Source Code on GitHub

SAAR

Spatial Analysis using ArcGIS Engine and R

Setup file (v.1.0.8) User tutorial Sample data

Introducing SAAR

Spatial data analysis (SDA) tools to efficiently handle and explore spatial data have become readily available. Although these SDA tools have their own strengths and purposes, they suffer from limited support in terms of a development environment offering easy customization and high extensibility, a strength of open source software.

This is a stand-alone software package for SDA in a geographic information systems (GIS) environment, called Spatial Analysis using ArcGIS Engine and R (SAAR), which provides an integrated GIS and SDA environment. A set of SDA tools in SAAR utilizes functions in R using R.NET, while other tools were developed in .NET independent of R.

SAAR provides an efficient working environment for both general and advanced GIS users. For general GIS users with limited programming skills, SAAR furnishes advanced SDA tools in a popular ArcGIS environment with graphical user interfaces. For advanced GIS users, SAAR offers an extensible GIS platform to help them customize and implement SDA functions with relatively little development effort.

Functions in SAAR

Functions available in SAAR are classified into the following three broad categories: basic GIS and geo- visualization; exploratory spatial data analysis (ESDA); and confirmatory spatial data analysis (CSDA).

GIS and geovisualization ESDA CSDA
  • Map display and navigation
  • Thematic mapping
  • Tabular data manipulation
  • Uncertainty visualization
  • Map layout
  • Histogram
  • Boxplot
  • Violin plot
  • Scatter plot
  • Quantile-comparison plot
  • Moran scatter plot
  • Variogram-cloud
  • L-functions
  • C-C maps
  • Spatial correlogram
  • Dynamic brushing and linking
  • Data transformation
  • Linear regression
  • Generalized linear model
  • Spatial autoregression
  • Eigenvector spatial filtering
  • Spatially varying coefficients

For more detail, please see Koo et al. (2018) in Transactions in GIS

Reference

Koo, H., Chun, Y., and Griffith, D. A. (2018). Integrating spatial data analysis functionalities in a GIS environment: Spatial Analysis using ArcGIS Engine and R (SAAR). Transactions in GIS, 22(3), 721-736.

SAAR Download

  • Setup file (v.1.0.8)
  • User tutorials
  • Sample data

    • System requirements

  • Operating system: Windows Vista or higher (.Net Framework 4.5)
  • Hard drive space: 278 MB (installation file size: 150 MB)
  • ArcGIS Runtime or Desktop license
  • An additional library (Gurobi optimizer 7.0 or higher) is required for the optimized classification with uncertainty tool.

    • Update notes

  • 1.0.8 update (02/27/2020)
  • 1.0.7 update (05/08/2019)
  • Note: Varying coefficients calculation is revised in 1.0.7 update
  • 1.0.6 update (03/13/2019) (Minor updates in 03/27)
  • 1.0.5 update (09/28/2018)
  • 1.0.4 update (09/08/2018)
  • 1.0.3 update (08/28/2018)

    • ESF tool

    If you do not have an ArcGIS license, the ESF tool can be alternative. This is a stand-alone software package mainly for Moran eigenvector spatial filtering (MESF) in a GIS environment, which is developed by integraing DotSpatial and R using R.NET. This software also supports other spatial statistical tools for linear regression and spatial regression, and furthemore spatially varying coefficients.