Spatial Data Calculator
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Point data
NN distances
All distances
Local clustering
Neighbour categories
K(t) and O(r) function
G(r), F(r), J(r) function
Spatial clusters
Thinning
Gaps
Local statistics
Interpolation
Directions
Linear data
Gradient
Line generalization
Statistics along a line
Relationships
Similarity and distance
Non-spatial clusters
Mantel test
Categorical overlay
Variogram
Autocorrelation
Local autocorrelation
Spatial correlation
Local correlation
3D autocorrelation
Non-spatial correlation
Non-spatial regression
Patterns
Random points
Suitability mapping
Frequency
Map sheet
Estonia
Probability
Map sheet
Estonia
kNN
Map sheet
Estonia
dNN
Map sheet
Estonia
SumSim
Map sheet
Estonia
Tests
Univariate sample
Difference tests
χ2 test
Classification
ROC curve
Regions of frequency
Coordinates
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ASP.NET Core version of this application
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Spatial clusters from ID, X, Y data
Density-based spatial clustering of applications with noise (DBSCAN)
Quality threshold clustering (QT)
k means clustering
Source data
Source data must be in columns as: [ID] [X] [Y] (every object in a separate row, column separator can be space, tab or semicolon, decimal separator can be either point or comma). Lines beginning with a character (field names) are excluded. If the last dimension has different units and meaning than X and Y (e.g. means date and time) then it must contain double pecision floating point numbers, which are representing days from the beginning of year 1900 in case of general DateTime format.
2D
X Y DateTime
Area borders
E min
E max
N min
N max
Boundaries from data
Estonian boundaries
Minimum number of objects in cluster
Search radius (unit as for coordinates)
N direction first
E direction first
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