Spatial interpolation

In your life as a spatial data scientist, you will find yourself in a situation where you have plenty of data to work with, just linked to different geometries or representing slightly different locations that you need. When this happens, you need to interpolate the data from one set of geometries, on which the data is shipped, to the other, the one you are interested in.

Any interpolation method is necessarily an approximation but some are better than others.

import geopandas as gpd
import tobler
import pyinterpolate
import numpy as np
import matplotlib.pyplot as plt

from libpysal import graph
from sklearn import neighbors
from scipy import interpolate

This chapter of the course covers two types of interpolation - from one set of polygons to another set of polygons, and from a sparse set of points to other locations in the same area.

Areal interpolation and dasymetric mapping

The first use case is the interpolation of data from one set of geometries to the other one, otherwise known as areal interpolation or dasymetric mapping.

Data zones and H3

You are already familiar with the Scottish Index of Multiple Deprivation, so let’s use it as an example of areal interpolation. Load the subset of SIMD 2020 for the City of Edinburgh.

simd = gpd.read_file(
    "https://martinfleischmann.net/sds/interpolation/data/edinburgh_simd_2020.gpkg"
)
simd.head(2)

	DataZone	DZName	LAName	SAPE2017	WAPE2017	Rankv2	Quintilev2	Decilev2	Vigintilv2	Percentv2	...	CrimeRate	CrimeRank	HouseNumOC	HouseNumNC	HouseOCrat	HouseNCrat	HouseRank	Shape_Leng	Shape_Area	geometry
0	S01008417	Balerno and Bonnington Village - 01	City of Edinburgh	708	397	5537	4	8	16	80	...	86	5392.0	17	8	2%	1%	6350.0	20191.721420	1.029993e+07	POLYGON ((315157.369 666212.846, 315173.727 66...
1	S01008418	Balerno and Bonnington Village - 02	City of Edinburgh	691	378	6119	5	9	18	88	...	103	5063.0	7	10	1%	1%	6650.0	25944.861787	2.357050e+07	POLYGON ((317816 666579, 318243 666121, 318495...

2 rows × 52 columns

Alternative

Instead of reading the file directly off the web, it is possible to download it manually, store it on your computer, and read it locally. To do that, you can follow these steps:

Download the file by right-clicking on this link and saving the file
Place the file in the same folder as the notebook where you intend to read it
Replace the code in the cell above with:

simd = gpd.read_file(
    "edinburgh_simd_2020.gpkg",
)

Get an interactive map with one of the variables to get more familiar with the data.

simd[["EmpNumDep", "geometry"]].explore("EmpNumDep", tiles="CartoDB Positron")

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This is the source - data linked to SIMD Data Zones. Let’s now focus on the target geometries. Popular spatial units of analysis are hexagonal grids and Uber’s hierarchical H3 grid especially.

	geometry
8819720cbdfffff	POLYGON ((326024.019 677541.096, 325560.507 67...
8819727403fffff	POLYGON ((319845.732 669416.699, 319381.667 66...

tobler and Tobler

The task ahead can be done in many ways falling under the umbrella of dasymetric mapping. PySAL has a package designed for areal interpolation called tobler. You have already used it to create the H3 grid but that is only a small utility function included in tobler. The name of the package is a homage to the Waldo R. Tobler, a famous geographer and an author of a Pycnophylactic interpolation covered below

Simple areal interpolation

But before getting to the Pycnophylactic interpolation, let’s start with the simple case of basic areal interpolation. The logic behind it is quite simple - the method redistributes values from the source geometries to target geometries based on the proportion of area that is shared between each source polygon and each target polygon. It is not a simple join as you would get with sjoin() or overlay() methods from geopandas but it is not far from it. Areal interpolation brings an additional step of taking the values and redistributing them instead of merging. That means that if the source geometry contains a count of 10 people and 40% of the geometry is covered by a target polygon A, 40% by a target polygon B and 20% by a target polygon C, each gets a relevant proportion of the original count (4, 4, and 2). Similarly are treated intensive variables (e.g. a percentage).

The function you need to use for this kind of areal interpolation lives in the area_weighted module of tobler and is called simply area_interpolate. Use it to interpolate a subset of data from simd to grid_8.

interpolated = tobler.area_weighted.area_interpolate(
1    source_df=simd,
2    target_df=grid_8,
3    extensive_variables=["EmpNumDep", "IncNumDep"],
4    intensive_variables=["EmpRate", "IncRate"],
)
interpolated.head(2)

1: Specify the source GeoDataFrame.
2: Specify the target GeoDataFrame.
3: Specify the list of extensive variables to be interpolated.
4: Specify the list of intensive variables to be interpolated.

	EmpNumDep	IncNumDep	EmpRate	IncRate	geometry
hex_id
8819720cbdfffff	264.569605	472.059309	6.583676	8.917363	POLYGON ((326024.019 677541.096, 325560.507 67...
8819727403fffff	32.622323	49.403157	2.870268	2.437487	POLYGON ((319845.732 669416.699, 319381.667 66...

The resulting interpolated GeoDataFrame contains the selected variables from simd but linked to the grid_8 geometries. You can check the result on a map and compare it to the one above.

interpolated.explore("EmpNumDep", tiles="CartoDB Positron")

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Raster masking

You may have noticed that even if the actual location where a grid cell lies does not contain any buildings, it gets a proportion of data based on the area of overlap. That may not be an issue in some cases but if you want to be more precise, you can use a raster layer as a mask to further influence the redistribution of values. See the documentation for details.

Pycnophylactic interpolation

Another option is to use Pycnophylactic interpolation (Tobler 1979), a method that creates a smooth contour map of the original data while preserving the volume but avoiding sharp boundaries and uses this to interpolate values onto the target polygons. Unlike area_interpolate, pycno_interpolate does not handle intensive variables.

pycno_8 = tobler.pycno.pycno_interpolate(
1    source_df=simd,
2    target_df=grid_8,
3    variables=["EmpNumDep", "IncNumDep"],
4    cellsize=200,
)

1: Specify the source GeoDataFrame.
2: Specify the target GeoDataFrame.
3: Specify the list of (extensive) variables to be interpolated.
4: The size of a cell of the intermediate raster (see below).

WARNING: nan_treatment='interpolate', however, NaN values detected post convolution. A contiguous region of NaN values, larger than the kernel size, are present in the input array. Increase the kernel size to avoid this. [astropy.convolution.convolve]
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/tobler/pycno/pycno.py:134: RuntimeWarning: divide by zero encountered in scalar divide
  correct = (val - nansum(data[mask])) / mask.sum()
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/tobler/pycno/pycno.py:145: RuntimeWarning: divide by zero encountered in scalar divide
  correct = val / nansum(data[mask])
WARNING: nan_treatment='interpolate', however, NaN values detected post convolution. A contiguous region of NaN values, larger than the kernel size, are present in the input array. Increase the kernel size to avoid this. [astropy.convolution.convolve]
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/tobler/pycno/pycno.py:134: RuntimeWarning: divide by zero encountered in scalar divide
  correct = (val - nansum(data[mask])) / mask.sum()
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/tobler/pycno/pycno.py:145: RuntimeWarning: divide by zero encountered in scalar divide
  correct = val / nansum(data[mask])
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/tobler/pycno/pycno.py:145: RuntimeWarning: invalid value encountered in scalar divide
  correct = val / nansum(data[mask])

From the user perspective, both area_interpolate and pycno_interpolate look similar but the results will likely differ.

pycno_8.explore("EmpNumDep", tiles="CartoDB Positron")

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For a better understanding of the method, you can look at the intermediate array of smoothed values by accessing the pycno function from the depth of the tobler.pycno module.

arr, _, _ = tobler.pycno.pycno.pycno(
    gdf=simd, value_field="EmpNumDep", cellsize=200, verbose=False
)

WARNING: nan_treatment='interpolate', however, NaN values detected post convolution. A contiguous region of NaN values, larger than the kernel size, are present in the input array. Increase the kernel size to avoid this. [astropy.convolution.convolve]
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/tobler/pycno/pycno.py:134: RuntimeWarning: divide by zero encountered in scalar divide
  correct = (val - nansum(data[mask])) / mask.sum()
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/tobler/pycno/pycno.py:145: RuntimeWarning: divide by zero encountered in scalar divide
  correct = val / nansum(data[mask])

The function returns a numpy array of smoothed values and two pieces of information related to CRS, that are not relevant here. The array itself can be explored directly using matplotlib:

_ = plt.imshow(arr)

Theory behind

For more details on the theory behind both areal and Pycnophylactic interpolation methods, check this resource by Comber and Zeng (2022).

Point interpolation

Another case of interpolation is an interpolation of values from a sparse set of points to any other location in between. Let’s explore our options based on the Airbnb data in Edinburgh.

Airbnb in Edinburgh

You are already familiar with the Airbnb data from the Is there a pattern? exercise. The dataset for Edinburgh looks just like that for Prague you used before. The only difference is that, for this section, it is pre-processed to create geometry and remove unnecessary columns.

airbnb = gpd.read_file(
    "https://martinfleischmann.net/sds/interpolation/data/edinburgh_airbnb_2023.gpkg"
)
airbnb.head()

	id	bedrooms	property_type	price	geometry
0	15420	1.0	Entire rental unit	$126.00	POINT (325921.137 674478.931)
1	790170	2.0	Entire condo	$269.00	POINT (325976.36 677655.252)
2	24288	2.0	Entire loft	$95.00	POINT (326069.186 673072.913)
3	821573	2.0	Entire rental unit	$172.00	POINT (326748.646 674001.683)
4	822829	3.0	Entire rental unit	$361.00	POINT (325691.831 674328.127)

Alternative

Instead of reading the file directly off the web, it is possible to download it manually, store it on your computer, and read it locally. To do that, you can follow these steps:

Download the file by right-clicking on this link and saving the file
Place the file in the same folder as the notebook where you intend to read it
Replace the code in the cell above with:

airbnb = gpd.read_file(
    "edinburgh_airbnb_2023.gpkg",
)

You will be focusing on the price of each listing. Let’s check that column.

airbnb.price.head()

0    $126.00
1    $269.00
2     $95.00
3    $172.00
4    $361.00
Name: price, dtype: object

While the values represent numbers, they are encoded as strings starting with the $ sign. That will not work for any interpolation (or any other mathematical method). Use pandas to strip the string of the $ character, remove , and cast the remaining to float.

airbnb["price_float"] = (
1    airbnb.price.str.strip("$").str.replace(",", "").astype(float)
)

1: Access string methods using the .str accessor. Remove , by replacing it with an empty string.

That is set now, you have numbers as expected. Since the dataset represents all types of Airbnb listings, it may be better to select only one type. Filter out only those with 2 bedrooms that can be rented as the whole flat and have a price under £300 per night (there are some crazy outliers).

two_bed_homes = airbnb[
    (airbnb["bedrooms"] == 2)
    & (airbnb["property_type"] == "Entire rental unit")
    & (airbnb["price_float"] < 300)
].copy()
two_bed_homes.head()

	id	bedrooms	property_type	price	geometry	price_float
3	821573	2.0	Entire rental unit	$172.00	POINT (326748.646 674001.683)	172.0
5	834777	2.0	Entire rental unit	$264.00	POINT (324950.724 673875.598)	264.0
6	450745	2.0	Entire rental unit	$177.00	POINT (326493.725 672853.904)	177.0
10	485856	2.0	Entire rental unit	$157.00	POINT (326597.124 673869.551)	157.0
17	51505	2.0	Entire rental unit	$155.00	POINT (325393.807 674177.409)	155.0

Another useful check before heading to the land of interpolation is for duplicated geometries. Having two points at the same place, each with a different value could lead to unexpected results.

two_bed_homes.geometry.duplicated().any()

True

There are some duplicated geometries. Let’s simply drop rows with duplicated locations and keep only the first occurrence (the default behaviour of drop_duplicates).

two_bed_homes = two_bed_homes.drop_duplicates("geometry")

Check how the cleaned result looks on a map.

two_bed_homes.explore("price_float", tiles="CartoDB Positron")

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There are some visible patterns of higher and lower prices, but it may be tricky to do interpolation since the data is a bit too chaotic. In general, point interpolation methods work only when there is a spatial autocorrelation in the data and stronger autocorrelation leads to better interpolation. As you know, spatially lagged variable of already autocorrelated variable shows higher levels of autocorrelation, hence using the spatial lag will be beneficial for this section.

1knn = graph.Graph.build_kernel(two_bed_homes, k=10).transform("r")
two_bed_homes["price_lag"] = knn.lag(two_bed_homes.price_float)
two_bed_homes.explore("price_lag", tiles="CartoDB Positron")

1: Build weights based on 10 nearest neighbours weighted by the distance, so those neighbours that are closer have more power to affect the lag.

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This looks much better. Let’s start with some interpolation. Create another H3 grid, this time with a resolution of 10 (much smaller cells than before). You will use it as a target of interpolation.

grid_10 = tobler.util.h3fy(simd, resolution=10)

/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/pyproj/crs/crs.py:1293: UserWarning: You will likely lose important projection information when converting to a PROJ string from another format. See: https://proj.org/faq.html#what-is-the-best-format-for-describing-coordinate-reference-systems
  proj = self._crs.to_proj4(version=version)

All of the methods below do not expect geometries as an input, but arrays of coordinates. That is an easy task. An array from the grid can be extracted from the centroid of each cell:

grid_coordinates = grid_10.centroid.get_coordinates()
grid_coordinates.head()

	x	y
hex_id
8a1972709a87fff	313332.614823	675061.542574
8a197267534ffff	314797.646111	660473.252161
8a1972758a87fff	316200.159478	665264.079590
8a197274e11ffff	320190.617070	666027.667686
8a1972765977fff	326302.591861	674925.644695

And an array from the Airbnb subset can be retrieved directly from point data:

homes_coordinates = two_bed_homes.get_coordinates()
homes_coordinates.head()

	x	y
3	326748.645636	674001.683211
5	324950.723888	673875.598033
6	326493.725178	672853.903917
10	326597.123684	673869.551295
17	325393.807222	674177.408961

Nearest

The simplest case is the nearest interpolation. That assigns a value to a given point based on the value of the nearest point in the original dataset. You can use the griddata function from the scipy.interpolate module to do that efficiently.

nearest = interpolate.griddata(
1    points=homes_coordinates,
2    values=two_bed_homes.price_lag,
3    xi=grid_coordinates,
4    method="nearest",
)
nearest

1: Use the array of coordinates of Airbnb as input points.
2: The lagged prices are the values linked to points.
3: xi is the array of point coordinates at which to interpolate data.
4: Specify the "nearest" as a method. Check the other options in the documentation yourself but be aware that not all may work well on your data (like in this case).

array([ 78.        , 181.        , 181.        , ..., 188.70398583,
       188.70398583,  84.        ])

The result is provided as a numpy.ndarray object aligned with grid_coordinates, so you can directly assign it as a column.

grid_10["nearest"] = nearest

Check the result of the nearest interpolation on a map.

_ = grid_10.plot('nearest', legend=True)

You can see that the result is actually a Voronoi tessellation. If you plot the original points on top, it is even clearer.

ax = grid_10.plot('nearest', legend=True)
_ = two_bed_homes.plot(ax=ax, color="red", markersize=1)

Nearest interpolation with point overlay

Nearest interpolation may be fine for some use cases, but it is not a good interpolation method in general.

K-nearest neighbours regression

Expanding the nearest method, which takes a single nearest neighbour and allocates the values, you can use the K-nearest neighbours regression (KNN) method. KNN takes into account multiple nearest neighbours and interpolates the value based on all of them.

Uniform

The simple KNN option is to find $K$ nearest neighbours (say 10), treat all equally (uniform weights), and obtain the interpolated value as a mean of values these neighbours have. The implementation of KNN is available in scikit-learn, so it has the API you are already familiar with from the Clustering and regionalisation section.

1interpolation_uniform = neighbors.KNeighborsRegressor(
2    n_neighbors=10, weights="uniform"
)

1: Create the regressor object.
2: Use 10 neighbors and uniform weights.

As with the clustering, use the fit() method to train the object.

interpolation_uniform.fit(
1    homes_coordinates, two_bed_homes.price_lag
)

1: Fit the model based on coordinates and lagged price.

KNeighborsRegressor(n_neighbors=10)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Once the model is ready, you can predict the values on the grid coordinates.

price_on_grid = interpolation_uniform.predict(grid_coordinates)
price_on_grid

array([113.51720211, 112.9987658 , 111.56773545, ..., 120.36697169,
       120.36697169, 120.36697169])

This is, again, a numpy.ndarray that is aligned and can be directly set as a column.

grid_10["knn_uniform"] = price_on_grid

Check the result on a map.

_ = grid_10.plot("knn_uniform", legend=True)

This is already much better than the simple nearest join based on a single neighbor but there are still a lot of artefacts in the areas where you have only a few points far away from each other.

Using KNeighborsRegressor for the nearest join

You have probably figured out that you don’t need scipy.interpolate.griddata to do the nearest join if you have access to sklearn.neighbors.KNeighborsRegressor. With n_neighbors=1, the result should be the same. However, there are situations when only one is available, so it is good to know your options.

Distance-weighted

One way to mitigate the artefacts and take geography a bit more into the equation is to use distance-weighted KNN regression. Instead of treating each neighbour equally, no matter how far from the location of interest they are, you can weigh the importance of each by distance, or to be more precise, by the inverse of the distance. This ensures that points that are closer and considered more important for the interpolation than those that are further away.

interpolation_distance = neighbors.KNeighborsRegressor(
    n_neighbors=10, weights="distance"
)

The only difference is in the weight argument. The rest is the same.

interpolation_distance.fit(
    homes_coordinates, two_bed_homes.price_lag
)

KNeighborsRegressor(n_neighbors=10, weights='distance')

Train the model, predict the values for the grid and assign to the GeoDataFrame.

grid_10["knn_distance"] = interpolation_distance.predict(grid_coordinates)

If you look at the resulting map, you can see that most of the artefacts are gone.

_ = grid_10.plot("knn_distance", legend=True)

Distance band regression

Distance can be employed in another way as well. Instead of selecting the neighbours from which the values are interpolated based on K-nearest neighbours, you can select them based on distance. For example, find all points in a radius of 1000 metres around a location of interest and draw the interpolated value from them. You can also further weigh these neighbours using the inverse distance. The code looks nearly identical. Just use neighbors.RadiusNeighborsRegressor instead of neighbors.KNeighborsRegressor.

interpolation_radius = neighbors.RadiusNeighborsRegressor(
    radius=1000, weights="distance"
)
interpolation_radius.fit(
    homes_coordinates, two_bed_homes.price_lag
)

grid_10["radius"] = interpolation_radius.predict(grid_coordinates)

/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/sklearn/neighbors/_regression.py:509: UserWarning: One or more samples have no neighbors within specified radius; predicting NaN.
  warnings.warn(empty_warning_msg)

Check the result. The issue with sparsely populated areas on the map is a bit different this time. When there is no neighbour in 1000m, the model is not able to produce any prediction and returns np.nan. This may be seen as an issue but it can actually be a strength of the model as it is upfront with the issues caused by sparsity. Note that the model warns you about this situation during the prediction phase.

_ = grid_10.plot("radius", legend=True, missing_kwds={'color': 'lightgrey'})

Ordinary Kriging

The final method of this section is ordinary kriging. Kriging is based on a linear combination of observations that are nearby, like all the cases above, but the model is more complex and takes into account geographical proximity, but also the spatial arrangement of observations and the pattern of autocorrelation. As such, it can be seen as the most robust of the presented options.

You will use the package pyinterpolate to do kriging. It requires all input data in a single numpy.ndarray composed of coordinates and values.

1input_data = np.hstack(
2    [homes_coordinates, two_bed_homes.price_lag.values.reshape(-1, 1)]
)
input_data

1: Use the np.hstack function to horizontally stack the arrays together.
2: two_bed_homes.price_lag.values gives you an underlying array of lagged prices. But it needs to be shaped differently for hstack than it is. reshape(-1, 1) fixes that. Try exploring what happens independently.

array([[3.26748646e+05, 6.74001683e+05, 2.07033397e+02],
       [3.24950724e+05, 6.73875598e+05, 1.54805935e+02],
       [3.26493725e+05, 6.72853904e+05, 1.43865293e+02],
       ...,
       [3.28513265e+05, 6.74048892e+05, 1.06875409e+02],
       [3.26840903e+05, 6.74767224e+05, 1.68848108e+02],
       [3.25415664e+05, 6.73345158e+05, 2.15847334e+02]])

The input_data is now an array pyinterpolate expects. The first step is to build an experimental variogram based on the data and a couple of parameters.

exp_semivar = pyinterpolate.build_experimental_variogram(
    input_array=input_data,
1    step_size=100,
2    max_range=5000,
)

1: step_size is the distance between lags within each point in included in the calculations.
2: max_range is the maximum range of analysis.

The result can be plotted and explored using the plot() method. The experimental variogram is a plot that shows how the semivariance between pairs of sample points changes with distance. The variogram is calculated by taking pairs of sample points and computing the semivariance of the differences in their values at different distances. It measures the degree of relationship between points at different distances. Semivariance is half the variance of the differences in values between pairs of points at a set distance.

exp_semivar.plot()

Next, you need to build a theoretical semivariogram based on the experimental variogram.

semivar = pyinterpolate.build_theoretical_variogram(
1    experimental_variogram=exp_semivar,
2    model_name='linear',
3    sill=exp_semivar.variance,
4    rang=5000,
)

1: The first input is the experimental variogram from the previous step.
2: Type of model used in kriging. "linear" will be the fastest but may not be the best.
3: sill captures the value at which dissimilarity is close to its maximum if the model is bounded. You can pass the variance from the experimental variogram.
4: The semivariogram range is the distance at which spatial correlation exists. It shouldn’t be set at a distance larger than half of the study extent.

Again, you can plot the result. The theoretical variogram is a model or a mathematical function that is fitted to the experimental variogram.

semivar.plot()

You can see that the linear model does not precisely follow the experimental semivariances. Let’s try another option.

semivar_spherical = pyinterpolate.build_theoretical_variogram(
    experimental_variogram=exp_semivar,
1    model_name='spherical',
    sill=exp_semivar.variance,
2    rang=2500,
)

1: Change the model type to spherical.
2: Change the range of the semivariogram to get a better fit.

Let’s see if it is a bit better.

semivar_spherical.plot()

Theoretical semivariogram using spherical model

Now you are ready to use kriging to interpolate data on your grid.

ordinary_kriging = pyinterpolate.kriging(
1    observations=input_data,
2    theoretical_model=semivar_spherical,
3    points=grid_coordinates.values,
4    how="ok",
5    no_neighbors=10,
6    show_progress_bar=False,
)

1: Input data representing Airbnb data, both coordinates and values.
2: Theoretical semivariogram.
3: Coordinates of the grid to interpolate on. Use .values to extract the underlying numpy.ndarray from the DataFrame.
4: Type of kriging. "ok" is for ordinary kriging, "sk" would stand for simple kriging.
5: The number of the nearest neighbours used for interpolation.
6: Whether to show a progress bar or not. Feel free to use True but it breaks the website :).

The resulting ordinary_kriging is a numpy.ndarray with four columns representing predicted value, variance error, x, and y. You can select the first one and assign it as a column.

grid_10["ordinary_kriging"] = ordinary_kriging[:, 0]

And check the result.

_ = grid_10.plot("ordinary_kriging", legend=True)

Ordinary kriging looks great in dense areas but shows yet another type of artefact in sparse areas. While there are ways to mitigate the issue by changing the radius and other parameters of the model, it is worth noting that the reliability of any interpolation method in sparsely populated areas (in terms of the density of original points) is questionable. Kriging has a method to indicate the error rate using the variance error, which may help assess the issue. Variance error is the second column of the ordinary_kriging array.

grid_10["variance_error"] = ordinary_kriging[:, 1]
_ = grid_10.plot("variance_error", legend=True)

You can see from the plot of variance error that anything further away from existing points becomes fairly unreliable. You can, for example, set the specific threshold of the variance error you think is acceptable and treat all the other locations as missing or unreliable.

Check the effect of the theoretical semivariogram model

Explore the difference between kriging using linear and spherical models in theoretical semivariograms. What are the other options and their effects?

Additional reading

Have a look at the chapter Spatial Feature Engineering from the Geographic Data Science with Python by Rey, Arribas-Bel, and Wolf (2023) to learn a bit more about areal interpolation or look at the Areal Interpolation topic of The Geographic Information Science & Technology Body of Knowledge by Comber and Zeng (2022). The same source also contains a nice explanation of kriging by Goovaerts (2019).

References

Comber, A, and W Zeng. 2022. “Areal Interpolation.” In The Geographic Information Science & Technology Body of Knowledge, edited by John P. Wilson. https://doi.org/10.22224/gistbok/2022.2.2.

Goovaerts, P. 2019. “Kriging Interpolation.” In The Geographic Information Science & Technology Body of Knowledge, edited by John P. Wilson. https://doi.org/10.22224/gistbok/2019.4.4.

Rey, Sergio, Dani Arribas-Bel, and Levi John Wolf. 2023. Geographic Data Science with Python. Chapman & Hall/CRC Texts in Statistical Science. London, England: Taylor & Francis.

Tobler, Waldo R. 1979. “Smooth Pycnophylactic Interpolation for Geographical Regions.” Journal of the American Statistical Association 74 (367): 519–30. https://doi.org/10.1080/01621459.1979.10481647.