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Integrating spatial data analysis functionalities in a GIS environment: Spatial Analysis using ArcGIS Engine and R

Hyeongmo Koo1, Yongwan Chun1, Daniel Griffith1

  • 1The University of Texas at Dallas, School of Economic, Political and Policy Sciences, 800 West Campbell Road, Richardson, United States.

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Summary
This summary is machine-generated.

Spatial Analysis using ArcGIS Engine and R (SAAR) integrates Geographic Information Systems (GIS) and spatial data analysis (SDA) tools. This extensible software package offers customization for advanced users and user-friendly interfaces for general GIS users.

Keywords:
.NETGISR-GIS integrationspatial data analysis

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Area of Science:

  • Geographic Information Systems (GIS)
  • Spatial Data Analysis (SDA)
  • Software Development

Background:

  • Existing spatial data analysis (SDA) tools offer limited customization and extensibility.
  • Open-source software strengths in customization and extensibility are often lacking in proprietary SDA tools.
  • There is a need for integrated environments that combine GIS capabilities with advanced SDA functionalities.

Purpose of the Study:

  • To present Spatial Analysis using ArcGIS Engine and R (SAAR), a novel software package for integrated GIS and SDA.
  • To provide an extensible platform for advanced GIS users to customize and implement SDA functions.
  • To offer general GIS users access to advanced SDA tools within a familiar ArcGIS environment with graphical user interfaces.

Main Methods:

  • Developed SAAR as a stand-alone software package within a GIS environment.
  • Integrated R functions for SDA using R.NET within the SAAR package.
  • Developed additional SDA tools in .NET, independent of R, for enhanced functionality.
  • Utilized census data for Texas counties to demonstrate SAAR functionalities.

Main Results:

  • SAAR provides an integrated GIS and SDA environment, enhancing usability for both general and advanced users.
  • The software leverages R functions for complex SDA tasks and .NET for independent tool development.
  • SAAR offers a customizable and extensible platform, reducing development effort for advanced SDA implementations.
  • Demonstrated SAAR's capabilities in analyzing spatial data using real-world census information.

Conclusions:

  • SAAR successfully bridges the gap between GIS and SDA, offering a versatile and user-friendly solution.
  • The software empowers users with limited programming skills through accessible graphical interfaces.
  • SAAR's extensible nature caters to advanced users, facilitating custom SDA function development and implementation.
  • The presented functionalities showcase SAAR's potential for efficient spatial data exploration and analysis.