for people with large data sets
One way to find tools suited to your task is to browse galleries of innovative visualizations such as Visual Complexity and Infosthetics. Longer lists of tools appear at WikiViz and AI3. What is Advertising SomeVisits
Google Maps: This classic solution adds spice to any geographic data.
modest maps: an open source Flash mapping solution that works with most major mapping backends.
Open Layers: An open-source Google Maps clone.
Mapstraction: An abstraction layer for different JS mapping tools including Google Maps, OpenLayers/OpenStreetMap, poly9 FreeEarth, and more.
SOCR Tools: Web-based applets, computational libraries, educational and data resources
Matplotlib: Python 2D plotting library. Useful for generating all sorts of graphs/plots/charts. Highly recommended.
pygooglechart: is a complete Python wrapper for the Google Chart API.
Chart Chooser recommends chart formats based on the type of thing you're trying to show.
SOCR_Charts: The largest openly web-accessible collection of tools for Exploratory Data Analysis (EDA)
Excel Chart Cleaner removes chartjunk from Excel spreadsheets.
Simile Timeline: Great for visualizing time-based data.
many eyes: While they require you to upload your data to them, this site makes getting some good visualizations off the ground quite fast.
Swivel: Another site to upload your data and generate some basic graphs.
Most graph tools seem to concentrate on making cluster-tree graphs, using some form of ball-and-spring model.
The best option for visualizing massive network graphs appears to be Cytoscape, a Java-based Open Source graph visualizer that scales well and has a flexible plugin structure. It maintains interactivity to the 100,000+ nodes and edges level.
The AI3 Blog has a far more comprehensive review of tools and approaches to large-graph visualization, including links to a couple dozen more tools.
Circos is notable for its innovative and beautiful ring-graph visualizations.
Graphviz: Graphviz layout programs take descriptions of graphs in a simple text language, and make diagrams in several useful formats such as images and SVG for web pages, Postscript for inclusion in PDF or other documents; or display in an interactive graph browser. (Graphviz also supports GXL, an XML dialect.)
These tools will scale efficiently for massive graphs that call for parallel processing across computer clusters:
These are typically written in Flash or Java and typically let you explore small local slices from a much larger remote graph.
The Prefuse Gallery shows several beautiful-looking graph visualizations that appear to scale to ~1000s of nodes.
SpringGraph - a simple but clean Flash (Flex) spring graph tool.
Eye-Sys: Windows visualization app launched in late 2007; offers a building-block approach for creating a huge variety of visualization systems. Version 2.0 released in Sept. 2008 includes a suite of graph theory objects, enabling faster and more customizable graphing in 2D & 3D
Processing: This classic workhorse will let you browse through linked RDF data from the comfort of your browser. While I admit most of its examples seem overly artsy, Processing can genuinely be used on real data sets -- indeed, there's a whole book on it.
nodebox: A Mac OS X application, similar to Processing, but using Python as a basis.
prefuse: A visualization toolkit for the Java programming language.
prefuse flare: provides much of the same functionality for Actionscript 3.
Google Visualization API: Embed visualizations directly into your website: Display attractive data on your website by choosing from a vast array of visualizations created by the developer community.
last modified October 3, 2012