Create univariate spatial point data over an interval:

Plot the data:

Create a 2D spatial point from random points in a disk:

Visualize the points:

Create 3D spatial point data:

The data points and the region:

Identify a hardcore radius by plotting the vectors between all pairs of different data points:

The plot of the vectors is called a Fry plot, and can be extracted directly from the spatial point data:

Construct SpatialPointData with random geodetic points:

Extract the locations of the largest cities in the United States:

Create SpatialPointData with the region specified by the country polygon:

Plot the locations of the cities:

Obtain a regular grid over Mexico using GeoElevationData:

The grid points are given in the form of an array:

Flatten to obtain a list of points with elevations:

Create spatial point data:

Obtain data from a ResourceObject:

SpatialPointData is defined with respect to an observation region in which the points occur:

Only the points within the specified observation region are retained:

If the observation region is not specified, it is estimated with the Ripley–Rasson estimator (a dilation of the convex hull):

Visualize the estimated region together with the points:

Create new spatial point data by intersecting it with another region:

New data:

New observation region:

Visualize the regions:

Create new spatial point data with a larger new region by reconstruction:

The new observation region is exactly the new region given:

Visualize the regions:

Translate SpatialPointData:

Define and apply a translation transform:

Plot the original data and region with the transformed results:

Rotate SpatialPointData:

Define and apply a rotating transform:

Plot the original data and region with the transformed results:

Rescale SpatialPointData:

Define and apply a scaling transform:

Plot the original data and region with the transformed results:

Extract the names and locations of the largest cities in Ireland:

Specify the region to be the country polygon:

List the annotations:

Annotations can be also accessed using specific annotation functions:

Extract the values for a given key:

Plot the data with the names in tooltips:

Add both categorical and numeric annotations to a point pattern:

Extract annotations:

Annotations are automatically labeled:

Plot data with the annotations:

Provide explicit keys for the annotations:

Extract the keys of annotations:

Show the annotations:

Extract a particular annotation:

Define spatial point data with multiple annotations of each type:

Annotation keys are shown in the summary:

Assign annotations to a subset of the pattern using SparseArray:

Show annotations:

Visualize the values:

Add annotations to spatial data defined with GeoPosition:

Show annotations:

Visualize the points with annotations:

Create spatial point data with two point configurations and different annotations:

Select first point configuration only:

Create new spatial point data by reversing the order of point configurations:

Create copies of point configurations:

Extract all the point configurations as individual SpatialPointData objects: