Vector analysis

Basic statistics for fields

Generates basic statistics for a field of the attribute table of a vector layer.

Numeric, date, time and string fields are supported.

The statistics returned will depend on the field type.

Statistics are generated as an HTML file and are available in the Processing ‣ Results viewer.

Default menu: Vector ‣ Analysis Tools

Parameters

Label Name Type Description
Input vector INPUT_LAYER [vector: any] Vector layer to calculate the statistics on
Field to calculate statistics on FIELD_NAME [tablefield: any] Any supported table field to calculate the statistics
Statistics OUTPUT_HTML_FILE [file] HTML file for the calculated statistics

Outputs

Label Name Type Description
Statistics OUTPUT_HTML_FILE [file] HTML file with the calculated statistics
Count COUNT [number]  
Number of unique values UNIQUE [number]  
Number of empty (null) values EMPTY [number]  
Number of non-empty values FILLED [number]  
Minimum value MIN [number]  
Maximum value MAX [number]  
Minimum length MIN_LENGTH [number]  
Maximum length MAX_LENGTH [number]  
Mean length MEAN_LENGTH [number]  
Coefficient of Variation CV [number]  
Sum SUM [number]  
Mean value MEAN [number]  
Standard deviation STD_DEV [number]  
Range RANGE [number]  
Median MEDIAN [number]  
Minority (rarest occurring value) MINORITY [number]  
Majority (most frequently occurring value) MAJORITY [number]  
First quartile FIRSTQUARTILE [number]  
Third quartile THIRDQUARTILE [number]  
Interquartile Range (IQR) IQR [number]  

Count points in polygon

Takes a point and a polygon layer and counts the number of points from the first one in each polygon of the second one.

A new polygons layer is generated, with the exact same content as the input polygons layer, but containing an additional field with the points count corresponding to each polygon.

../../../../_images/count_points_polygon.png

The labels identify the point count


An optional weight field can be used to assign weights to each point. Alternatively, a unique class field can be specified. If both options are used, the weight field will take precedence and the unique class field will be ignored.

Default menu: Vector ‣ Analysis Tools

Parameters

Label Name Type Description
Polygons POLYGONS [vector: polygon] Polygon layer whose features are associated with the count of points they contain
Points POINTS [vector: point] Point layer with features to count

Weight field

Optional

WEIGHT [tablefield: any] A field from the point layer. The count generated will be the sum of the weight field of the points contained by the polygon. If the weight field is not numeric, the count will be 0.

Class field

Optional

CLASSFIELD [tablefield: any] Points are classified based on the selected attribute and if several points with the same attribute value are within the polygon, only one of them is counted. The final count of the points in a polygon is, therefore, the count of different classes that are found in it.
Count field name FIELD

[string]

Default: ‘NUMPOINTS’

The name of the field to store the count of points
Count OUTPUT [vector: polygon] Specification of the output layer

Outputs

Label Name Type Description
Count OUTPUT [vector: polygon] Resulting layer with the attribute table containing the new column with the points count

DBSCAN clustering

Clusters point features based on a 2D implementation of Density-based spatial clustering of applications with noise (DBSCAN) algorithm.

The algorithm requires two parameters, a minimum cluster size, and the maximum distance allowed between clustered points.

Parameters

Label Name Type Description
Input layer INPUT [vector: point] Layer to analyze
Minimum cluster size MIN_SIZE

[number]

Default: 5

Minimum number of features to generate a cluster
Maximum distance between clustered points EPS

[number]

Default: 1.0

Distance beyond which two features can not belong to the same cluster (eps)
Cluster field name FIELD_NAME

[string]

Default: ‘CLUSTER_ID’

Name of the field where the associated cluster number shall be stored

Treat border points as noise (DBSCAN*)

Optional

DBSCAN*

[boolean]

Default: False

If checked, points on the border of a cluster are themselves treated as unclustered points, and only points in the interior of a cluster are tagged as clustered.
Clusters OUTPUT [vector: point] Vector layer for the result of the clustering

Outputs

Label Name Type Description
Clusters OUTPUT [vector: point] Vector layer containing the original features with a field setting the cluster they belong to
Number of clusters NUM_CLUSTERS [number] The number of clusters discovered

Distance matrix

Calculates for point features distances to their nearest features in the same layer or in another layer.

Default menu: Vector ‣ Analysis Tools

Parameters

Label Name Type Description
Input point layer INPUT [vector: point] Point layer for which the distance matrix is calculated (from points)
Input unique ID field INPUT_FIELD [tablefield: any] Field to use to uniquely identify features of the input layer. Used in the output attribute table.
Target point layer TARGET [vector: point] Point layer containing the nearest point(s) to search (to points)
Target unique ID field TARGET_FIELD [tablefield: any] Field to use to uniquely identify features of the target layer. Used in the output attribute table.
Output matrix type MATRIX_TYPE

[enumeration]

Default: 0

Different types of calculation are available:

  • 0 — Linear (N * k x 3) distance matrix: for each input point, reports the distance to each of the k nearest target points. The output matrix consists of up to k rows per input point, and each row has three columns: InputID, TargetID and Distance.
  • 1 — Standard (N x T) distance matrix
  • 2 — Summary distance matrix (mean, std. dev., min, max): for each input point, reports statistics on the distances to its target points.
Use only the nearest (k) target points NEAREST_POINTS

[number]

Default: 0

You can choose to calculate the distance to all the points in the target layer (0) or limit to a number (k) of closest features.
Distance matrix OUTPUT [vector: point]  

Outputs

Label Name Type Description
Distance matrix OUTPUT [vector: point] Point (or MultiPoint for the “Linear (N * k x 3)” case) vector layer containing the distance calculation for each input feature. Its features and attribute table depend on the selected output matrix type.

Distance to nearest hub (line to hub)

Creates lines that join each feature of an input vector to the nearest feature in a destination layer. Distances are calculated based on the center of each feature.

../../../../_images/distance_hub.png

Display the nearest hub for the red input features

Parameters

Label Name Type Description
Source points layer INPUT [vector: any] Vector layer for which the nearest feature is searched
Destination hubs layer HUBS [vector: any] Vector layer containing the features to search for
Hub layer name attribute FIELD [tablefield: any] Field to use to uniquely identify features of the destination layer. Used in the output attribute table
Measurement unit UNIT

[enumeration]

Default: 0

Units in which to report the distance to the closest feature:

  • 0 — Meters
  • 1 — Feet
  • 2 — Miles
  • 3 — Kilometers
  • 4 — Layer units
Hub distance OUTPUT [vector: line] Line vector layer for the distance matrix output

Outputs

Label Name Type Description
Hub distance OUTPUT [vector: line] Line vector layer with the attributes of the input features, the identifier of their closest feature and the calculated distance.

Distance to nearest hub (points)

Creates a point layer representing the center of the input features with the addition of two fields containing the identifier of the nearest feature (based on its center point) and the distance between the points.

Parameters

Label Name Type Description
Source points layer INPUT [vector: any] Vector layer for which the nearest feature is searched
Destination hubs layer HUBS [vector: any] Vector layer containing the features to search for
Hub layer name attribute FIELD [tablefield: any] Field to use to uniquely identify features of the destination layer. Used in the output attribute table
Measurement unit UNIT

[enumeration]

Default: 0

Units in which to report the distance to the closest feature:

  • 0 — Meters
  • 1 — Feet
  • 2 — Miles
  • 3 — Kilometers
  • 4 — Layer units
Hub distance OUTPUT [vector: point] Point vector layer for the distance matrix output.

Outputs

Label Name Type Description
Hub distance OUTPUT [vector: point] Point vector layer with the attributes of the input features, the identifier of their closest feature and the calculated distance.

Join by lines (hub lines)

Creates hub and spoke diagrams by connecting lines from points on the spoke layer to matching points in the hub layer.

Determination of which hub goes with each point is based on a match between the Hub ID field on the hub points and the Spoke ID field on the spoke points.

If input layers are not point layers, a point on the surface of the geometries will be taken as the connecting location.

../../../../_images/join_lines.png

Join points on common field

Parameters

Label Name Type Description
Hub layer HUBS [vector: any] Input layer
Hub ID field HUB_FIELD [tablefield: any] Field of the hub layer with ID to join

Hub layer fields to copy (leave empty to copy all fields)

Optional

HUB_FIELDS [tablefield: any] [list] The field(s) of the hub layer to be copied. If no field(s) are chosen all fields are taken.
Spoke layer SPOKES [vector: any] Additional spoke point layer
Spoke ID field SPOKE_FIELD [tablefield: any] Field of the spoke layer with ID to join

Spoke layer fields to copy (leave empty to copy all fields)

Optional

SPOKE_FIELDS [tablefield: any] [list] Field(s) of the spoke layer to be copied. If no fields are chosen all fields are taken.
Hub lines OUTPUT [vector: lines] The resulting line layer

Outputs

Label Name Type Description
Hub lines OUTPUT [vector: lines] The resulting line layer

K-means clustering

Calculates the 2D distance based k-means cluster number for each input feature.

K-means clustering aims to partition the features into k clusters in which each feature belongs to the cluster with the nearest mean. The mean point is represented by the barycenter of the clustered features.

If input geometries are lines or polygons, the clustering is based on the centroid of the feature.

../../../../_images/kmeans.png

A five class point clusters

Parameters

Label Name Type Description
Input layer INPUT [vector: any] Layer to analyze
Number of clusters CLUSTERS

[number]

Default: 5

Number of clusters to create with the features
Cluster field name FIELD_NAME

[string]

Default: ‘CLUSTER_ID’

Name of the cluster number field
Clusters OUTPUT [vector: any] Vector layer for generated the clusters

Outputs

Label Name Type Description
Clusters OUTPUT [vector: any] Vector layer containing the original features with a field specifying the cluster they belong to

List unique values

Lists unique values of an attribute table field and counts their number.

Default menu: Vector ‣ Analysis Tools

Parameters

Label Name Type Description
Input layer INPUT [vector: any] Layer to analyze
Target field(s) FIELDS [tablefield: any] Field to analyze
Unique values OUTPUT [table] Summary table layer with unique values
HTML report OUTPUT_HTML_FILE [html] HTML report of unique values in the Processing ‣ Results viewer

Outputs

Label Name Type Description
Unique values OUTPUT [table] Summary table layer with unique values
HTML report OUTPUT_HTML_FILE [html] HTML report of unique values. Can be opened from the Processing ‣ Results viewer
Total unique values TOTAL_VALUES [number] The number of uniqe values in the input field
UNIQUE_VALUES Unique values [string] A string with the comma separated list of unique values found in the input field

Mean coordinate(s)

Computes a point layer with the center of mass of geometries in an input layer.

An attribute can be specified as containing weights to be applied to each feature when computing the center of mass.

If an attribute is selected in the parameter, features will be grouped according to values in this field. Instead of a single point with the center of mass of the whole layer, the output layer will contain a center of mass for the features in each category.

Default menu: Vector ‣ Analysis Tools

Parameters

Label Name Type Description
Input layer INPUT [vector: any] Input vector layer

Weight field

Optional

WEIGHT [tablefield: numeric] Field to use if you want to perform a weighted mean
Unique ID field UID [tablefield: numeric] Unique field on which the calculation of the mean will be made
Mean coordinates OUTPUT [vector: point] The (point vector) layer for the result

Outputs

Label Name Type Description
Mean coordinates OUTPUT [vector: point] Resulting point(s) layer

Nearest neighbour analysis

Performs nearest neighbor analysis for a point layer.

Output is generated as an HTML file with the computed statistical values:

  • Observed mean distance
  • Expected mean distance
  • Nearest neighbour index
  • Number of points
  • Z-Score

Default menu: Vector ‣ Analysis Tools

Parameters

Label Name Type Description
Input layer INPUT [vector: point] Point vector layer to calculate the statistics on
Nearest neighbour OUTPUT_HTML_FILE [html] HTML file for the computed statistics

Outputs

Label Name Type Description
Nearest neighbour OUTPUT_HTML_FILE [html] HTML file with the computed statistics
Observed mean distance OBSERVED_MD [number] Observed mean distance
Expected mean distance EXPECTED_MD [number] Expected mean distance
Nearest neighbour index NN_INDEX [number] Nearest neighbour index
Number of points POINT_COUNT [number] Number of points
Z-Score Z_SCORE [number] Z-Score
Points [vector: point]
Point vector layer to calculate the statistics on.

Outputs

Nearest neighbour [html]
HTML file in output with the computed statistics.

Statistics by categories

Calculates statistics of fields depending on a parent class.

For numerical fields, a table layer with the following statistics is output:

  • count
  • unique
  • min
  • max
  • range
  • sum
  • mean
  • median
  • stdev
  • minority
  • majority
  • q1
  • q3
  • iqr

For string fields, the following statistics will be calculated:

  • count
  • unique
  • empty
  • filled
  • min
  • max
  • min_length
  • max_length
  • mean_length

Parameters

Parameters

Label Name Type Description
Input vector layer INPUT [vector: any] Input vector layer with unique classes and values

Field to calculate statistics on (if empty, only count is calculated)

Optional

VALUES_FIELD_NAME [tablefield: any] If empty only the count will be calculated
Field(s) with categories CATEGORIES_FIELD_NAME [vector: any] [list] The fields that (combined) define the categories
Statistics by category OUTPUT [table] Table for the generated statistics

Outputs

Label Name Type Description
Statistics by category OUTPUT [table] Table containing the statistics

Sum line lengths

Takes a polygon layer and a line layer and measures the total length of lines and the total number of them that cross each polygon.

The resulting layer has the same features as the input polygon layer, but with two additional attributes containing the length and count of the lines across each polygon.

The names of these two fields can be configured in the algorithm parameters.

Default menu: Vector ‣ Analysis Tools

Parameters

Label Name Type Description
Lines LINES [vector: line] Input vector line layer
Polygons POLYGONS [vector: polygon] Polygon vector layer
Lines length field name LEN_FIELD

[string]

Default: ‘LENGTH’

Name of the field for the lines length
Lines count field name COUNT_FIELD

[string]

Default: ‘COUNT’

Name of the field for the lines count
Line length OUTPUT [vector: polygon] The output polygon vector layer

Outputs

Label Name Type Description
Line length OUTPUT [vector: polygon] Polygon output layer with fields of lines length and line count