Spatial Analysis in Macroecology (SAM)

Written by Grant Hamilton
Basic Information
Name: Spatial Analysis in Macroecology (v. 4.0)
Acronym: SAM
Author/Owner/Steward: Thiago Fernando Rangel (Department of Ecology and Evolutionary Biology, University of Connecticut); Jose Alexandre F. Diniz (Department of Biology, Federal University of Goiás, Brazil); Luis Mauricio Bini (Department of Biology, Federal University of Goiás)
Type: Free standalone program
Platform: Windows


SAM is a program for performing spatial statistics functions, particularly surface pattern analysis. The program features a graphical user interface with drop-down menu navigation. ESRI shapefiles, rasters, Excel spreadsheets, DBASE (.dbf) tables, and text files are supported. SAM allows users to create and edit publishable quality maps, charts, and other graphics to display data. It combines the power of a statistical analysis program with the geospatial capabilities of a GIS program. SAM requires minimal computer resources and takes full advantage of multi-core processors. All functions can be completed with GUI; no command line code is required. The following tools are available in SAM:

  • Graphical Exploratory Data Analysis (GEDA) tools
  • Moran’s I and Auto-Correlogram
  • Spatial Correlation
  • Regression and Partial Regression
  • Presence/Absence Matrix (PAM) and Species Attributes Mapping
  • Principal Component Analysis
  • Auto-Regression: Lagged
  • Auto-Regression: SAR/CAR
  • Auto-Regression: GLS
  • Spatial Eigenvector Mapping (SEVM)
  • Model Selection and Multi-Model Inference
  • Logistic Regression
  • Geographically Weighted Regression (GWR)
  • GIS Processing and Mapping
  • Pattern Finder
  • Ripley’s K
  • Join-Count Analysis
  • Mantel Test


SAM has many statistical tools available. Results can be visualized into publishable quality charts and graphs.

Unique Features

Graphical Exploratory Data Analysis (GEDA) refers to the ability to display statistical data in a visual form. Examples include box plots, quantile normal plots, histograms, stem and leaf plots, scatter plots, pie charts, bar graphs, pictograms, and contingency tables.

A species matrix includes species names as rows and traits as columns. Individual species in the presence/absence matrix a trait of interest can be selected (e.g. species larger than average body size), or species traits can be mapped in the geographical space, given the species assemblage in each location.

Spatial eigenvector mapping is a variation of Moran’s I measure of autocorrelation. Spatial eigenvectors maximize the spatial autocorrelation of Moran’s I.


Rangel, T.F., Diniz-Filho, A.F., Bini, L.M. (2010). SAM: a comprehensive application for Spatial Analysis in Macroecology. Ecography. Vol. 33, pp. 46-50. doi: 10.1111/j.1600-0587.2009.06299.x.

  • Discussion of SAM’s capabilities by its creators.

Rangel, T.F., Diniz-Filho, A.F., Bini, L.M. (2006). Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecology and Biogeography. 15(4), pp. 321 – 327. doi: 10.1111/j.1466-822X.2006.00237.x.

  • Focuses on SAM’s Moran’s I autocorrelation function and its ability to calculate spatial autocorrelation based on a range of matrices describing spatial relationships.

Rangel, T.F., Diniz-Filho, A.F., Bini, L.M (n.d.). SAM: Spatial Analysis in Macroecology.

  • SAM website with mirrors to download the program.

Contact Information

Contact Name: Thiago Fernando Rangel
Contact Email:

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