24.1.4. Interpolation

24.1.4.1. Heatmap (kernel density estimation)

Creates a density (heatmap) raster of an input point vector layer using kernel density estimation.

The density is calculated based on the number of points in a location, with larger numbers of clustered points resulting in larger values. Heatmaps allow easy identification of hotspots and clustering of points.

Parameters

Label	Nimi	Type	Kuvaus
Point layer	INPUT	[vector: point]	Point vector layer to use for the heatmap
Radius	RADIUS	[number] Default: 100.0	Heatmap search radius (or kernel bandwidth) in map units. The radius specifies the distance around a point at which the influence of the point will be felt. Larger values result in greater smoothing, but smaller values may show finer details and variation in point density.
Output raster size	PIXEL_SIZE	[number] Default: 0.1	Pixel size of the output raster layer in layer units. In the GUI, the size can be specified by the number of rows (Number of rows) / columns (Number of columns) or the pixel size( Pixel Size X / Pixel Size Y). Increasing the number of rows or columns will decrease the cell size and increase the file size of the output raster. The values in Rows, Columns, Pixel Size X and Pixel Size Y will be updated simultaneously - doubling the number of rows will double the number of columns, and the cell size will be halved. The extent of the output raster will remain the same (approximately).
Radius from field Optional	RADIUS_FIELD	[tablefield: numeric]	Sets the search radius for each feature from an attribute field in the input layer.
Weight from field Optional	WEIGHT_FIELD	[tablefield: numeric]	Allows input features to be weighted by an attribute field. This can be used to increase the influence certain features have on the resultant heatmap.
Kernel shape	KERNEL	[enumeration] Default: 0	Controls the rate at which the influence of a point decreases as the distance from the point increases. Different kernels decay at different rates, so a triweight kernel gives features greater weight for distances closer to the point then the Epanechnikov kernel does. Consequently, triweight results in “sharper” hotspots and Epanechnikov results in “smoother” hotspots. There are many shapes available (please see the Wikipedia page for further information): 0 — Quartic 1 — Triangular 2 — Uniform 3 — Triweight 4 — Epanechnikov
Decay ratio (Triangular kernels only) Optional	DECAY	[number] Default: 0.0	Can be used with Triangular kernels to further control how heat from a feature decreases with distance from the feature. A value of 0 (=minimum) indicates that the heat will be concentrated in the center of the given radius and completely extinguished at the edge. A value of 0.5 indicates that pixels at the edge of the radius will be given half the heat as pixels at the center of the search radius. A value of 1 means the heat is spread evenly over the whole search radius circle. (This is equivalent to the ‘Uniform’ kernel.) A value greater than 1 indicates that the heat is higher towards the edge of the search radius than at the center.
Output value scaling	OUTPUT_VALUE	[enumeration] Default: Raw	Allow to change the values of the output heatmap raster. One of: 0 — Raw 1 — Scaled
Heatmap	OUTPUT	[raster] Default: [Save to temporary file]	Specify the output raster layer with kernel density values. One of: Save to a Temporary File Save to File… The file encoding can also be changed here.

Tulokset

Label	Nimi	Type	Kuvaus
Heatmap	OUTPUT	[raster]	Raster layer with kernel density values

Example: Creating a Heatmap

For the following example, we will use the airports vector point layer from the QGIS sample dataset (see Downloading sample data). Another excellent QGIS tutorial on making heatmaps can be found at http://qgistutorials.com.

In Fig. 24.2, the airports of Alaska are shown.

Fig. 24.2 Airports of Alaska

1. Open the Heatmap (Kernel Density Estimation) algorithm from the QGIS Interpolation group

2. In the Point layer field, select airports from the list of point layers loaded in the current project.

3. Change the Radius to 1000000 meters.

4. Change the Pixel size X to 1000. The Pixel size Y, Rows and Columns will be automatically updated.

5. Click on Run to create and load the airports heatmap (see Fig. 24.4).

Fig. 24.3 The Heatmap Dialog

QGIS will generate the heatmap and add it to your map window. By default, the heatmap is shaded in greyscale, with lighter areas showing higher concentrations of airports. The heatmap can now be styled in QGIS to improve its appearance.

Fig. 24.4 The heatmap after loading looks like a grey surface

1. Open the properties dialog of the heatmap_airports layer (select the layer heatmap_airports, open the context menu with the right mouse button and select Properties).

2. Select the Symbology tab.

3. Change the Render type to ’Singleband pseudocolor’.

4. Select a suitable Color ramp , for instance YlOrRd.

5. Click the Classify button.

6. Press OK to update the layer.

The final result is shown in Fig. 24.5.

Fig. 24.5 Styled heatmap of airports of Alaska

Python code

Algorithm ID: qgis:heatmapkerneldensityestimation

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.4.2. IDW Interpolation

Generates an Inverse Distance Weighted (IDW) interpolation of a point vector layer.

Sample points are weighted during interpolation such that the influence of one point relative to another declines with distance from the unknown point you want to create.

The IDW interpolation method also has some disadvantages: the quality of the interpolation result can decrease, if the distribution of sample data points is uneven.

Furthermore, maximum and minimum values in the interpolated surface can only occur at sample data points.

Parameters

Label	Nimi	Type	Kuvaus
Input layer(s)	INTERPOLATION_DATA	[string]	Vector layer(s) and field(s) to use for the interpolation, coded in a string (see the ParameterInterpolationData class in InterpolationWidgets for more details). The following GUI elements are provided to compose the interpolation data string: Vector layer [vector: any] Interpolation attribute [tablefield: numeric]: Attribute to use in the interpolation Use Z-coordinate for interpolation [boolean]: Uses the layer’s stored Z values (Default: False) For each of the added layer-field combinations, a type can be chosen: Points Structured lines Break lines In the string, the layer-field elements are separated by '::|::'. The sub-elements of the layer-field elements are separated by '::~::'.
Distance coefficient P	DISTANCE_COEFFICIENT	[number] Default: 2.0	Sets the distance coefficient for the interpolation. Minimum: 0.0, maximum: 100.0.
Extent (xmin, xmax, ymin, ymax)	EXTENT	[extent]	Extent of the output raster layer. You have to declare the output extent by either choosing it from the map canvas, selecting it from another layer or type it manually.
Output raster size	PIXEL_SIZE	[number] Default: 0.1	Pixel size of the output raster layer in layer units. In the GUI, the size can be specified by the number of rows (Number of rows) / columns (Number of columns) or the pixel size( Pixel Size X / Pixel Size Y). Increasing the number of rows or columns will decrease the cell size and increase the file size of the output raster. The values in Rows, Columns, Pixel Size X and Pixel Size Y will be updated simultaneously - doubling the number of rows will double the number of columns, and the cell size will be halved. The extent of the output raster will remain the same (approximately).
Interpolated	OUTPUT	[raster] Default: [Save to temporary file]	Raster layer of interpolated values. One of: Save to a Temporary File Save to File… The file encoding can also be changed here.

Tulokset

Label	Nimi	Type	Kuvaus
Interpolated	OUTPUT	[raster]	Raster layer of interpolated values

Python code

Algorithm ID: qgis:idwinterpolation

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.4.3. Line Density

Calculates for each raster cell, the density measure of linear features within a circular neighbourhood. This measure is obtained by summing all the line segments intersecting the circular neighbourhood and dividing this sum by the area of such neighbourhood. A weighting factor can be applied to the line segments.

Fig. 24.6 Line density example. Input layer source: Roads Overijssel - The Netherlands (OSM).

Parameters

Label	Nimi	Type	Kuvaus
Input line layer	INPUT	[vector: any]	Input vector layer containing line features
Weight field	WEIGHT	[number]	Field of the layer containing the weight factor to use during the calculation
Search Radius	RADIUS	[number] Default: 10	Radius of the circular neighbourhood. Units can be specified here.
Pixel size	PIXEL_SIZE	[number] Default: 10	Pixel size of the output raster layer in layer units. The raster has square pixels.
Line density raster	OUTPUT	[raster] Default: [Save to temporary file]	The output as a raster layer. One of: Save to a Temporary File Save to File… The file encoding can also be changed here.

Tulokset

Label	Nimi	Type	Kuvaus
Line density raster	OUTPUT	[raster]	The output line density raster layer.

Python code

Algorithm ID: native:linedensity

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.

24.1.4.4. TIN Interpolation

Generates a Triangulated Irregular Network (TIN) interpolation of a point vector layer.

With the TIN method you can create a surface formed by triangles of nearest neighbor points. To do this, circumcircles around selected sample points are created and their intersections are connected to a network of non overlapping and as compact as possible triangles. The resulting surfaces are not smooth.

The algorithm creates both the raster layer of the interpolated values and the vector line layer with the triangulation boundaries.

Parameters

Label	Nimi	Type	Kuvaus
Input layer(s)	INTERPOLATION_DATA	[string]	Vector layer(s) and field(s) to use for the interpolation, coded in a string (see the ParameterInterpolationData class in InterpolationWidgets for more details). The following GUI elements are provided to compose the interpolation data string: Vector layer [vector: any] Interpolation attribute [tablefield: numeric]: Attribute to use in the interpolation Use Z-coordinate for interpolation [boolean]: Uses the layer’s stored Z values (Default: False) For each of the added layer-field combinations, a type can be chosen: Points Structured lines Break lines In the string, the layer-field elements are separated by '::|::'. The sub-elements of the layer-field elements are separated by '::~::'.
Interpolation method	METHOD	[enumeration] Default: 0	Set the interpolation method to be used. One of: Linear Clough-Toucher (cubic)
Extent (xmin, xmax, ymin, ymax)	EXTENT	[extent]	Extent of the output raster layer. You have to declare the output extent by either choosing it from the map canvas, selecting it from another layer or type it manually.
Output raster size	PIXEL_SIZE	[number] Default: 0.1	Pixel size of the output raster layer in layer units. In the GUI, the size can be specified by the number of rows (Number of rows) / columns (Number of columns) or the pixel size( Pixel Size X / Pixel Size Y). Increasing the number of rows or columns will decrease the cell size and increase the file size of the output raster. The values in Rows, Columns, Pixel Size X and Pixel Size Y will be updated simultaneously - doubling the number of rows will double the number of columns, and the cell size will be halved. The extent of the output raster will remain the same (approximately).
Interpolated	OUTPUT	[raster] Default: [Save to temporary file]	The output TIN interpolation as a raster layer. One of: Save to a Temporary File Save to File… The file encoding can also be changed here.
Triangulation	TRIANGULATION	[vector: line] Default: [Skip output]	The output TIN as a vector layer. One of: Skip Output Create Temporary Layer (TEMPORARY_OUTPUT) Save to File… Save to Geopackage… Save to PostGIS Table…

Tulokset

Label	Nimi	Type	Kuvaus
Interpolated	OUTPUT	[raster]	The output TIN interpolation as a raster layer
Triangulation	TRIANGULATION	[vector: line]	The output TIN as a vector layer.

Python code

Algorithm ID: qgis:tininterpolation

import processing
processing.run("algorithm_id", {parameter_dictionary})

The algorithm id is displayed when you hover over the algorithm in the Processing Toolbox. The parameter dictionary provides the parameter NAMEs and values. See Using processing algorithms from the console for details on how to run processing algorithms from the Python console.