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Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

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Displaying R spatial statistics on Google dynamic maps with web applications created by Rwui.

Richard Newton¹, Andrew Deonarine, Lorenz Wernisch

¹MRC Biostatistics Unit, Robinson Way, Cambridge, UK. richard.newton@mrc-bsu.cam.ac.uk

International Journal of Health Geographics

|September 25, 2012

Summary

This study presents a method to display spatial statistics output from R scripts directly onto Google dynamic maps. This integration simplifies visualization for spatial statistics and health geography research, making complex data accessible.

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Area of Science:

Spatial Statistics
Geographic Information Systems (GIS)
Data Visualization

Background:

The R project offers extensive spatial statistics packages.
Google dynamic maps provide global map and satellite imagery access.
A method is described to display spatial R output directly on Google dynamic maps.

Purpose of the Study:

To develop a user-friendly method for visualizing spatial statistics output from R on Google dynamic maps.
To integrate R script results seamlessly with interactive web-based mapping.

Main Methods:

A Java-based web application was created to execute R scripts and display results on dynamic maps.
The method was integrated into the R Web User Interface (Rwui) application for ease of use.
Rwui automatically generates web application code, enabling users without programming knowledge to create R-based web applications.

Main Results:

Web applications can now be generated using Rwui to display R script results on Google dynamic maps.
Results can be visualized as discrete markers or continuous overlays.
Users can interact with the map to select regions of interest, with coordinates automatically fed back to the R script.

Conclusions:

The method facilitates easy visualization of spatial statistics results for R users working with real-world data.
Researchers in health geography can directly display their R analysis results on dynamic maps.
Statisticians can create web applications enabling non-R users to perform spatial data analyses.
The approach has potential applications in education for teaching spatial statistics and health geography.