Welcome to FastMapping


Tools for univariate analysis:

  • Depuration
    • Global outliers
    • Spatial outliers
    • Border effects
  • Spatial interpolation
    • Variogram fitting
    • Kriging prediction
  • Classification
    • Fuzzy k-means cluster
Example dataset:

Tools for multivariate analysis:

  • Spatial Principal Components
  • Fuzzy k-means on spatial principal components (KM-sPC)
Example dataset:

We are changing the User Interface and some other functionalities. If you detect some bug, please let us know!

FastMapping can read vector data! (.gpkg, .shp, and others files format)

We have a new installer (only for windows) you can download it from github page

If you have any question please write to fastmapping@agro.unc.edu.ar, brief tutorial is available in this link

You can create an issue or a bug report at github

Data depuration parameters

Global Outliers Options:

Upper and lower threshold boundaries to constrain data within a range of realistic values
Removes all data points which are more than N times the standard deviation from the mean value

Spatial Outliers Options:

Search radius to identify a local neighborhood for each data point

Border Effects:

Remove data points for a given distance from field edges

Select spatial model(s) to fit

Kriging options:

Local neighbourhood selections based on distance as radius (Max.Dist), number of data points (Max, Min), of nearest site of the target point

Output graphical options

Key Scale of predicted values
Key Scale for prediction variance

Fuzzy k-means parameters

Spatial PCA parameters

Neighborhood network

It seems that you want more distance than 1000 m between neighbours... which value do you want?

No depuration process was made.

Download depurated data Download data with finally condition

Variogram Plot

Predicted values

Predicted variance values


Statistical Indices
Download Clasification
Statistical Indices