.178 65.526 14.444 6.920 66.157 14.213 5.750 66.474 12.430 5.150 63.447 10.067 6.129

1.862 1.178 65.558 14.093 6.152 66.197 14.007 5.218 67.581 12.437 5.166 65.084 10.132 6.237

1.964 1.178 67.057 14.240 6.482 67.640 14.100 5.463 69.278 12.460 5.222 68.081 10.193 6.334

2.066 1.178 70.112 14.578 7.195 70.627 14.324 6.018 71.368 12.513 5.347 76.039 10.389 6.645

2.168 1.178 71.519 14.841 7.714 72.546 14.494 6.413 72.771 12.556 5.446 73.054 10.551 6.896

2.271 1.178 73.506 14.648 7.335 74.238 14.383 6.156 73.929 12.554 5.441 75.065 10.537 6.874

2.373 1.178 76.080 14.434 6.898 76.187 14.220 5.767 75.219 12.507 5.333 75.749 10.337 6.564

2.475 1.178 77.581 14.237 6.477 77.176 14.079 5.408 76.169 12.455 5.211 78.401 10.114 6.206

2.577 1.178 78.287 14.233 6.469 77.895 14.102 5.468 76.612 12.456 5.213 76.110 10.122 6.220

2.679 1.178 79.645 14.586 7.211 78.754 14.357 6.097 77.101 12.490 5.294 77.752 10.250 6.427

55

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.781 1.178 79.937 14.904 7.834 78.482 14.586 6.618 77.140 12.519 5.362 80.284 10.359 6.598

2.883 1.178 74.595 15.204 8.391 74.011 14.755 6.982 74.880 12.507 5.333 77.391 10.325 6.545

2.985 1.178 70.427 14.996 8.008 70.212 14.622 6.697 71.829 12.465 5.235 72.405 10.094 6.174

3.087 1.178 67.307 14.652 7.345 66.822 14.427 6.260 69.249 12.515 5.351 69.904 10.159 6.279

3.189 1.178 64.551 15.075 8.155 63.294 14.674 6.808 67.015 12.675 5.716 61.479 10.423 6.698

3.291 1.178 62.685 15.832 9.482 60.392 15.112 7.708 64.883 13.012 6.430 61.579 11.207 7.862

3.394 1.178 62.388 16.601 10.717 58.431 15.534 8.506 63.362 13.391 7.165 60.216 11.781 8.661

3.496 1.178 63.277 17.292 11.759 57.178 15.874 9.113 62.416 13.789 7.884 57.244 12.135 9.137

3.598 1.178 63.832 17.820 12.522 55.848 16.096 9.493 61.751 14.180 8.550 54.969 12.286 9.336

3.700 1.178 64.618 18.157 12.998 54.879 16.202 9.672 61.171 14.547 9.146 53.230 12.323 9.385

3.802 1.178 65.727 18.336 13.246 54.265 16.232 9.722 60.677 14.892 9.684 51.452 12.324 9.387

3.904 1.178 66.115 18.426 13.371 53.218 16.234 9.726 60.237 15.231 10.198 49.328 12.324 9.387

4.006 1.178 66.054 18.504 13.478 51.927 16.234 9.726 59.945 15.589 10.725 47.196 12.324 9.387

4.108 1.178 65.993 18.587 13.591 50.646 16.234 9.726 59.660 15.994 11.306 45.065 12.324 9.387

127 1.281 66.619 16.668 10.820 52.724 15.573 8.578 59.951 14.093 8.403 57.064 10.683 7.096

229 1.281 66.412 15.934 9.652 56.290 15.239 7.953 60.634 13.575 7.504 65.090 10.620 7.001

331 1.281 66.367 15.760 9.361 58.689 15.154 7.790 60.925 13.390 7.164 65.570 11.157 7.791

433 1.281 66.188 15.653 9.180 60.377 15.083 7.652 61.345 13.248 6.894 65.945 11.458 8.217

535 1.281 66.197 15.545 8.994 61.689 14.986 7.457 61.889 13.069 6.543 65.605 11.331 8.039

637 1.281 64.767 14.926 7.876 61.872 14.601 6.652 62.130 12.792 5.970 64.207 10.554 6.900

739 1.281 61.621 14.411 6.851 60.449 14.291 5.938 62.075 12.607 5.563 62.239 10.111 6.202

841 1.281 59.670 14.619 7.278 59.379 14.418 6.238 62.034 12.545 5.421 58.474 10.006 6.028

943 1.281 58.447 14.995 8.007 59.036 14.644 6.745 62.348 12.607 5.564 58.777 10.242 6.413

1.045 1.281 60.068 14.849 7.729 60.602 14.604 6.658 63.002 12.664 5.691 61.980 10.204 6.352

1.148 1.281 63.573 15.343 8.641 63.679 14.904 7.291 64.241 12.883 6.164 66.197 11.192 7.842

1.250 1.281 67.158 15.777 9.390 66.759 15.138 7.758 65.407 12.989 6.383 69.612 11.759 8.631

1.352 1.281 69.682 15.580 9.054 69.467 14.986 7.459 66.369 12.950 6.303 72.123 11.703 8.555

1.454 1.281 69.791 14.820 7.673 70.079 14.493 6.410 67.232 12.776 5.936 73.721 11.077 7.677

1.556 1.281 68.212 14.040 6.030 68.992 13.995 5.187 67.768 12.517 5.357 73.388 10.050 6.102

1.658 1.281 68.256 14.305 6.624 68.746 14.130 5.540 67.911 12.453 5.205 71.569 10.095 6.176

1.760 1.281 67.912 14.278 6.566 68.425 14.119 5.512 68.223 12.383 5.035 69.242 9.868 5.797

1.862 1.281 68.418 14.085 6.136 68.659 14.005 5.213 69.110 12.405 5.090 69.448 10.041 6.087

1.964 1.281 69.504 14.340 6.701 69.854 14.162 5.621 70.662 12.410 5.102 71.336 10.006 6.030

56

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.066 1.281 72.206 14.322 6.661 72.627 14.191 5.695 72.947 12.430 5.149 75.675 9.999 6.017

2.168 1.281 72.896 14.681 7.402 74.332 14.412 6.226 74.760 12.512 5.345 78.622 10.379 6.630

2.271 1.281 77.200 14.747 7.531 77.972 14.451 6.314 76.656 12.560 5.457 82.373 10.628 7.013

2.373 1.281 80.917 14.634 7.308 81.138 14.354 6.089 78.612 12.506 5.331 84.568 10.335 6.561

2.475 1.281 84.186 14.181 6.352 83.555 14.063 5.367 80.170 12.434 5.160 86.521 9.961 5.954

2.577 1.281 85.526 14.085 6.136 84.743 14.027 5.272 81.062 12.444 5.184 84.834 9.999 6.017

2.679 1.281 85.577 14.806 7.647 84.612 14.488 6.399 81.691 12.480 5.270 87.240 10.128 6.229

2.781 1.281 84.468 15.180 8.347 83.402 14.742 6.954 81.999 12.474 5.255 92.112 10.025 6.060

2.883 1.281 80.775 15.193 8.370 79.946 14.741 6.953 79.243 12.489 5.290 87.910 10.200 6.346

2.985 1.281 77.587 14.636 7.311 76.691 14.389 6.172 75.991 12.423 5.133 81.415 9.890 5.835

3.087 1.281 72.617 14.331 6.681 71.920 14.181 5.669 72.655 12.476 5.260 77.277 10.062 6.122

3.189 1.281 67.894 14.457 6.946 66.977 14.259 5.861 69.577 12.603 5.554 70.458 10.222 6.381

3.291 1.281 63.902 15.214 8.410 62.541 14.714 6.896 66.524 12.918 6.237 59.660 10.965 7.514

3.394 1.281 62.459 16.154 10.010 59.840 15.262 7.998 64.398 13.283 6.961 58.411 11.538 8.328

3.496 1.281 62.833 17.010 11.340 58.327 15.723 8.845 63.212 13.682 7.694 56.397 12.001 8.959

3.598 1.281 63.359 17.651 12.281 57.107 16.022 9.368 62.513 14.059 8.346 54.591 12.237 9.272

3.700 1.281 64.193 18.060 12.862 56.250 16.174 9.626 61.859 14.398 8.906 53.220 12.315 9.375

3.802 1.281 65.179 18.268 13.152 55.706 16.225 9.711 61.167 14.708 9.399 51.584 12.324 9.387

3.904 1.281 66.225 18.362 13.283 55.150 16.231 9.721 60.586 15.012 9.868 49.779 12.324 9.387

4.006 1.281 66.199 18.431 13.377 53.889 16.230 9.719 60.203 15.339 10.359 47.904 12.324 9.387

4.108 1.281 66.136 18.511 13.488 52.610 16.230 9.719 59.838 15.724 10.921 45.773 12.324 9.387

25 1.384 67.344 17.651 12.281 50.989 15.993 9.317 58.743 15.190 10.136 45.015 11.865 8.775

127 1.384 67.073 17.099 11.472 54.347 15.773 8.935 59.886 14.300 8.746 49.271 11.364 8.085

229 1.384 67.060 16.514 10.582 57.757 15.518 8.477 60.707 13.660 7.655 53.550 10.914 7.440

331 1.384 66.880 15.891 9.580 60.580 15.215 7.908 61.206 13.221 6.843 61.063 10.482 6.790

433 1.384 66.455 15.385 8.716 62.461 14.940 7.365 61.896 13.107 6.619 62.000 10.985 7.543

535 1.384 66.892 15.308 8.578 63.856 14.846 7.173 62.656 12.969 6.342 63.723 11.122 7.741

637 1.384 66.236 14.557 7.152 64.303 14.384 6.159 63.098 12.711 5.795 64.502 10.478 6.783

739 1.384 61.880 14.516 7.069 61.676 14.325 6.020 62.752 12.539 5.407 61.631 10.089 6.165

841 1.384 58.673 14.684 7.408 59.673 14.424 6.252 61.691 12.495 5.304 54.319 10.031 6.070

943 1.384 57.984 14.704 7.447 59.721 14.460 6.335 62.460 12.582 5.508 56.427 10.383 6.636

1.045 1.384 61.899 14.898 7.824 63.029 14.595 6.638 64.086 12.718 5.811 63.647 10.840 7.330

57

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

1.148 1.384 66.386 15.285 8.536 66.886 14.830 7.139 65.698 12.839 6.071 69.541 11.280 7.967

1.250 1.384 70.352 15.476 8.875 70.368 14.934 7.354 67.114 12.900 6.200 72.563 11.575 8.379

1.352 1.384 73.506 15.328 8.614 73.529 14.817 7.112 68.250 12.862 6.119 74.840 11.485 8.255

1.454 1.384 74.329 14.762 7.561 74.468 14.438 6.285 69.305 12.716 5.807 75.982 10.950 7.492

1.556 1.384 72.387 14.145 6.272 73.012 14.044 5.316 70.094 12.505 5.328 74.830 10.123 6.221

1.658 1.384 70.056 14.272 6.553 70.937 14.113 5.497 70.085 12.443 5.181 73.068 10.114 6.207

1.760 1.384 69.949 14.332 6.683 70.583 14.158 5.613 70.314 12.389 5.050 70.099 9.960 5.953

1.862 1.384 69.294 14.268 6.544 70.024 14.125 5.528 71.020 12.401 5.079 69.108 10.072 6.138

1.964 1.384 69.323 14.413 6.854 70.437 14.224 5.776 71.960 12.401 5.079 68.281 10.030 6.068

2.066 1.384 70.230 14.623 7.285 72.198 14.361 6.106 72.876 12.410 5.102 67.412 10.009 6.034

2.168 1.384 73.127 14.550 7.137 75.546 14.342 6.062 75.542 12.459 5.219 71.186 10.189 6.328

2.271 1.384 80.044 14.672 7.383 81.369 14.414 6.230 78.809 12.463 5.229 77.357 10.111 6.202

2.373 1.384 83.674 14.741 7.521 84.804 14.434 6.274 81.036 12.488 5.287 82.214 10.212 6.364

2.475 1.384 85.780 14.233 6.467 86.495 14.134 5.552 82.701 12.469 5.243 85.758 10.049 6.100

2.577 1.384 86.810 14.493 7.021 87.329 14.298 5.956 83.795 12.495 5.305 84.087 10.113 6.205

2.679 1.384 87.405 15.097 8.196 87.604 14.685 6.833 84.035 12.554 5.442 84.816 10.358 6.596

2.781 1.384 86.234 15.455 8.838 86.302 14.909 7.302 83.232 12.611 5.571 86.266 10.691 7.108

2.883 1.384 84.346 15.223 8.425 84.120 14.758 6.988 80.913 12.551 5.435 83.949 10.466 6.765

2.985 1.384 81.401 14.337 6.692 80.970 14.205 5.729 77.664 12.449 5.195 78.319 10.014 6.042

3.087 1.384 75.001 14.119 6.212 75.237 14.016 5.243 74.498 12.467 5.240 74.620 10.085 6.159

3.189 1.384 70.658 14.000 5.937 70.506 13.931 5.012 70.959 12.553 5.441 68.886 10.129 6.230

3.291 1.384 64.237 14.408 6.844 64.408 14.211 5.743 67.345 12.753 5.886 58.790 10.331 6.555

3.394 1.384 62.568 15.597 9.084 61.641 14.931 7.348 65.043 13.119 6.644 56.984 11.026 7.603

3.496 1.384 62.893 16.688 10.851 60.263 15.547 8.530 63.842 13.547 7.453 55.929 11.804 8.692

3.598 1.384 63.780 17.464 12.011 59.426 15.939 9.225 63.061 13.911 8.096 54.553 12.173 9.187

3.700 1.384 64.579 17.938 12.689 58.693 16.141 9.570 62.419 14.217 8.611 53.254 12.305 9.362

3.802 1.384 65.153 18.160 13.001 57.964 16.208 9.682 61.666 14.484 9.045 51.706 12.323 9.385

3.904 1.384 66.222 18.232 13.101 57.547 16.211 9.686 60.947 14.742 9.453 49.896 12.316 9.377

4.006 1.384 66.275 18.269 13.153 56.386 16.202 9.672 60.430 15.032 9.899 48.085 12.311 9.370

4.108 1.384 66.202 18.342 13.254 55.118 16.203 9.673 59.918 15.396 10.443 46.282 12.314 9.374

4.210 1.384 66.157 18.456 13.411 53.801 16.212 9.689 59.180 15.864 11.122 44.342 12.321 9.383

25 1.487 67.780 17.931 12.679 52.479 16.102 9.504 58.606 15.422 10.481 46.621 12.131 9.132

127 1.487 67.703 17.538 12.118 55.638 15.968 9.275 59.826 14.583 9.201 50.898 11.839 8.740

58

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

229 1.487 68.004 17.065 11.421 59.025 15.779 8.946 60.916 13.901 8.077 55.207 11.358 8.077

331 1.487 68.245 16.514 10.581 62.080 15.526 8.491 61.777 13.379 7.143 59.363 10.792 7.259

433 1.487 68.099 15.991 9.745 64.197 15.252 7.979 62.734 13.169 6.741 63.313 11.074 7.672

535 1.487 68.119 15.471 8.865 65.622 14.943 7.371 63.703 12.977 6.358 66.120 11.129 7.752

637 1.487 66.504 14.935 7.893 65.340 14.606 6.662 64.396 12.698 5.768 67.508 10.506 6.826

739 1.487 62.997 14.842 7.716 63.220 14.514 6.458 64.052 12.519 5.362 64.573 10.129 6.230

841 1.487 60.668 14.678 7.396 61.910 14.407 6.214 62.964 12.472 5.251 56.588 10.085 6.159

943 1.487 61.890 14.688 7.416 63.301 14.416 6.235 63.965 12.539 5.409 58.913 10.376 6.625

1.045 1.487 65.950 14.957 7.936 66.858 14.584 6.614 66.099 12.640 5.638 67.090 10.715 7.144

1.148 1.487 69.972 15.014 8.041 70.472 14.631 6.716 67.899 12.711 5.795 73.218 10.933 7.467

1.250 1.487 73.291 14.899 7.825 73.484 14.567 6.575 69.196 12.735 5.848 76.071 11.015 7.587

1.352 1.487 75.942 14.895 7.817 76.212 14.537 6.508 70.170 12.706 5.784 77.381 10.940 7.478

1.454 1.487 78.022 14.590 7.220 77.799 14.319 6.007 71.110 12.611 5.573 75.237 10.625 7.009

1.556 1.487 78.763 14.003 5.945 77.962 13.961 5.093 71.861 12.472 5.251 75.471 10.117 6.211

1.658 1.487 72.673 14.392 6.811 73.249 14.178 5.663 71.885 12.431 5.152 74.834 10.127 6.227

1.760 1.487 69.918 14.609 7.258 71.052 14.315 5.997 72.008 12.393 5.059 71.420 10.017 6.047

1.862 1.487 68.919 14.634 7.308 70.342 14.339 6.054 72.605 12.400 5.078 69.072 10.097 6.178

1.964 1.487 69.492 14.591 7.222 71.225 14.330 6.032 73.284 12.399 5.073 66.826 10.069 6.133

2.066 1.487 71.684 14.667 7.374 73.966 14.382 6.155 74.132 12.402 5.082 65.456 10.040 6.085

2.168 1.487 76.962 14.551 7.141 79.141 14.323 6.016 77.004 12.432 5.155 70.199 10.127 6.227

2.271 1.487 82.182 14.551 7.141 84.005 14.332 6.037 80.661 12.445 5.186 76.543 10.063 6.123

2.373 1.487 85.994 14.862 7.755 87.734 14.512 6.452 83.158 12.515 5.353 84.817 10.306 6.515

2.475 1.487 87.941 14.949 7.921 89.311 14.573 6.589 84.769 12.557 5.450 87.174 10.380 6.632

2.577 1.487 88.497 14.966 7.951 89.700 14.616 6.684 85.564 12.606 5.561 83.596 10.532 6.866

2.679 1.487 88.372 15.342 8.640 89.405 14.855 7.190 85.273 12.662 5.688 82.767 10.782 7.245

2.781 1.487 86.961 15.573 9.043 87.935 14.987 7.460 83.990 12.694 5.759 83.925 11.030 7.609

2.883 1.487 85.406 15.315 8.590 85.987 14.814 7.106 81.834 12.597 5.541 82.126 10.644 7.038

2.985 1.487 82.972 14.691 7.421 83.227 14.410 6.221 78.892 12.471 5.249 77.802 10.108 6.197

3.087 1.487 78.734 14.353 6.727 79.016 14.156 5.608 75.668 12.468 5.241 74.213 10.114 6.206

3.189 1.487 74.901 13.942 5.799 74.716 13.897 4.917 72.076 12.541 5.413 69.005 10.171 6.299

3.291 1.487 65.008 14.092 6.150 66.417 14.023 5.262 68.531 12.725 5.826 60.886 10.410 6.679

3.394 1.487 63.885 15.329 8.616 64.152 14.779 7.034 66.013 13.049 6.503 58.396 11.047 7.633

59

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.496 1.487 63.781 16.458 10.494 62.628 15.431 8.315 64.497 13.430 7.237 60.012 11.793 8.678

3.598 1.487 64.387 17.274 11.733 61.783 15.864 9.095 63.451 13.751 7.817 58.318 12.172 9.187

3.700 1.487 65.127 17.773 12.455 61.156 16.097 9.496 62.701 14.010 8.264 56.366 12.302 9.358

3.802 1.487 65.515 17.974 12.740 60.451 16.165 9.610 61.970 14.222 8.618 54.793 12.295 9.348

3.904 1.487 66.221 17.989 12.761 59.927 16.146 9.577 61.258 14.421 8.943 53.011 12.240 9.276

4.006 1.487 66.279 17.966 12.729 58.844 16.115 9.526 60.635 14.661 9.326 51.238 12.201 9.225

4.108 1.487 66.195 18.027 12.815 57.590 16.115 9.525 59.983 15.004 9.856 49.485 12.225 9.256

4.210 1.487 66.160 18.181 13.031 56.248 16.145 9.576 59.218 15.481 10.568 47.316 12.281 9.331

25 1.590 68.328 18.167 13.011 53.230 16.181 9.636 58.376 15.711 10.902 48.243 12.272 9.318

127 1.590 68.298 17.891 12.622 56.126 16.108 9.514 59.826 14.910 9.712 52.555 12.135 9.136

229 1.590 68.852 17.516 12.085 59.428 15.978 9.292 61.238 14.250 8.665 56.034 11.890 8.810

331 1.590 69.444 17.016 11.349 62.591 15.766 8.922 62.464 13.746 7.809 59.586 11.677 8.519

433 1.590 69.569 16.399 10.401 64.922 15.462 8.373 63.632 13.389 7.161 63.399 11.589 8.398

535 1.590 69.372 15.622 9.127 66.406 15.037 7.561 64.740 13.071 6.547 66.515 11.291 7.982

637 1.590 68.080 14.903 7.833 66.511 14.616 6.683 65.839 12.716 5.806 71.202 10.451 6.742

739 1.590 64.415 14.939 7.900 64.368 14.576 6.595 65.859 12.501 5.318 70.219 10.001 6.020

841 1.590 63.298 14.459 6.951 63.924 14.269 5.886 65.163 12.425 5.137 63.575 9.884 5.824

943 1.590 67.452 14.254 6.515 67.288 14.140 5.565 66.288 12.464 5.230 65.899 10.124 6.222

1.045 1.590 71.079 14.609 7.257 70.683 14.349 6.077 68.805 12.440 5.174 72.696 9.829 5.731

1.148 1.590 73.583 14.431 6.893 73.268 14.261 5.868 70.191 12.525 5.376 76.430 10.232 6.398

1.250 1.590 75.138 14.374 6.772 74.939 14.212 5.747 71.230 12.501 5.319 79.002 10.047 6.096

1.352 1.590 75.285 14.448 6.928 75.728 14.233 5.798 71.572 12.502 5.321 75.234 10.116 6.209

1.454 1.590 76.400 14.473 6.980 76.445 14.222 5.772 72.129 12.474 5.256 73.602 10.111 6.202

1.556 1.590 75.920 14.267 6.541 75.640 14.085 5.424 72.950 12.389 5.050 76.639 9.792 5.667

1.658 1.590 73.752 14.124 6.223 73.603 14.010 5.226 73.037 12.409 5.099 75.800 10.068 6.130

1.760 1.590 70.010 14.452 6.937 70.968 14.202 5.722 73.072 12.350 4.953 72.130 9.771 5.630

1.862 1.590 69.290 14.392 6.811 70.607 14.180 5.668 73.904 12.385 5.041 70.839 10.012 6.039

1.964 1.590 70.897 14.364 6.751 72.488 14.180 5.667 75.049 12.377 5.020 69.892 9.954 5.942

2.066 1.590 73.255 14.494 7.024 75.632 14.266 5.878 76.529 12.368 4.999 70.838 9.867 5.795

2.168 1.590 76.324 14.214 6.426 79.322 14.112 5.495 79.623 12.413 5.108 75.773 10.057 6.113

2.271 1.590 84.017 14.311 6.637 85.886 14.161 5.618 83.200 12.396 5.068 81.819 9.772 5.632

2.373 1.590 88.289 14.745 7.528 89.902 14.426 6.257 85.566 12.563 5.462 89.076 10.541 6.881

2.475 1.590 90.349 15.054 8.115 91.440 14.633 6.720 86.799 12.668 5.701 90.557 10.942 7.480

60

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.577 1.590 89.639 15.271 8.512 90.804 14.788 7.052 86.840 12.735 5.848 85.024 11.183 7.828

2.679 1.590 88.091 15.433 8.800 89.184 14.898 7.279 85.882 12.759 5.899 81.765 11.270 7.952

2.781 1.590 86.294 15.448 8.826 87.282 14.901 7.286 84.400 12.719 5.814 82.683 11.137 7.762

2.883 1.590 85.217 15.188 8.363 85.735 14.726 6.921 82.576 12.593 5.531 83.504 10.604 6.977

2.985 1.590 84.389 14.660 7.359 84.309 14.393 6.180 80.172 12.426 5.141 82.466 9.840 5.749

3.087 1.590 81.237 14.607 7.253 81.042 14.317 6.001 76.390 12.450 5.199 77.266 10.040 6.086

3.189 1.590 76.641 14.461 6.955 76.553 14.222 5.770 72.931 12.522 5.369 71.565 10.139 6.248

3.291 1.590 72.097 14.619 7.278 72.105 14.336 6.048 70.019 12.757 5.895 63.799 10.809 7.284

3.394 1.590 67.968 15.334 8.625 67.802 14.790 7.056 67.228 13.021 6.447 58.186 11.338 8.048

3.496 1.590 64.988 16.274 10.203 64.403 15.344 8.153 65.083 13.312 7.017 59.149 11.861 8.769

3.598 1.590 64.394 17.056 11.409 62.916 15.775 8.938 63.614 13.573 7.499 62.336 12.168 9.181

3.700 1.590 65.025 17.534 12.112 62.424 16.017 9.359 62.653 13.778 7.865 61.922 12.261 9.304

3.802 1.590 65.434 17.682 12.325 61.910 16.073 9.454 61.986 13.930 8.128 59.752 12.182 9.199

3.904 1.590 65.938 17.591 12.195 61.411 16.006 9.339 61.395 14.053 8.337 57.923 11.993 8.948

4.006 1.590 66.120 17.463 12.008 60.550 15.923 9.197 60.761 14.225 8.624 56.113 11.850 8.755

4.108 1.590 66.130 17.506 12.071 59.378 15.917 9.187 60.054 14.551 9.151 54.356 11.940 8.876

4.210 1.590 66.112 17.745 12.415 57.999 15.993 9.317 59.273 15.056 9.935 51.037 12.137 9.140

-77 1.692 69.224 18.467 13.427 51.522 16.231 9.721 56.513 16.903 12.560 45.476 12.324 9.387

25 1.692 68.860 18.331 13.239 53.783 16.220 9.702 58.085 16.017 11.339 49.852 12.317 9.378

127 1.692 68.779 18.134 12.964 56.389 16.185 9.644 59.794 15.244 10.217 53.135 12.277 9.325

229 1.692 69.551 17.825 12.529 59.599 16.095 9.491 61.474 14.593 9.218 56.032 12.197 9.219

331 1.692 70.522 17.345 11.837 62.909 15.906 9.168 62.960 14.061 8.350 59.241 12.106 9.099

433 1.692 70.340 16.667 10.819 65.070 15.590 8.608 64.236 13.617 7.579 62.768 11.924 8.855

535 1.692 68.983 15.773 9.383 65.921 15.121 7.726 65.389 13.205 6.811 65.903 11.452 8.208

637 1.692 66.461 14.994 8.004 65.383 14.670 6.801 66.186 12.801 5.990 69.044 10.602 6.974

739 1.692 64.330 14.918 7.861 64.421 14.565 6.571 66.252 12.556 5.447 68.888 10.167 6.292

841 1.692 62.849 14.462 6.957 63.757 14.257 5.857 66.206 12.463 5.230 63.844 10.088 6.164

943 1.692 67.330 14.201 6.398 67.352 14.084 5.422 67.535 12.454 5.207 66.581 10.150 6.264

1.045 1.692 72.658 14.427 6.883 71.905 14.223 5.773 69.719 12.448 5.192 72.060 10.084 6.157

1.148 1.692 75.605 14.415 6.859 74.726 14.213 5.750 71.690 12.467 5.238 77.801 10.152 6.268

1.250 1.692 75.986 14.107 6.184 75.517 14.017 5.246 72.539 12.460 5.222 79.145 10.095 6.176

1.352 1.692 73.250 13.996 5.929 74.052 13.944 5.047 72.014 12.460 5.221 72.329 10.122 6.220

61

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

1.454 1.692 72.811 14.172 6.332 73.578 14.034 5.290 72.167 12.448 5.193 70.270 10.135 6.241

1.556 1.692 71.254 13.885 5.661 72.111 13.858 4.804 73.090 12.419 5.122 72.947 10.069 6.133

1.658 1.692 71.358 13.945 5.806 71.908 13.880 4.869 73.810 12.415 5.113 72.816 10.136 6.243

1.760 1.692 69.940 14.104 6.177 71.237 13.979 5.142 74.693 12.395 5.064 71.867 10.065 6.127

1.862 1.692 71.063 13.876 5.638 72.372 13.860 4.809 76.184 12.401 5.080 72.773 10.115 6.208

1.964 1.692 73.864 14.061 6.081 75.452 13.982 5.153 77.981 12.398 5.073 73.522 10.098 6.180

2.066 1.692 77.838 14.439 6.909 80.025 14.215 5.754 79.897 12.399 5.075 75.730 10.078 6.148

2.168 1.692 84.252 14.409 6.847 86.095 14.195 5.705 83.012 12.420 5.125 81.124 10.138 6.246

2.271 1.692 91.341 14.000 5.937 91.816 13.956 5.080 86.406 12.441 5.178 86.797 10.112 6.203

2.373 1.692 94.083 14.368 6.759 94.360 14.172 5.646 88.228 12.534 5.397 94.861 10.430 6.709

2.475 1.692 93.237 14.480 6.994 93.542 14.285 5.926 88.572 12.630 5.614 94.115 10.742 7.185

2.577 1.692 89.690 14.950 7.922 90.556 14.574 6.591 87.416 12.726 5.828 87.385 11.165 7.803

2.679 1.692 85.223 15.010 8.033 86.513 14.618 6.688 85.669 12.728 5.833 82.402 11.153 7.786

2.781 1.692 83.667 14.885 7.799 84.602 14.539 6.513 83.890 12.636 5.630 81.017 10.731 7.168

2.883 1.692 83.921 14.750 7.537 84.156 14.430 6.266 82.329 12.542 5.414 83.140 10.413 6.682

2.985 1.692 84.555 14.241 6.485 84.037 14.112 5.494 80.207 12.454 5.208 82.791 10.104 6.190

3.087 1.692 81.433 14.352 6.726 81.085 14.161 5.618 76.843 12.453 5.205 79.384 10.139 6.248

3.189 1.692 77.872 14.260 6.527 77.540 14.122 5.521 73.670 12.511 5.343 74.800 10.228 6.391

3.291 1.692 77.249 14.227 6.454 75.870 14.131 5.544 70.933 12.662 5.688 68.109 10.561 6.912

3.394 1.692 70.219 14.987 7.991 69.740 14.598 6.645 67.917 12.887 6.172 61.430 11.092 7.699

3.496 1.692 65.313 15.957 9.688 65.139 15.174 7.828 65.221 13.144 6.693 55.082 11.668 8.507

3.598 1.692 63.575 16.745 10.938 63.055 15.626 8.672 63.338 13.361 7.108 57.529 11.963 8.907

3.700 1.692 63.952 17.214 11.643 62.644 15.883 9.127 62.189 13.528 7.417 59.561 12.049 9.023

3.802 1.692 64.719 17.306 11.780 62.568 15.925 9.200 61.627 13.635 7.612 62.225 11.935 8.869

3.904 1.692 65.145 17.054 11.406 62.204 15.783 8.952 61.261 13.675 7.683 62.849 11.553 8.349

4.006 1.692 65.576 16.706 10.879 61.724 15.589 8.605 60.812 13.730 7.780 60.966 11.098 7.706

4.108 1.692 65.966 16.731 10.917 60.852 15.575 8.580 60.088 14.064 8.356 58.441 11.365 8.087

4.210 1.692 66.036 17.143 11.539 59.445 15.742 8.879 59.308 14.626 9.271 54.734 11.831 8.730

-77 1.795 69.490 18.525 13.506 52.393 16.234 9.726 56.001 17.176 12.924 47.030 12.324 9.387

25 1.795 69.292 18.429 13.375 54.503 16.233 9.723 57.733 16.317 11.759 50.187 12.324 9.387

127 1.795 69.247 18.283 13.173 56.906 16.218 9.698 59.626 15.559 10.682 53.104 12.320 9.381

229 1.795 70.208 18.021 12.806 60.078 16.154 9.591 61.527 14.902 9.700 55.979 12.307 9.364

331 1.795 71.135 17.568 12.161 63.299 15.991 9.313 63.192 14.336 8.805 58.792 12.267 9.311

62

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

433 1.795 70.569 16.896 11.168 65.221 15.692 8.791 64.554 13.833 7.961 61.876 12.081 9.065

535 1.795 68.740 15.994 9.750 65.872 15.232 7.941 65.686 13.360 7.107 65.366 11.598 8.410

637 1.795 66.424 15.097 8.196 65.601 14.724 6.917 66.312 12.893 6.184 67.481 10.654 7.052

739 1.795 64.769 14.720 7.478 65.080 14.462 6.341 66.431 12.598 5.542 65.976 10.125 6.224

841 1.795 62.289 14.512 7.061 63.809 14.287 5.930 66.586 12.462 5.227 61.664 9.929 5.901

943 1.795 63.203 14.110 6.191 65.020 14.042 5.313 68.200 12.461 5.224 64.428 10.128 6.229

1.045 1.795 69.338 14.539 7.117 70.170 14.291 5.938 70.134 12.412 5.105 69.402 9.841 5.751

1.148 1.795 74.633 14.436 6.902 74.494 14.237 5.808 72.442 12.457 5.214 76.364 10.124 6.222

1.250 1.795 76.584 14.350 6.720 76.256 14.167 5.634 73.415 12.426 5.141 78.070 9.925 5.893

1.352 1.795 72.375 14.377 6.779 73.825 14.173 5.650 71.991 12.449 5.197 68.511 10.054 6.108

1.454 1.795 70.335 14.335 6.690 72.220 14.137 5.558 72.077 12.449 5.194 66.067 10.110 6.200

1.556 1.795 71.574 14.151 6.286 72.801 14.010 5.228 73.381 12.382 5.034 69.537 9.817 5.711

1.658 1.795 74.292 14.132 6.243 74.921 13.997 5.191 75.058 12.406 5.091 70.128 10.069 6.133

1.760 1.795 74.651 14.189 6.370 76.064 14.033 5.290 76.930 12.352 4.960 72.710 9.793 5.669

1.862 1.795 71.687 10.942 72.394 11.028 79.598 12.385 5.041 77.556 10.017 6.047

1.964 1.795 81.731 14.108 6.187 83.015 14.010 5.228 82.028 12.375 5.017 80.720 9.958 5.948

2.066 1.795 85.782 14.352 6.725 87.504 14.167 5.636 83.714 12.365 4.991 82.251 9.879 5.815

2.168 1.795 92.499 14.363 6.749 93.538 14.176 5.656 86.445 12.401 5.080 86.890 10.057 6.113

2.271 1.795 97.194 14.120 6.215 97.366 14.011 5.229 89.500 12.371 5.006 91.870 9.753 5.598

2.373 1.795 103.076 13.967 5.859 101.269 13.928 5.004 90.053 12.462 5.228 96.811 10.118 6.213

2.475 1.795 95.150 14.127 6.230 94.972 14.057 5.353 89.487 12.501 5.319 96.376 10.116 6.210

2.577 1.795 86.384 14.532 7.102 87.981 14.305 5.973 86.743 12.633 5.623 87.448 10.791 7.258

2.679 1.795 79.513 14.391 6.809 81.871 14.222 5.771 84.103 12.625 5.603 81.246 10.743 7.186

2.781 1.795 78.642 14.245 6.494 80.237 14.120 5.515 81.943 12.476 5.259 78.374 9.979 5.985

2.883 1.795 80.364 14.401 6.830 80.951 14.188 5.688 80.766 12.461 5.225 79.617 10.121 6.217

2.985 1.795 82.923 14.052 6.058 82.347 13.972 5.126 79.360 12.383 5.036 81.776 9.802 5.684

3.087 1.795 79.052 14.159 6.304 79.131 14.037 5.300 76.275 12.417 5.118 79.311 10.027 6.064

3.189 1.795 75.792 14.441 6.913 75.945 14.212 5.747 73.403 12.440 5.175 74.942 9.987 5.997

3.291 1.795 72.725 14.490 7.015 72.825 14.264 5.874 70.708 12.506 5.331 69.609 9.984 5.992

3.394 1.795 67.525 14.567 7.172 68.040 14.354 6.089 67.476 12.705 5.782 62.232 10.564 6.916

3.496 1.795 63.451 15.494 8.905 64.247 14.911 7.307 64.524 12.930 6.260 55.094 11.162 7.799

3.598 1.795 61.586 16.336 10.301 62.300 15.411 8.278 62.358 13.109 6.624 51.213 11.424 8.169

63

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.700 1.795 62.146 16.852 11.101 62.214 15.711 8.824 61.136 13.267 6.929 53.316 11.562 8.361

3.802 1.795 63.366 16.956 11.259 62.582 15.771 8.931 60.829 13.390 7.163 56.229 11.608 8.425

3.904 1.795 63.964 16.575 10.676 62.553 15.567 8.566 60.789 13.412 7.204 59.775 11.253 7.928

4.006 1.795 64.879 15.763 9.366 62.672 15.140 7.763 60.710 13.319 7.030 63.402 10.283 6.478

4.108 1.795 65.622 15.816 9.454 62.108 15.139 7.761 60.015 13.690 7.710 62.145 10.827 7.312

4.210 1.795 65.974 16.481 10.529 60.773 15.440 8.333 59.244 14.256 8.674 57.991 11.373 8.098

4.312 1.795 66.149 17.298 11.767 59.073 15.792 8.968 58.250 15.038 9.907 45.802 11.835 8.735

-77 1.898 69.655 18.563 13.558 52.692 16.234 9.726 55.364 17.444 13.278 47.136 12.324 9.387

25 1.898 69.580 18.480 13.444 54.804 16.234 9.726 57.258 16.601 12.149 50.017 12.324 9.387

127 1.898 69.671 18.364 13.285 57.145 16.228 9.715 59.291 15.848 11.098 52.899 12.324 9.387

229 1.898 70.690 18.139 12.972 60.267 16.183 9.640 61.338 15.180 10.121 55.769 12.324 9.387

331 1.898 71.426 17.726 12.388 63.318 16.046 9.409 63.154 14.583 9.202 58.935 12.303 9.359

433 1.898 70.534 17.098 11.471 65.037 15.780 8.946 64.650 14.038 8.311 62.882 12.156 9.165

535 1.898 69.073 16.276 10.205 65.957 15.369 8.201 65.886 13.531 7.424 66.741 11.776 8.654

637 1.898 67.569 15.413 8.765 66.341 14.887 7.257 66.704 13.081 6.568 69.818 11.176 7.819

739 1.898 66.224 14.810 7.654 66.262 14.511 6.450 67.099 12.756 5.893 67.921 10.661 7.063

841 1.898 64.792 14.564 7.167 65.931 14.333 6.039 67.814 12.545 5.421 62.301 10.186 6.322

943 1.898 65.084 14.474 6.982 66.923 14.272 5.895 69.189 12.524 5.372 64.437 10.358 6.596

1.045 1.898 68.420 14.743 7.524 70.088 14.422 6.248 70.789 12.546 5.423 70.886 10.549 6.893

1.148 1.898 72.549 14.658 7.356 73.468 14.376 6.140 72.190 12.512 5.345 72.420 10.350 6.585

1.250 1.898 74.226 14.446 6.924 74.964 14.259 5.861 72.754 12.491 5.294 72.849 10.192 6.333

1.352 1.898 72.707 14.724 7.486 74.500 14.402 6.202 72.642 12.557 5.450 68.887 10.573 6.929

1.454 1.898 71.485 14.556 7.150 73.673 14.283 5.920 73.042 12.541 5.411 66.728 10.538 6.876

1.556 1.898 71.405 13.811 5.475 73.458 13.836 4.741 74.391 12.444 5.183 68.032 10.100 6.183

1.658 1.898 79.531 14.209 6.414 80.010 14.058 5.355 76.775 12.422 5.130 70.481 10.120 6.216

1.760 1.898 82.166 14.251 6.508 83.270 14.098 5.460 79.699 12.388 5.049 75.603 10.003 6.025

1.862 1.898 88.275 14.336 6.690 88.745 14.139 5.563 83.861 12.397 5.071 85.344 10.087 6.163

1.964 1.898 92.742 14.289 6.591 92.917 14.117 5.509 87.056 12.392 5.057 91.680 10.057 6.113

2.066 1.898 94.044 14.356 6.734 94.885 14.172 5.647 88.662 12.388 5.049 94.211 10.029 6.066

2.168 1.898 95.012 14.534 7.106 96.421 14.274 5.899 89.232 12.408 5.096 92.617 10.108 6.197

2.271 1.898 95.855 14.332 6.683 96.972 14.159 5.614 89.658 12.404 5.086 89.581 9.997 6.015

2.373 1.898 97.403 14.370 6.763 97.380 14.183 5.675 89.447 12.449 5.196 93.696 10.131 6.234

2.475 1.898 90.146 14.527 7.091 91.213 14.295 5.949 87.887 12.471 5.249 92.357 10.117 6.211

64

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.577 1.898 80.697 14.492 7.018 83.539 14.295 5.950 84.572 12.511 5.343 80.718 10.272 6.461

2.679 1.898 77.268 14.389 6.805 79.617 14.220 5.766 81.363 12.505 5.328 73.797 10.277 6.469

2.781 1.898 74.885 14.209 6.414 76.776 14.096 5.453 79.062 12.446 5.188 73.189 10.060 6.119

2.883 1.898 74.984 14.339 6.698 76.278 14.159 5.615 77.800 12.434 5.159 73.982 10.121 6.218

2.985 1.898 76.098 14.206 6.409 76.677 14.078 5.408 76.501 12.402 5.082 75.486 10.008 6.033

3.087 1.898 74.207 14.123 6.222 74.935 14.031 5.282 74.408 12.418 5.122 75.760 10.093 6.172

3.189 1.898 71.586 14.265 6.538 72.420 14.125 5.528 71.789 12.438 5.168 72.141 10.071 6.136

3.291 1.898 67.621 14.466 6.965 68.915 14.253 5.846 68.518 12.481 5.273 64.127 10.074 6.141

3.394 1.898 63.865 14.243 6.490 65.161 14.162 5.623 65.646 12.584 5.511 63.217 10.276 6.467

3.496 1.898 60.498 15.062 8.131 62.062 14.669 6.798 63.186 12.685 5.739 57.160 10.333 6.557

3.598 1.898 58.975 15.885 9.569 60.513 15.171 7.824 60.806 12.845 6.084 46.445 10.576 6.933

3.700 1.898 60.103 16.510 10.576 60.871 15.538 8.512 59.427 13.008 6.420 46.285 10.734 7.172

3.802 1.898 61.561 16.779 10.991 61.463 15.689 8.785 59.689 13.252 6.902 50.106 11.358 8.076

3.904 1.898 63.016 16.538 10.619 62.102 15.556 8.546 60.070 13.377 7.139 54.725 11.451 8.207

4.006 1.898 64.456 15.933 9.649 62.573 15.228 7.934 60.054 13.437 7.250 59.008 11.117 7.734

4.108 1.898 65.397 15.513 8.940 62.370 14.998 7.482 59.656 13.591 7.532 63.344 10.784 7.248

4.210 1.898 66.010 16.131 9.972 61.199 15.279 8.031 59.073 13.967 8.191 61.220 10.660 7.062

4.312 1.898 66.262 17.154 11.554 59.434 15.733 8.863 57.914 14.848 9.616 49.050 11.457 8.215

-77 2.001 69.820 18.606 13.617 51.636 16.234 9.726 54.578 17.701 13.614 46.855 12.324 9.387

25 2.001 69.758 18.523 13.504 53.752 16.234 9.726 56.601 16.870 12.514 50.611 12.324 9.387

127 2.001 69.971 18.418 13.359 56.151 16.230 9.719 58.735 16.117 11.479 54.367 12.324 9.387

229 2.001 70.747 18.221 13.086 59.059 16.197 9.664 60.894 15.436 10.502 58.129 12.324 9.387

331 2.001 71.479 17.851 12.566 62.042 16.085 9.476 62.827 14.814 9.564 61.969 12.304 9.360

433 2.001 70.644 17.254 11.702 63.873 15.840 9.052 64.466 14.232 8.635 65.817 12.160 9.170

535 2.001 69.302 16.413 10.423 64.984 15.431 8.315 65.955 13.683 7.696 69.258 11.793 8.677

637 2.001 68.442 15.403 8.747 66.082 14.884 7.252 67.075 13.190 6.782 72.099 11.203 7.857

739 2.001 66.900 14.684 7.408 66.108 14.450 6.312 67.581 12.827 6.046 69.963 10.681 7.093

841 2.001 66.013 14.502 7.039 66.347 14.316 5.998 68.159 12.581 5.505 63.286 10.163 6.286

943 2.001 67.261 14.735 7.508 68.158 14.427 6.260 69.422 12.546 5.424 65.342 10.360 6.599

1.045 2.001 69.728 14.763 7.563 70.692 14.424 6.253 70.896 12.561 5.459 71.737 10.568 6.922

1.148 2.001 71.991 14.615 7.269 72.802 14.335 6.044 71.871 12.523 5.371 71.790 10.358 6.596

1.250 2.001 73.247 14.481 6.996 74.033 14.276 5.904 72.433 12.511 5.342 72.697 10.236 6.404

65

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

1.352 2.001 73.444 14.842 7.716 74.942 14.487 6.395 73.085 12.609 5.569 71.279 10.791 7.258

1.454 2.001 74.780 14.817 7.667 76.267 14.454 6.322 74.208 12.589 5.524 70.469 10.738 7.179

1.556 2.001 77.540 14.304 6.622 78.618 14.138 5.560 76.110 12.460 5.222 72.022 10.124 6.223

1.658 2.001 84.382 14.320 6.658 84.384 14.135 5.553 78.928 12.427 5.144 75.636 10.116 6.209

1.760 2.001 89.341 14.431 6.893 89.395 14.200 5.717 82.327 12.386 5.043 82.196 9.972 5.972

1.862 2.001 97.277 14.362 6.747 96.070 14.155 5.603 86.954 12.396 5.068 93.554 10.075 6.143

1.964 2.001 100.852 14.184 6.358 99.352 14.047 5.327 89.909 12.390 5.052 99.661 10.039 6.084

2.066 2.001 96.745 14.245 6.494 96.871 14.093 5.446 90.462 12.385 5.041 99.311 10.002 6.022

2.168 2.001 92.726 14.452 6.937 94.191 14.225 5.779 89.431 12.408 5.097 93.628 10.097 6.180

2.271 2.001 90.302 14.380 6.784 91.932 14.203 5.725 88.273 12.405 5.090 87.472 9.996 6.011

2.373 2.001 90.505 14.572 7.182 91.195 14.330 6.033 87.249 12.464 5.232 91.065 10.232 6.397

2.475 2.001 85.377 14.626 7.293 86.355 14.397 6.189 85.439 12.470 5.246 89.363 10.182 6.316

2.577 2.001 79.204 14.965 7.950 81.101 14.585 6.615 82.119 12.460 5.222 79.745 10.133 6.237

2.679 2.001 75.179 14.926 7.876 76.985 14.549 6.535 78.562 12.443 5.181 72.245 10.106 6.194

2.781 2.001 71.033 14.773 7.582 72.994 14.443 6.296 75.759 12.406 5.093 69.553 9.980 5.985

2.883 2.001 69.080 14.596 7.231 70.786 14.334 6.043 74.581 12.415 5.115 70.215 10.110 6.199

2.985 2.001 68.543 14.402 6.830 70.039 14.229 5.789 73.721 12.391 5.055 72.655 9.976 5.979

3.087 2.001 70.529 14.525 7.088 71.054 14.289 5.935 72.294 12.417 5.120 74.440 10.087 6.162

3.189 2.001 69.108 14.552 7.142 69.526 14.309 5.982 69.981 12.445 5.187 71.863 10.115 6.209

3.291 2.001 64.173 14.506 7.047 65.542 14.299 5.959 66.713 12.493 5.301 62.259 10.183 6.318

3.394 2.001 60.787 14.649 7.338 62.297 14.403 6.205 64.121 12.556 5.447 62.497 10.258 6.438

3.496 2.001 57.921 15.061 8.130 59.461 14.684 6.832 62.030 12.597 5.541 64.724 10.063 6.123

3.598 2.001 56.259 15.353 8.658 57.634 14.909 7.303 59.815 12.770 5.923 52.192 10.352 6.588

3.700 2.001 58.371 16.315 10.267 58.470 15.439 8.330 58.490 12.973 6.350 43.984 10.629 7.014

3.802 2.001 60.440 16.833 11.073 59.415 15.712 8.826 58.910 13.287 6.969 49.320 11.466 8.227

3.904 2.001 62.854 16.868 11.126 60.579 15.717 8.836 59.312 13.497 7.361 54.228 11.786 8.668

4.006 2.001 64.514 16.617 10.742 61.035 15.568 8.568 59.338 13.633 7.608 58.655 11.633 8.459

4.108 2.001 65.653 16.421 10.436 60.831 15.446 8.344 58.940 13.765 7.842 62.975 11.111 7.725

4.210 2.001 66.324 16.732 10.918 59.845 15.568 8.567 58.386 14.076 8.375 63.754 10.690 7.107

4.312 2.001 66.518 17.395 11.909 58.224 15.841 9.055 57.153 14.943 9.762 52.333 11.448 8.202

-77 2.104 69.984 18.654 13.683 50.085 16.234 9.726 53.607 17.949 13.935 47.557 12.324 9.387

25 2.104 69.924 18.570 13.568 52.202 16.234 9.726 55.736 17.127 12.859 51.664 12.324 9.387

127 2.104 70.220 18.472 13.433 54.641 16.232 9.722 57.916 16.375 11.839 55.771 12.324 9.387

66

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

229 2.104 70.716 18.298 13.194 57.362 16.207 9.681 60.152 15.686 10.866 59.900 12.324 9.386

331 2.104 71.028 17.975 12.742 60.021 16.119 9.532 62.166 15.046 9.920 64.322 12.295 9.349

433 2.104 70.632 17.432 11.963 62.193 15.906 9.167 63.925 14.437 8.969 68.576 12.129 9.129

535 2.104 69.446 16.627 10.756 63.560 15.528 8.495 65.640 13.842 7.975 72.013 11.683 8.527

637 2.104 68.185 15.557 9.015 64.626 14.974 7.433 67.190 13.244 6.886 74.785 10.773 7.230

739 2.104 64.853 14.846 7.724 63.726 14.563 6.568 67.693 12.839 6.072 73.256 10.236 6.404

841 2.104 64.035 14.734 7.506 64.247 14.472 6.362 67.269 12.620 5.592 66.098 10.021 6.053

943 2.104 67.452 14.837 7.707 67.639 14.491 6.405 68.744 12.551 5.435 68.748 10.180 6.314

1.045 2.104 71.330 14.193 6.379 71.221 14.086 5.427 70.701 12.465 5.234 74.004 9.926 5.895

1.148 2.104 72.139 14.140 6.261 72.444 14.033 5.289 71.404 12.487 5.286 75.445 10.157 6.276

1.250 2.104 73.407 14.266 6.540 73.905 14.142 5.570 71.890 12.486 5.284 76.092 10.108 6.197

1.352 2.104 75.258 14.841 7.714 76.133 14.489 6.401 72.963 12.604 5.556 77.129 10.763 7.216

1.454 2.104 78.776 14.864 7.759 79.273 14.496 6.417 74.960 12.586 5.515 78.009 10.708 7.134

1.556 2.104 83.340 14.429 6.888 83.261 14.223 5.775 78.053 12.438 5.168 82.080 9.978 5.982

1.658 2.104 88.034 14.214 6.426 87.532 14.083 5.421 80.763 12.423 5.133 85.585 10.086 6.161

1.760 2.104 91.041 14.190 6.373 90.919 14.062 5.364 83.995 12.366 4.994 90.186 9.840 5.749

1.862 2.104 98.188 14.237 6.476 96.776 14.069 5.383 87.991 12.390 5.053 100.441 10.030 6.069

1.964 2.104 103.390 13.838 5.545 100.885 13.836 4.740 89.784 12.380 5.028 103.882 9.976 5.979

2.066 2.104 93.776 14.168 6.322 93.831 14.026 5.269 88.351 12.371 5.005 98.031 9.906 5.862

2.168 2.104 87.072 14.322 6.661 88.634 14.132 5.546 86.657 12.404 5.088 92.059 10.066 6.128

2.271 2.104 83.681 14.208 6.413 85.375 14.098 5.458 85.096 12.391 5.055 86.248 9.872 5.804

2.373 2.104 83.057 14.685 7.410 84.127 14.395 6.186 83.891 12.508 5.336 85.233 10.472 6.775

2.475 2.104 79.115 15.001 8.017 80.285 14.607 6.665 82.274 12.536 5.402 83.352 10.587 6.951

2.577 2.104 75.111 15.057 8.123 76.596 14.651 6.759 79.851 12.454 5.208 83.145 10.135 6.241

2.679 2.104 72.445 14.838 7.708 73.631 14.515 6.461 76.059 12.422 5.130 76.600 10.048 6.098

2.781 2.104 67.036 14.562 7.162 68.886 14.342 6.061 72.044 12.375 5.015 69.163 9.841 5.751

2.883 2.104 66.240 14.475 6.984 67.487 14.289 5.934 71.399 12.406 5.091 71.731 10.080 6.150

2.985 2.104 66.854 14.728 7.495 67.655 14.430 6.266 71.435 12.369 5.002 76.302 9.841 5.751

3.087 2.104 68.335 14.792 7.619 68.488 14.466 6.348 70.209 12.415 5.114 78.483 10.047 6.097

3.189 2.104 68.117 14.554 7.147 67.948 14.341 6.060 68.324 12.460 5.222 75.253 10.142 6.252

3.291 2.104 63.554 14.737 7.512 64.403 14.451 6.314 65.483 12.578 5.498 60.216 10.607 6.981

3.394 2.104 60.774 14.934 7.892 61.640 14.595 6.637 63.101 12.621 5.595 61.947 10.531 6.865

67

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.496 2.104 58.145 15.321 8.602 58.802 14.853 7.187 61.376 12.621 5.594 69.099 10.024 6.059

3.598 2.104 57.190 15.790 9.412 57.157 15.146 7.775 59.614 12.896 6.191 48.807 10.788 7.253

3.700 2.104 58.448 16.540 10.621 57.145 15.552 8.539 58.682 13.187 6.777 47.848 11.377 8.104

3.802 2.104 60.911 17.093 11.464 58.062 15.830 9.034 58.580 13.475 7.321 53.315 11.853 8.759

3.904 2.104 63.557 17.299 11.769 59.065 15.913 9.180 58.642 13.724 7.769 58.396 12.082 9.067

4.006 2.104 65.040 17.280 11.740 59.194 15.878 9.119 58.459 13.938 8.142 62.423 12.019 8.982

4.108 2.104 66.152 17.260 11.712 58.859 15.839 9.051 57.893 14.186 8.560 62.034 11.760 8.633

4.210 2.104 66.752 17.440 11.976 57.899 15.889 9.138 57.132 14.608 9.241 61.726 11.629 8.453

4.312 2.104 66.858 17.802 12.496 56.397 16.011 9.349 55.967 15.318 10.328 54.127 11.854 8.761

-77 2.206 70.149 18.707 13.755 48.270 16.234 9.726 52.410 18.188 14.242 47.460 12.324 9.387

25 2.206 70.087 18.622 13.639 50.386 16.234 9.726 54.648 17.378 13.191 51.567 12.324 9.387

127 2.206 70.290 18.529 13.512 52.750 16.233 9.724 56.831 16.631 12.191 55.673 12.324 9.387

229 2.206 70.661 18.385 13.313 55.356 16.217 9.697 59.074 15.943 11.233 59.795 12.324 9.387

331 2.206 70.667 18.124 12.951 57.787 16.156 9.594 61.099 15.299 10.300 64.194 12.300 9.355

433 2.206 70.335 17.691 12.339 59.961 16.002 9.334 62.932 14.688 9.367 67.372 12.158 9.167

535 2.206 69.560 17.072 11.432 61.680 15.734 8.865 64.691 14.096 8.409 68.862 11.790 8.673

637 2.206 67.701 16.338 10.305 62.448 15.379 8.219 66.145 13.532 7.426 70.467 11.159 7.795

739 2.206 64.805 15.650 9.175 62.091 15.021 7.528 66.707 13.126 6.657 69.756 10.793 7.260

841 2.206 64.276 15.404 8.749 63.076 14.873 7.229 66.857 12.870 6.136 65.136 10.619 7.000

943 2.206 67.270 15.180 8.348 66.354 14.709 6.885 68.237 12.690 5.749 67.278 10.439 6.723

1.045 2.206 70.851 14.300 6.615 69.986 14.172 5.647 69.994 12.543 5.416 71.400 10.137 6.243

1.148 2.206 70.563 14.071 6.103 70.717 14.011 5.229 70.508 12.501 5.318 72.166 10.158 6.279

1.250 2.206 73.423 14.389 6.804 73.422 14.218 5.762 70.811 12.496 5.308 71.584 10.188 6.327

1.352 2.206 76.301 14.768 7.572 76.508 14.459 6.333 71.891 12.550 5.434 71.523 10.487 6.797

1.454 2.206 80.710 14.705 7.450 80.443 14.419 6.240 74.366 12.536 5.402 73.863 10.464 6.761

1.556 2.206 85.174 14.125 6.225 84.502 14.068 5.381 77.750 12.456 5.213 80.567 10.141 6.250

1.658 2.206 85.795 14.385 6.796 85.805 14.179 5.664 80.615 12.429 5.148 84.044 10.140 6.249

1.760 2.206 86.519 14.115 6.204 87.096 14.009 5.224 83.201 12.401 5.079 86.052 10.061 6.120

1.862 2.206 89.250 14.166 6.318 89.375 14.014 5.237 85.628 12.405 5.089 91.566 10.114 6.207

1.964 2.206 92.016 14.138 6.256 91.059 13.987 5.165 86.139 12.400 5.078 92.268 10.094 6.174

2.066 2.206 85.232 14.044 6.039 85.681 13.942 5.043 84.436 12.398 5.073 87.228 10.073 6.140

2.168 2.206 79.885 14.183 6.357 81.233 14.029 5.279 82.570 12.415 5.113 80.625 10.131 6.235

2.271 2.206 79.083 14.103 6.175 79.792 14.013 5.235 80.967 12.426 5.140 75.423 10.108 6.197

68

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.373 2.206 78.154 14.437 6.904 78.557 14.243 5.823 79.985 12.492 5.299 79.148 10.392 6.650

2.475 2.206 73.992 14.863 7.756 74.984 14.520 6.471 78.649 12.512 5.346 78.528 10.446 6.735

2.577 2.206 70.775 14.716 7.470 72.125 14.459 6.334 76.282 12.469 5.244 77.141 10.223 6.382

2.679 2.206 69.351 14.545 7.128 70.242 14.341 6.059 72.783 12.440 5.175 70.404 10.140 6.248

2.781 2.206 63.883 14.298 6.609 65.645 14.177 5.659 69.348 12.415 5.114 65.697 10.066 6.127

2.883 2.206 63.615 14.574 7.187 64.883 14.319 6.007 68.193 12.420 5.126 66.464 10.144 6.256

2.985 2.206 65.166 14.811 7.656 65.687 14.459 6.333 67.826 12.408 5.096 70.008 10.066 6.129

3.087 2.206 66.343 14.800 7.635 66.397 14.458 6.330 66.892 12.431 5.152 72.457 10.136 6.243

3.189 2.206 66.255 14.460 6.954 66.110 14.274 5.897 65.437 12.475 5.258 71.386 10.227 6.389

3.291 2.206 63.934 14.719 7.477 64.128 14.442 6.294 63.436 12.570 5.478 59.897 10.497 6.813

3.394 2.206 62.084 14.917 7.860 61.974 14.613 6.678 61.620 12.667 5.697 61.786 10.618 6.998

3.496 2.206 60.005 15.556 9.013 59.372 15.002 7.490 60.212 12.799 5.986 68.048 10.694 7.112

3.598 2.206 59.362 16.285 10.220 57.595 15.410 8.278 59.047 13.077 6.559 47.959 11.271 7.954

3.700 2.206 60.301 16.925 11.212 56.980 15.741 8.878 58.455 13.401 7.184 53.007 11.814 8.706

3.802 2.206 62.493 17.415 11.938 57.311 15.969 9.276 58.130 13.715 7.754 57.766 12.132 9.133

3.904 2.206 64.756 17.682 12.324 57.702 16.070 9.450 57.878 14.009 8.262 60.503 12.257 9.299

4.006 2.206 65.906 17.776 12.460 57.348 16.084 9.473 57.433 14.300 8.747 60.324 12.238 9.274

4.108 2.206 66.791 17.826 12.530 56.783 16.074 9.457 56.696 14.642 9.296 59.977 12.148 9.154

4.210 2.206 67.187 17.947 12.702 55.725 16.092 9.487 55.805 15.116 10.025 59.668 12.107 9.100

4.312 2.206 67.210 18.153 12.992 54.290 16.140 9.567 54.574 15.778 10.998 55.914 12.173 9.188

4.414 2.206 67.258 18.391 13.322 52.788 16.189 9.649 53.149 16.618 12.173 44.262 12.268 9.313

-77 2.309 70.313 18.765 13.833 46.308 16.234 9.726 50.957 18.421 14.538 47.362 12.324 9.387

25 2.309 70.252 18.679 13.717 48.424 16.234 9.726 53.294 17.626 13.516 49.811 12.324 9.387

127 2.309 70.291 18.592 13.599 50.671 16.234 9.725 55.493 16.890 12.542 52.133 12.324 9.387

229 2.309 70.545 18.476 13.439 53.161 16.227 9.713 57.664 16.211 11.612 54.454 12.324 9.387

331 2.309 70.374 18.285 13.175 55.418 16.193 9.657 59.639 15.580 10.713 56.945 12.315 9.375

433 2.309 69.821 17.984 12.754 57.390 16.105 9.508 61.461 14.989 9.833 59.618 12.246 9.283

535 2.309 69.398 17.570 12.164 59.303 15.951 9.245 63.158 14.438 8.971 61.161 12.067 9.047

637 2.309 68.589 17.102 11.477 60.817 15.753 8.900 64.468 13.947 8.157 62.845 11.822 8.717

739 2.309 67.292 16.667 10.819 61.658 15.554 8.543 65.177 13.551 7.460 63.090 11.632 8.458

841 2.309 66.671 16.347 10.318 62.823 15.393 8.246 65.906 13.237 6.873 62.642 11.409 8.148

943 2.309 67.359 15.953 9.683 64.758 15.165 7.812 67.249 12.941 6.284 65.859 10.904 7.425

69

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

1.045 2.309 69.402 15.340 8.636 67.460 14.802 7.081 69.311 12.602 5.553 69.748 9.839 5.747

1.148 2.309 70.449 14.921 7.867 69.402 14.552 6.542 68.852 12.555 5.445 66.812 10.119 6.215

1.250 2.309 71.566 14.841 7.714 71.230 14.513 6.455 68.844 12.473 5.254 65.517 9.908 5.864

1.352 2.309 73.153 14.837 7.707 73.626 14.521 6.474 69.994 12.488 5.288 63.515 10.075 6.142

1.454 2.309 77.096 14.798 7.631 77.259 14.488 6.398 72.708 12.479 5.267 67.370 10.132 6.236

1.556 2.309 81.328 14.542 7.122 81.109 14.319 6.006 76.263 12.390 5.054 75.564 9.767 5.623

1.658 2.309 82.420 14.496 7.028 82.518 14.261 5.867 78.832 12.414 5.111 77.898 10.062 6.121

1.760 2.309 81.738 14.161 6.308 82.559 14.043 5.315 80.471 12.349 4.951 77.088 9.735 5.568

1.862 2.309 83.314 13.973 5.873 83.623 13.911 4.954 81.582 12.388 5.048 77.851 10.007 6.030

1.964 2.309 82.886 13.891 5.676 82.737 13.847 4.773 81.444 12.377 5.019 76.949 9.941 5.921

2.066 2.309 75.454 13.970 5.866 76.683 13.891 4.900 79.839 12.362 4.985 72.878 9.849 5.765

2.168 2.309 70.407 13.943 5.801 72.312 13.872 4.844 78.467 12.399 5.074 68.046 10.046 6.095

2.271 2.309 70.999 14.128 6.233 72.264 14.002 5.206 76.734 12.346 4.945 64.733 9.646 5.411

2.373 2.309 71.186 14.167 6.321 72.203 14.079 5.409 76.402 12.437 5.166 71.293 10.098 6.180

2.475 2.309 70.416 14.608 7.257 71.328 14.368 6.122 75.174 12.429 5.147 74.880 9.931 5.903

2.577 2.309 68.625 14.708 7.455 69.822 14.448 6.307 72.713 12.455 5.210 68.840 10.039 6.084

2.679 2.309 66.266 14.672 7.383 67.559 14.408 6.215 69.649 12.457 5.214 63.389 10.082 6.154

2.781 2.309 63.260 14.197 6.388 64.643 14.111 5.493 66.568 12.389 5.050 60.531 9.753 5.599

2.883 2.309 63.035 14.270 6.548 63.875 14.125 5.528 65.204 12.428 5.145 60.978 10.067 6.129

2.985 2.309 63.421 14.378 6.781 63.944 14.189 5.690 64.079 12.370 5.004 62.532 9.739 5.575

3.087 2.309 64.026 14.446 6.923 64.329 14.242 5.821 63.218 12.421 5.128 65.059 10.015 6.044

3.189 2.309 65.850 14.128 6.233 65.402 14.078 5.406 62.120 12.439 5.171 64.056 9.978 5.982

3.291 2.309 63.956 14.680 7.399 63.721 14.421 6.247 60.780 12.486 5.284 58.187 9.950 5.936

3.394 2.309 62.530 15.299 8.562 61.804 14.822 7.123 59.653 12.669 5.702 59.596 10.444 6.731

3.496 2.309 61.474 15.853 9.517 59.658 15.167 7.816 58.796 12.922 6.245 65.327 10.993 7.554

3.598 2.309 61.489 16.524 10.597 58.003 15.536 8.510 58.131 13.238 6.875 54.083 11.509 8.288

3.700 2.309 62.439 17.160 11.563 56.931 15.846 9.063 57.735 13.603 7.553 58.328 11.981 8.932

3.802 2.309 64.161 17.645 12.271 56.540 16.054 9.422 57.331 13.965 8.188 58.811 12.236 9.271

3.904 2.309 65.922 17.943 12.697 56.263 16.159 9.599 56.870 14.316 8.773 58.526 12.315 9.375

4.006 2.309 66.881 18.088 12.901 55.556 16.188 9.648 56.223 14.676 9.348 58.195 12.314 9.373

4.108 2.309 67.437 18.166 13.010 54.678 16.187 9.648 55.344 15.086 9.980 57.877 12.296 9.350

4.210 2.309 67.549 18.256 13.135 53.463 16.192 9.655 54.322 15.598 10.739 57.567 12.289 9.340

4.312 2.309 67.534 18.386 13.315 52.088 16.207 9.680 53.079 16.247 11.661 57.270 12.301 9.357

70

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

4.414 2.309 67.533 18.534 13.518 50.665 16.222 9.706 51.694 17.040 12.743 46.012 12.318 9.379

127 2.412 70.356 18.657 13.687 48.311 16.234 9.726 53.902 17.153 12.894 44.437 12.324 9.387

229 2.412 70.483 18.564 13.560 50.660 16.233 9.723 55.994 16.491 11.999 46.759 12.324 9.387

331 2.412 70.275 18.431 13.377 52.847 16.220 9.702 57.863 15.881 11.145 49.109 12.324 9.386

433 2.412 69.641 18.239 13.112 54.710 16.182 9.638 59.614 15.319 10.329 51.649 12.308 9.365

535 2.412 68.934 17.989 12.761 56.383 16.111 9.519 61.247 14.807 9.554 53.893 12.257 9.299

637 2.412 68.782 17.710 12.365 58.273 16.020 9.363 62.525 14.354 8.835 55.835 12.188 9.207

739 2.412 68.354 17.429 11.959 59.756 15.917 9.187 63.286 13.965 8.187 56.151 12.098 9.088

841 2.412 67.883 17.143 11.539 61.146 15.798 8.980 63.995 13.623 7.589 57.853 11.912 8.839

943 2.412 67.493 16.777 10.986 62.527 15.617 8.656 64.909 13.295 6.983 60.331 11.502 8.278

1.045 2.412 67.706 16.297 10.240 64.120 15.361 8.186 65.947 12.989 6.383 63.258 10.947 7.488

1.148 2.412 68.208 15.761 9.362 65.876 15.070 7.626 66.277 12.749 5.879 65.408 10.467 6.767

1.250 2.412 68.488 15.102 8.205 67.474 14.713 6.894 66.603 12.608 5.567 64.674 10.229 6.392

1.352 2.412 69.060 14.726 7.490 69.411 14.500 6.427 68.153 12.604 5.556 64.455 10.507 6.828

1.454 2.412 72.695 14.862 7.754 72.936 14.549 6.535 71.030 12.555 5.444 68.862 10.469 6.770

1.556 2.412 77.269 14.511 7.058 76.923 14.333 6.039 74.533 12.456 5.212 76.187 10.122 6.219

1.658 2.412 77.997 14.591 7.220 78.108 14.339 6.055 76.416 12.429 5.147 75.984 10.136 6.243

1.760 2.412 76.955 14.305 6.624 77.871 14.154 5.602 77.240 12.399 5.074 72.476 10.039 6.084

1.862 2.412 77.526 14.393 6.813 78.205 14.172 5.648 77.802 12.405 5.089 71.236 10.104 6.190

1.964 2.412 75.931 14.134 6.247 76.524 14.019 5.250 77.754 12.400 5.076 70.157 10.079 6.150

2.066 2.412 72.009 14.234 6.469 73.070 14.065 5.373 77.134 12.396 5.068 68.754 10.056 6.111

2.168 2.412 68.091 14.042 6.035 69.649 13.950 5.064 76.252 12.410 5.102 66.602 10.122 6.219

2.271 2.412 68.845 14.211 6.419 69.816 14.058 5.355 75.292 12.405 5.090 64.808 10.033 6.073

2.373 2.412 68.891 14.400 6.828 69.910 14.202 5.721 74.213 12.444 5.184 70.538 10.135 6.241

2.475 2.412 68.889 14.678 7.396 69.621 14.407 6.214 72.561 12.485 5.281 73.467 10.179 6.312

2.577 2.412 68.696 14.995 8.007 69.367 14.620 6.693 70.432 12.575 5.490 70.307 10.542 6.883

2.679 2.412 67.076 15.135 8.265 67.655 14.696 6.858 67.618 12.584 5.511 66.168 10.533 6.869

2.781 2.412 64.828 14.845 7.722 65.340 14.508 6.444 64.929 12.513 5.348 63.462 10.164 6.287

2.883 2.412 62.824 14.340 6.700 63.162 14.196 5.708 62.911 12.500 5.317 62.985 10.164 6.288

2.985 2.412 62.011 14.255 6.515 62.334 14.137 5.559 61.336 12.474 5.254 63.183 10.068 6.131

3.087 2.412 62.662 14.476 6.986 62.730 14.272 5.894 60.074 12.487 5.285 64.246 10.137 6.244

3.189 2.412 63.500 14.695 7.429 63.080 14.412 6.225 58.919 12.503 5.323 61.878 10.115 6.207

71

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.291 2.412 62.645 14.981 7.980 62.077 14.610 6.669 57.732 12.555 5.445 54.742 10.113 6.206

3.394 2.412 61.671 15.270 8.510 60.380 14.819 7.118 57.430 12.704 5.780 55.504 10.315 6.529

3.496 2.412 61.876 15.739 9.326 58.839 15.111 7.706 57.365 12.936 6.273 61.410 10.557 6.905

3.598 2.412 62.882 16.495 10.552 57.425 15.515 8.471 56.941 13.351 7.089 58.823 11.317 8.019

3.700 2.412 63.984 17.221 11.654 56.096 15.861 9.090 56.580 13.803 7.908 57.963 11.943 8.881

3.802 2.412 65.453 17.753 12.427 55.174 16.080 9.467 56.129 14.228 8.629 57.235 12.232 9.266

3.904 2.412 66.889 18.080 12.889 54.435 16.189 9.650 55.561 14.637 9.288 56.534 12.317 9.378

4.006 2.412 67.713 18.251 13.128 53.480 16.223 9.706 54.772 15.055 9.933 56.050 12.324 9.387

4.108 2.412 67.900 18.342 13.254 52.267 16.227 9.713 53.771 15.518 10.621 55.744 12.324 9.386

4.210 2.412 67.845 18.417 13.358 50.973 16.227 9.714 52.707 16.061 11.400 55.438 12.323 9.386

4.312 2.412 67.802 18.508 13.483 49.646 16.230 9.719 51.510 16.711 12.299 55.135 12.324 9.386

4.414 2.412 67.764 18.605 13.617 48.281 16.233 9.724 50.136 17.479 13.324 47.742 12.324 9.387

535 2.515 69.147 18.280 13.169 53.607 16.198 9.666 59.050 15.177 10.117 46.158 12.318 9.379

637 2.515 68.912 18.118 12.943 55.377 16.165 9.609 60.275 14.749 9.464 48.536 12.309 9.367

739 2.515 68.677 17.942 12.695 56.975 16.122 9.538 60.998 14.369 8.860 49.636 12.284 9.334

841 2.515 68.141 17.728 12.390 58.347 16.055 9.423 61.495 14.022 8.284 51.531 12.188 9.207

943 2.515 67.142 17.428 11.957 59.419 15.934 9.215 62.070 13.688 7.706 53.047 11.951 8.891

1.045 2.515 66.377 17.004 11.331 60.469 15.738 8.873 62.768 13.360 7.107 57.593 11.538 8.328

1.148 2.515 66.235 16.427 10.445 61.912 15.451 8.354 63.325 13.027 6.459 59.925 10.857 7.356

1.250 2.515 66.069 15.676 9.218 63.337 15.058 7.601 63.916 12.780 5.945 62.246 10.365 6.607

1.352 2.515 66.783 15.262 8.496 65.660 14.821 7.122 65.522 12.721 5.817 63.174 10.647 7.041

1.454 2.515 68.409 15.112 8.224 68.149 14.712 6.891 68.313 12.617 5.587 67.538 10.531 6.865

1.556 2.515 71.681 14.879 7.786 71.420 14.553 6.545 71.913 12.464 5.231 74.209 10.009 6.033

1.658 2.515 73.506 14.770 7.577 73.491 14.456 6.325 73.614 12.437 5.166 72.707 10.103 6.189

1.760 2.515 73.785 14.357 6.735 74.304 14.199 5.714 74.448 12.385 5.042 69.390 9.918 5.882

1.862 2.515 74.269 14.487 7.009 74.792 14.258 5.861 75.367 12.399 5.076 70.020 10.055 6.110

1.964 2.515 74.385 14.525 7.087 74.604 14.284 5.922 75.928 12.390 5.053 70.876 10.010 6.036

2.066 2.515 73.678 14.524 7.084 73.758 14.286 5.926 76.000 12.382 5.034 71.737 9.961 5.953

2.168 2.515 71.881 14.474 6.983 72.237 14.256 5.854 75.444 12.406 5.091 70.380 10.087 6.162

2.271 2.515 71.310 14.444 6.918 71.419 14.233 5.798 74.651 12.389 5.051 68.668 9.899 5.849

2.373 2.515 70.190 14.288 6.588 70.421 14.163 5.625 73.545 12.452 5.203 72.754 10.102 6.186

2.475 2.515 68.821 14.612 7.264 69.083 14.390 6.175 71.653 12.512 5.346 75.445 10.166 6.291

2.577 2.515 68.508 15.215 8.412 68.482 14.770 7.014 69.179 12.678 5.723 74.458 10.849 7.344

72

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.679 2.515 66.993 15.583 9.059 66.792 14.991 7.469 66.045 12.724 5.825 71.104 10.916 7.443

2.781 2.515 64.538 15.581 9.057 64.315 14.982 7.449 63.193 12.640 5.638 65.098 10.347 6.579

2.883 2.515 61.900 15.391 8.726 61.665 14.853 7.187 60.817 12.647 5.654 63.265 10.422 6.697

2.985 2.515 60.818 15.142 8.278 60.490 14.699 6.864 58.936 12.604 5.556 62.137 10.206 6.355

3.087 2.515 61.057 14.817 7.668 60.455 14.520 6.471 57.399 12.627 5.609 61.018 10.340 6.569

3.189 2.515 61.374 14.631 7.301 60.317 14.430 6.268 56.204 12.643 5.645 57.201 10.293 6.494

3.291 2.515 60.324 15.035 8.080 58.935 14.676 6.814 55.086 12.700 5.770 50.094 10.254 6.432

3.394 2.515 58.698 14.918 7.862 56.771 14.656 6.771 55.257 12.863 6.122 54.007 10.428 6.706

3.496 2.515 61.305 15.223 8.426 56.772 14.853 7.187 55.688 13.071 6.547 57.601 10.386 6.641

3.598 2.515 63.430 16.427 10.446 55.571 15.470 8.388 55.337 13.559 7.474 56.881 11.286 7.976

3.700 2.515 65.042 17.262 11.715 54.283 15.863 9.093 54.986 14.061 8.350 56.087 11.936 8.870

3.802 2.515 66.318 17.807 12.503 53.109 16.087 9.478 54.568 14.529 9.116 55.302 12.229 9.261

3.904 2.515 67.591 18.135 12.966 52.166 16.193 9.658 53.946 14.981 9.821 54.390 12.316 9.376

4.006 2.515 68.196 18.312 13.213 50.973 16.228 9.715 53.042 15.444 10.513 53.890 12.324 9.387

4.108 2.515 68.128 18.408 13.345 49.558 16.234 9.726 52.023 15.946 11.237 53.585 12.324 9.387

4.210 2.515 68.065 18.480 13.444 48.266 16.234 9.726 51.004 16.514 12.031 53.281 12.324 9.387

4.312 2.515 68.007 18.555 13.548 46.961 16.234 9.726 49.847 17.171 12.917 52.976 12.324 9.387

4.414 2.515 67.947 18.633 13.654 45.626 16.234 9.726 48.453 17.925 13.904 49.459 12.324 9.387

1.148 2.618 65.307 17.036 11.378 58.607 15.758 8.908 60.485 13.432 7.241 52.344 11.530 8.316

1.250 2.618 64.686 16.428 10.446 59.804 15.458 8.367 61.180 13.131 6.665 55.100 11.065 7.658

1.352 2.618 64.330 15.797 9.423 61.639 15.123 7.731 62.297 12.842 6.078 59.131 10.443 6.730

1.454 2.618 64.205 15.095 8.192 63.349 14.729 6.927 64.737 12.673 5.712 63.024 10.265 6.449

1.556 2.618 66.835 14.881 7.791 66.524 14.574 6.591 67.819 12.526 5.377 69.772 9.989 6.000

1.658 2.618 70.028 14.743 7.523 69.743 14.456 6.325 70.423 12.481 5.271 71.328 10.118 6.213

1.760 2.618 72.458 14.401 6.830 72.353 14.233 5.798 72.486 12.406 5.091 71.853 9.916 5.878

1.862 2.618 74.577 14.520 7.076 74.331 14.292 5.941 74.339 12.409 5.100 74.998 10.054 6.109

1.964 2.618 76.412 14.616 7.272 75.799 14.358 6.100 75.817 12.395 5.066 77.740 10.011 6.037

2.066 2.618 77.247 14.785 7.605 76.434 14.461 6.337 76.469 12.387 5.045 80.597 9.956 5.946

2.168 2.618 75.799 14.950 7.921 75.371 14.562 6.564 75.424 12.414 5.110 77.952 10.083 6.156

2.271 2.618 74.271 14.830 7.693 73.850 14.498 6.421 73.771 12.403 5.085 72.339 9.897 5.846

2.373 2.618 73.359 14.426 6.882 72.745 14.286 5.928 73.396 12.483 5.277 79.743 10.132 6.235

2.475 2.618 71.412 14.793 7.621 70.562 14.523 6.478 72.007 12.561 5.459 84.434 10.204 6.352

73

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.577 2.618 68.528 15.389 8.722 67.854 14.899 7.282 67.838 12.760 5.901 77.110 10.948 7.489

2.679 2.618 65.719 15.790 9.412 65.105 15.149 7.781 64.022 12.871 6.139 69.027 11.240 7.910

2.781 2.618 63.371 15.988 9.740 62.554 15.263 8.000 60.864 12.913 6.227 63.771 11.268 7.950

2.883 2.618 60.785 16.025 9.801 59.814 15.270 8.014 58.277 12.929 6.259 61.025 11.258 7.935

2.985 2.618 58.954 15.839 9.493 57.956 15.153 7.788 56.158 12.921 6.242 58.402 11.165 7.803

3.087 2.618 58.421 15.459 8.844 57.137 14.936 7.358 54.559 12.915 6.230 56.077 11.049 7.636

3.189 2.618 58.501 15.223 8.425 56.465 14.809 7.095 53.565 12.955 6.312 52.156 11.041 7.624

3.291 2.618 58.252 15.523 8.956 55.269 14.979 7.445 53.105 13.070 6.546 50.064 11.159 7.794

3.394 2.618 58.741 15.721 9.295 54.135 15.099 7.683 53.289 13.264 6.925 53.714 11.286 7.975

3.496 2.618 61.074 16.080 9.891 53.687 15.294 8.058 53.484 13.548 7.454 54.711 11.423 8.167

3.598 2.618 63.636 16.780 10.991 52.982 15.636 8.690 53.314 13.950 8.162 54.892 11.746 8.613

3.700 2.618 65.582 17.443 11.979 51.937 15.931 9.211 53.055 14.414 8.933 54.118 12.085 9.070

3.802 2.618 66.986 17.905 12.642 50.872 16.114 9.523 52.703 14.886 9.675 53.054 12.268 9.313

3.904 2.618 67.979 18.185 13.036 49.771 16.201 9.670 52.041 15.360 10.390 52.149 12.321 9.382

4.006 2.618 68.378 18.341 13.254 48.467 16.229 9.718 51.138 15.852 11.104 51.730 12.324 9.387

4.108 2.618 68.296 18.434 13.381 47.059 16.234 9.726 50.189 16.381 11.848 51.425 12.324 9.387

4.210 2.618 68.234 18.503 13.477 45.771 16.234 9.726 49.214 16.970 12.649 51.121 12.324 9.387

4.312 2.618 68.172 18.575 13.576 44.469 16.234 9.726 48.083 17.633 13.526 50.816 12.324 9.387

4.414 2.618 68.111 18.651 13.679 43.135 16.234 9.726 46.612 18.379 14.485 50.511 12.324 9.387

1.454 2.720 62.914 15.465 8.856 60.342 14.939 7.364 62.012 12.953 6.308 51.551 10.679 7.089

1.556 2.720 65.213 14.928 7.880 63.648 14.626 6.706 64.750 12.766 5.916 49.077 10.476 6.781

1.658 2.720 68.880 14.768 7.573 67.645 14.494 6.413 67.553 12.624 5.603 55.043 10.343 6.574

1.760 2.720 73.268 14.470 6.975 72.131 14.287 5.929 70.391 12.495 5.306 68.794 10.088 6.164

1.862 2.720 77.215 14.544 7.126 75.726 14.305 5.973 73.346 12.454 5.207 75.294 10.114 6.207

1.964 2.720 79.547 14.583 7.204 77.946 14.331 6.035 75.615 12.430 5.150 80.234 10.092 6.170

2.066 2.720 80.830 14.302 6.617 79.315 14.203 5.725 76.498 12.424 5.135 83.647 10.071 6.136

2.168 2.720 77.375 15.009 8.033 76.832 14.615 6.682 74.833 12.444 5.184 78.704 10.133 6.238

2.271 2.720 75.057 15.102 8.204 74.648 14.682 6.827 72.196 12.464 5.232 70.543 10.084 6.157

2.373 2.720 75.043 14.928 7.881 73.809 14.605 6.660 71.068 12.565 5.467 75.813 10.348 6.581

2.475 2.720 72.108 15.277 8.523 70.602 14.826 7.131 69.179 12.670 5.705 76.899 10.485 6.794

2.577 2.720 67.842 15.629 9.139 66.694 15.061 7.608 65.099 12.815 6.019 70.591 10.796 7.265

2.679 2.720 64.024 15.643 9.163 63.143 15.109 7.703 61.279 12.964 6.331 63.207 11.143 7.771

2.781 2.720 61.944 16.034 9.815 60.512 15.325 8.118 58.187 13.109 6.623 60.771 11.575 8.379

74

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.883 2.720 59.792 16.333 10.297 57.856 15.475 8.397 55.642 13.181 6.764 57.546 11.730 8.592

2.985 2.720 57.300 16.242 10.152 55.369 15.415 8.287 53.455 13.177 6.756 54.468 11.545 8.337

3.087 2.720 55.778 15.929 9.642 53.632 15.240 7.956 51.776 13.136 6.676 51.120 11.137 7.763

3.189 2.720 55.568 15.826 9.472 52.407 15.179 7.838 50.900 13.197 6.796 47.049 11.091 7.697

3.291 2.720 56.639 16.115 9.948 51.713 15.325 8.118 50.848 13.415 7.210 49.338 11.511 8.291

3.394 2.720 58.538 16.475 10.521 51.183 15.506 8.455 51.182 13.693 7.715 50.950 11.833 8.732

3.496 2.720 61.144 16.854 11.104 50.767 15.685 8.779 51.324 14.020 8.281 52.234 12.000 8.958

3.598 2.720 63.740 17.299 11.768 50.217 15.879 9.121 51.170 14.407 8.920 52.969 12.128 9.128

3.700 2.720 65.995 17.728 12.391 49.544 16.049 9.413 50.965 14.840 9.605 52.489 12.244 9.281

3.802 2.720 67.536 18.052 12.850 48.659 16.161 9.603 50.661 15.301 10.303 51.454 12.309 9.367

3.904 2.720 68.347 18.258 13.137 47.531 16.215 9.694 50.005 15.782 11.004 50.518 12.324 9.387

4.006 2.720 68.540 18.382 13.310 46.166 16.232 9.722 49.149 16.289 11.720 49.832 12.324 9.387

4.108 2.720 68.459 18.462 13.420 44.804 16.234 9.726 48.291 16.837 12.470 49.265 12.324 9.387

4.210 2.720 68.398 18.529 13.512 43.521 16.234 9.726 47.337 17.438 13.271 48.960 12.324 9.387

4.312 2.720 68.337 18.600 13.609 42.219 16.234 9.726 46.157 18.105 14.136 48.656 12.324 9.387

4.414 2.720 68.276 18.674 13.711 40.885 16.234 9.726 44.628 18.844 15.071 48.351 12.324 9.387

1.556 2.823 64.898 15.926 9.638 61.023 15.170 7.821 62.303 13.175 6.753 40.838 11.285 7.974

1.658 2.823 67.535 15.453 8.835 64.604 14.886 7.256 64.888 12.893 6.185 50.443 10.736 7.175

1.760 2.823 70.417 14.751 7.539 68.577 14.461 6.338 67.834 12.596 5.539 62.522 9.877 5.812

1.862 2.823 73.244 14.320 6.656 72.015 14.190 5.692 71.686 12.535 5.399 71.434 10.054 6.108

1.964 2.823 77.315 14.454 6.940 75.660 14.261 5.866 74.452 12.478 5.265 78.160 9.979 5.985

2.066 2.823 78.757 14.603 7.246 77.229 14.369 6.125 75.742 12.457 5.216 82.257 9.912 5.871

2.168 2.823 74.752 14.988 7.994 74.593 14.609 6.668 73.106 12.497 5.310 74.222 10.107 6.196

2.271 2.823 71.145 14.997 8.009 71.475 14.634 6.723 68.889 12.462 5.226 61.947 9.714 5.531

2.373 2.823 70.248 15.276 8.522 69.831 14.808 7.094 67.241 12.682 5.732 60.708 10.635 7.024

2.475 2.823 68.762 15.683 9.231 67.317 15.066 7.618 64.881 12.821 6.032 60.167 10.890 7.404

2.577 2.823 65.953 15.925 9.636 64.155 15.237 7.950 61.473 12.855 6.104 59.550 10.441 6.726

2.679 2.823 63.011 15.843 9.501 60.951 15.228 7.934 58.206 13.037 6.479 59.422 10.769 7.225

2.781 2.823 61.603 16.264 10.187 58.631 15.453 8.357 55.615 13.300 6.993 57.528 11.621 8.443

2.883 2.823 59.969 16.602 10.718 56.133 15.620 8.662 53.379 13.430 7.237 54.304 11.895 8.816

2.985 2.823 57.224 16.481 10.530 53.270 15.552 8.539 51.362 13.439 7.253 51.338 11.620 8.441

3.087 2.823 54.361 15.972 9.714 50.586 15.288 8.047 49.404 13.331 7.052 47.281 10.779 7.239

75

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.189 2.823 53.621 15.836 9.488 48.830 15.210 7.899 48.431 13.400 7.183 45.403 10.614 6.992

3.291 2.823 56.361 16.488 10.540 48.800 15.526 8.491 48.800 13.782 7.872 47.467 11.555 8.351

3.394 2.823 59.121 17.003 11.329 48.595 15.769 8.928 49.184 14.140 8.483 49.441 12.041 9.012

3.496 2.823 61.866 17.397 11.913 48.230 15.940 9.227 49.251 14.506 9.079 50.762 12.233 9.266

3.598 2.823 64.580 17.728 12.390 47.880 16.065 9.440 49.058 14.896 9.691 51.387 12.290 9.342

3.700 2.823 66.899 18.002 12.780 47.386 16.152 9.587 48.913 15.317 10.326 50.797 12.313 9.373

3.802 2.823 68.256 18.204 13.063 46.506 16.204 9.676 48.569 15.769 10.985 49.930 12.323 9.386

3.904 2.823 68.714 18.337 13.248 45.264 16.227 9.715 47.900 16.249 11.664 49.224 12.324 9.387

4.006 2.823 68.691 18.428 13.373 43.863 16.234 9.725 47.137 16.764 12.372 48.556 12.324 9.387

4.108 2.823 68.624 18.494 13.465 42.560 16.234 9.726 46.314 17.321 13.116 47.887 12.324 9.387

4.210 2.823 68.563 18.560 13.555 41.276 16.234 9.726 45.323 17.928 13.908 47.218 12.324 9.387

4.312 2.823 68.501 18.630 13.650 39.975 16.234 9.726 44.070 18.595 14.758 46.550 12.324 9.387

4.414 2.823 68.440 18.703 13.749 38.640 16.234 9.726 42.496 19.326 15.669 46.191 12.324 9.387

1.760 2.926 65.924 15.488 8.895 62.776 14.891 7.265 65.254 13.026 6.457 48.560 10.792 7.259

1.862 2.926 66.260 14.626 7.292 64.848 14.403 6.203 68.333 12.797 5.981 65.848 10.369 6.614

1.964 2.926 69.831 14.534 7.106 68.486 14.348 6.076 70.580 12.699 5.769 77.117 10.293 6.494

2.066 2.926 72.070 14.951 7.923 70.729 14.590 6.626 71.174 12.713 5.800 77.802 10.538 6.876

2.168 2.926 70.202 15.002 8.020 69.817 14.623 6.699 69.421 12.691 5.752 72.653 10.476 6.781

2.271 2.926 66.533 14.562 7.162 66.870 14.383 6.158 66.375 12.668 5.700 64.294 10.264 6.447

2.373 2.926 65.475 15.052 8.113 65.058 14.677 6.815 63.805 12.824 6.039 61.331 10.792 7.260

2.475 2.926 65.005 15.810 9.444 63.015 15.134 7.750 61.249 12.996 6.397 59.078 11.161 7.797

2.577 2.926 63.923 16.300 10.244 60.782 15.433 8.319 58.435 13.132 6.667 56.451 11.105 7.717

2.679 2.926 62.908 16.578 10.681 58.657 15.597 8.620 55.778 13.338 7.066 55.969 11.315 8.017

2.781 2.926 62.196 16.838 11.081 56.711 15.730 8.859 53.629 13.590 7.530 54.152 11.818 8.712

2.883 2.926 61.011 16.989 11.309 54.392 15.798 8.979 51.760 13.759 7.831 52.019 12.030 8.997

2.985 2.926 58.650 16.843 11.087 51.596 15.717 8.835 49.986 13.827 7.950 49.570 11.823 8.719

3.087 2.926 56.202 16.430 10.450 48.905 15.505 8.453 48.269 13.838 7.969 46.929 11.345 8.059

3.189 2.926 55.786 16.369 10.354 47.151 15.466 8.380 47.302 13.973 8.202 46.406 11.289 7.979

3.291 2.926 58.167 16.922 11.207 46.851 15.724 8.848 47.222 14.296 8.740 47.826 11.778 8.658

3.394 2.926 60.800 17.415 11.938 46.556 15.947 9.238 47.382 14.661 9.325 49.088 12.143 9.148

3.496 2.926 63.513 17.770 12.451 46.232 16.089 9.482 47.323 15.038 9.908 49.824 12.292 9.345

3.598 2.926 66.015 18.022 12.807 45.853 16.169 9.616 47.099 15.431 10.494 49.891 12.322 9.385

3.700 2.926 67.940 18.201 13.058 45.260 16.209 9.684 46.882 15.845 11.095 49.286 12.324 9.387

76

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.802 2.926 68.858 18.322 13.227 44.236 16.227 9.714 46.436 16.289 11.719 48.617 12.324 9.387

3.904 2.926 68.922 18.405 13.342 42.867 16.233 9.724 45.798 16.766 12.374 47.948 12.324 9.387

4.006 2.926 68.849 18.471 13.432 41.532 16.234 9.726 45.070 17.282 13.064 47.279 12.324 9.387

4.108 2.926 68.788 18.532 13.516 40.252 16.234 9.726 44.200 17.840 13.794 46.611 12.324 9.387

4.210 2.926 68.727 18.596 13.604 38.968 16.234 9.726 43.154 18.447 14.571 45.942 12.324 9.387

4.312 2.926 68.666 18.664 13.697 37.667 16.234 9.726 41.848 19.109 15.401 45.273 12.324 9.387

4.414 2.926 68.604 18.736 13.794 36.332 16.234 9.726 40.204 19.830 16.286 44.604 12.324 9.387

1.964 3.029 66.573 15.685 9.234 63.057 15.014 7.514 65.922 13.065 6.536 55.137 10.532 6.867

2.066 3.029 67.268 15.675 9.216 64.635 15.015 7.517 66.334 13.096 6.597 71.092 11.068 7.663

2.168 3.029 66.361 15.355 8.661 64.727 14.834 7.148 65.204 13.018 6.442 67.732 10.900 7.418

2.271 3.029 64.216 14.555 7.148 63.110 14.387 6.167 63.034 12.872 6.141 63.035 10.194 6.336

2.373 3.029 62.116 14.929 7.882 60.800 14.614 6.679 60.633 13.021 6.447 58.662 10.764 7.217

2.475 3.029 62.307 15.836 9.489 59.185 15.145 7.773 58.273 13.206 6.812 55.168 11.165 7.802

2.577 3.029 62.868 16.569 10.667 57.898 15.560 8.554 56.021 13.449 7.273 53.906 11.499 8.274

2.679 3.029 63.407 17.047 11.396 56.657 15.815 9.009 53.958 13.725 7.772 53.376 11.806 8.696

2.781 3.029 63.305 17.320 11.800 54.976 15.943 9.232 52.179 13.992 8.234 52.318 12.086 9.072

2.883 3.029 62.381 17.420 11.946 52.719 15.978 9.292 50.454 14.204 8.589 51.029 12.202 9.225

2.985 3.029 61.247 17.335 11.822 50.529 15.926 9.202 48.736 14.352 8.832 49.264 12.112 9.106

3.087 3.029 60.096 17.168 11.575 48.360 15.835 9.043 47.223 14.479 9.037 47.587 11.945 8.883

3.189 3.029 60.029 17.177 11.589 46.674 15.827 9.030 46.238 14.665 9.331 47.173 11.929 8.862

3.291 3.029 61.346 17.444 11.981 45.828 15.940 9.227 45.817 14.943 9.762 48.705 12.092 9.080

3.394 3.029 63.333 17.767 12.447 45.234 16.074 9.455 45.729 15.278 10.268 49.267 12.248 9.287

3.496 3.029 65.520 18.028 12.816 44.688 16.166 9.611 45.509 15.642 10.803 49.225 12.314 9.373

3.598 3.029 67.483 18.210 13.071 44.109 16.213 9.690 45.160 16.027 11.352 48.676 12.324 9.387

3.700 3.029 68.820 18.328 13.235 43.290 16.230 9.719 44.811 16.432 11.917 48.009 12.324 9.387

3.802 3.029 69.143 18.402 13.338 42.033 16.234 9.725 44.301 16.865 12.508 47.340 12.324 9.387

3.904 3.029 69.075 18.459 13.416 40.690 16.234 9.726 43.647 17.336 13.136 46.671 12.324 9.387

4.006 3.029 69.014 18.515 13.492 39.399 16.234 9.726 42.873 17.846 13.802 46.003 12.324 9.387

4.108 3.029 68.953 18.574 13.574 38.118 16.234 9.726 41.970 18.400 14.511 45.334 12.324 9.387

4.210 3.029 68.891 18.637 13.660 36.835 16.234 9.726 40.862 19.001 15.266 44.665 12.324 9.387

4.312 3.029 68.830 18.704 13.751 35.534 16.234 9.726 39.473 19.654 16.071 44.239 12.324 9.387

4.414 3.029 68.769 18.774 13.846 34.199 16.234 9.726 37.758 20.362 16.930 44.239 12.324 9.387

77

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

2.271 3.132 62.239 15.606 9.100 59.167 14.987 7.460 59.640 13.439 7.254 48.007 11.123 7.743

2.373 3.132 60.799 15.247 8.470 57.729 14.822 7.122 57.724 13.358 7.104 55.037 10.713 7.142

2.475 3.132 61.451 15.930 9.644 56.462 15.201 7.881 55.737 13.438 7.252 52.624 10.642 7.035

2.577 3.132 63.158 16.804 11.029 55.856 15.661 8.736 53.937 13.826 7.948 52.390 11.464 8.225

2.679 3.132 64.479 17.369 11.872 55.003 15.943 9.231 52.261 14.194 8.572 51.885 11.978 8.927

2.781 3.132 64.830 17.676 12.316 53.551 16.078 9.463 50.572 14.509 9.085 51.124 12.228 9.260

2.883 3.132 64.627 17.793 12.483 51.686 16.116 9.527 48.834 14.770 9.496 50.141 12.300 9.355

2.985 3.132 64.116 17.772 12.454 49.742 16.093 9.487 47.137 14.984 9.826 48.943 12.276 9.324

3.087 3.132 63.549 17.711 12.366 47.802 16.053 9.420 45.660 15.182 10.125 47.823 12.233 9.267

3.189 3.132 63.641 17.731 12.395 46.163 16.049 9.414 44.673 15.404 10.454 47.461 12.229 9.261

3.291 3.132 64.443 17.866 12.588 45.052 16.096 9.494 44.162 15.673 10.847 48.197 12.270 9.315

3.394 3.132 65.808 18.050 12.848 44.164 16.159 9.601 43.912 15.987 11.296 49.462 12.310 9.368

3.496 3.132 67.340 18.214 13.077 43.328 16.206 9.679 43.495 16.331 11.778 48.617 12.323 9.386

3.598 3.132 68.601 18.332 13.240 42.437 16.228 9.716 43.039 16.697 12.281 47.401 12.324 9.387

3.700 3.132 69.278 18.409 13.346 41.342 16.234 9.726 42.635 17.086 12.805 46.732 12.324 9.387

3.802 3.132 69.301 18.459 13.415 40.013 16.234 9.726 42.068 17.506 13.360 46.064 12.324 9.387

3.904 3.132 69.240 18.509 13.485 38.695 16.234 9.726 41.381 17.965 13.956 45.395 12.324 9.387

4.006 3.132 69.178 18.563 13.559 37.404 16.234 9.726 40.574 18.466 14.594 44.726 12.324 9.387

4.108 3.132 69.117 18.621 13.638 36.124 16.234 9.726 39.602 19.010 15.277 44.239 12.324 9.387

4.210 3.132 69.056 18.683 13.722 34.840 16.234 9.726 38.412 19.601 16.007 44.239 12.324 9.387

4.312 3.132 68.995 18.748 13.811 33.539 16.234 9.726 36.966 20.243 16.787 44.239 12.324 9.387

4.414 3.132 68.933 18.817 13.904 32.204 16.234 9.726 35.212 20.940 17.621 44.239 12.324 9.387

2.577 3.234 64.322 17.173 11.582 54.292 15.815 9.008 51.858 14.447 8.985 49.228 11.567 8.368

2.679 3.234 65.929 17.657 12.290 53.626 16.038 9.395 50.245 14.834 9.596 50.304 12.057 9.033

2.781 3.234 66.933 17.942 12.695 52.564 16.154 9.591 48.512 15.175 10.115 49.392 12.271 9.317

2.883 3.234 67.154 18.067 12.871 50.905 16.193 9.657 46.740 15.469 10.551 48.488 12.321 9.383

2.985 3.234 66.902 18.085 12.896 49.063 16.188 9.648 45.033 15.726 10.923 47.775 12.321 9.383

3.087 3.234 66.495 18.068 12.873 47.179 16.172 9.621 43.660 15.965 11.265 47.132 12.316 9.376

3.189 3.234 66.424 18.085 12.897 45.450 16.170 9.618 42.727 16.210 11.610 46.807 12.315 9.375

3.291 3.234 66.880 18.155 12.994 44.183 16.186 9.646 42.203 16.481 11.985 46.950 12.320 9.382

3.394 3.234 67.726 18.252 13.130 43.054 16.210 9.685 41.726 16.782 12.396 47.569 12.324 9.387

3.496 3.234 68.651 18.345 13.259 41.934 16.226 9.713 41.168 17.108 12.833 47.544 12.324 9.387

3.598 3.234 69.342 18.418 13.360 40.785 16.233 9.724 40.780 17.454 13.292 46.124 12.324 9.387

78

Coordinates

(in meters)

Universal

Point Kriging

Point Kriging

on a G.A.M.

Universal


x y

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

n.e. free

Predic-

tion

Error

of pred.

Error

sin e.n.

Predic-

tion

Error of

pred.

Error

n.e. free

3.700 3.234 69.527 18.470 13.431 39.469 16.234 9.726 40.321 17.823 13.772 45.456 12.324 9.387

3.802 3.234 69.465 18.515 13.493 38.110 16.234 9.726 39.733 18.222 14.285 44.787 12.324 9.387

3.904 3.234 69.404 18.564 13.560 36.792 16.234 9.726 39.009 18.664 14.845 44.239 12.324 9.387

4.006 3.234 69.343 18.617 13.632 35.500 16.234 9.726 38.163 19.150 15.451 44.239 12.324 9.387

4.108 3.234 69.282 18.673 13.709 34.220 16.234 9.726 37.144 19.681 16.104 44.239 12.324 9.387

4.210 3.234 69.220 18.734 13.791 32.937 16.234 9.726 35.899 20.259 16.806 44.239 12.324 9.387

4.312 3.234 69.159 18.797 13.878 31.635 16.234 9.726 34.360 20.888 17.559 44.239 12.324 9.387

4.414 3.234 69.098 18.865 13.969 30.301 16.234 9.726 32.596 21.574 18.369 44.239 12.324 9.387

2.883 3.337 69.136 18.253 13.131 49.868 16.223 9.708 44.366 16.313 11.753 46.252 12.324 9.386

2.985 3.337 69.013 18.282 13.171 48.094 16.225 9.711 42.797 16.586 12.129 45.491 12.324 9.387

3.087 3.337 68.663 18.283 13.173 46.240 16.221 9.703 41.573 16.844 12.480 45.171 12.324 9.387

3.189 3.337 68.565 18.301 13.198 44.501 16.220 9.702 40.718 17.101 12.824 44.868 12.324 9.387

3.291 3.337 68.783 18.341 13.254 43.123 16.224 9.709 40.122 17.371 13.183 44.572 12.324 9.387

3.394 3.337 69.202 18.395 13.327 41.794 16.230 9.719 39.512 17.662 13.563 44.895 12.324 9.387

3.496 3.337 69.608 18.448 13.400 40.430 16.234 9.725 38.993 17.969 13.961 45.218 12.324 9.387

3.598 3.337 69.752 18.492 13.462 39.025 16.234 9.726 38.559 18.297 14.380 44.848 12.324 9.387

3.700 3.337 69.691 18.532 13.517 37.610 16.234 9.726 38.021 18.644 14.820 44.239 12.324 9.387

3.802 3.337 69.630 18.576 13.577 36.251 16.234 9.726 37.365 19.024 15.295 44.239 12.324 9.387

3.904 3.337 69.568 18.624 13.642 34.933 16.234 9.726 36.603 19.448 15.819 44.239 12.324 9.387

4.006 3.337 69.507 18.675 13.712 33.642 16.234 9.726 35.693 19.916 16.392 44.239 12.324 9.387

4.108 3.337 69.446 18.731 13.787 32.362 16.234 9.726 34.587 20.432 17.014 44.239 12.324 9.387

4.210 3.337 69.385 18.789 13.867 31.078 16.234 9.726 33.226 20.996 17.687 44.239 12.324 9.387

4.312 3.337 69.323 18.852 13.951 29.777 16.234 9.726 31.601 21.610 18.413 44.239 12.324 9.387

3.802 3.440 69.794 18.642 13.667 34.390 16.234 9.726 34.947 19.912 16.387 44.239 12.324 9.387

3.904 3.440 69.733 18.689 13.730 33.072 16.234 9.726 34.089 20.319 16.878 44.239 12.324 9.387

4.006 3.440 69.672 18.739 13.798 31.781 16.234 9.726 33.067 20.771 17.420 44.239 12.324 9.387

4.108 3.440 69.610 18.792 13.871 30.501 16.234 9.726 31.828 21.271 18.013 44.239 12.324 9.387

4.210 3.440 69.549 18.850 13.949 29.217 16.234 9.726 30.359 21.819 18.657 44.239 12.324 9.387

4.312 3.440 69.488 18.911 14.031 27.916 16.234 9.726 28.725 22.422 19.359 44.239 12.324 9.387

4.006 3.543 69.836 18.807 13.891 29.870 16.234 9.726 30.213 21.714 18.534 44.239 12.324 9.387

4.108 3.543 69.775 18.859 13.961 28.590 16.234 9.726 28.868 22.198 19.099 44.239 12.324 9.387

Nombre de archivo: docufacultad.WPDDirectorio: C:\WINNT\Profiles\aparra\EscritorioPlantilla: C:\Archivos de programa\Microsoft

Office\Plantillas\Normal.dotTítulo:Asunto:Autor: Alejo Parra Fernández-YáñezPalabras clave:Comentarios:Fecha de creación: 25/06/99 17.19Cambio número: 1Guardado el:Guardado por:Tiempo de edición: 3 minutosImpreso el: 25/06/99 17.52Última impresión completa

Número de páginas: 79Número de palabras: 26.830 (aprox.)Número de caracteres: 152.934 (aprox.)

Documents

OF HOUSING PRICE IN ALBACETE - MITweb.mit.edu/11.951/oldstuff/albacete/Course Reader/Transportation... · behaviour of the housing price in Albacete. Keywords: Kriging prediction,