Generating Cache Density Plots
About Density Plots
Cache Density plots are nothing more than a specialized google earth file that includes horizontal colored planes representing how many caches are undereath each square. Red squares have less caches underneath, purple have more.
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|Looking down at a density plot|| Looking between the ground and the density plane|
(in the sky)
Command Line Instructions
geoqo -s set:import:myPocketQuery -e out.kml
Creating a cache density plot will let you see where the most dense caches of an area are. To do this, you need to export data to a "kml" google earth file. The density plot will be included with it.
You can either create a kml export from the command line using arbitrary search terms, or you can use the GUI by simply running "geoqo".
Then select the pocket query (for example) name you want to export in a density plot, and change the output format to "Google Earth (KML)" and the file name to a .kml ending of your choice.
The next screen will let you set parameters that affect the .kml file. There are a lot, so I suggest starting with the default and working from there.
Then, launch google earth and open the resulting file. The "red" areas will be the areas with the least number of caches in them, and purple will have the most.
Graphical User Interface (GUI)
- Preparing to go geocaching:
- Advanced Graphical User Interface (GUI) Topics
Command Line Tutorial
- Importing your waypoints
- Searching through you waypoints
- Displaying your waypoints
- Exporting your waypoints
Advanced GeoQO Features
- Advanced ways of Generating Cache Density Plots
- How to find good geocaches
Writing code for GeoQO
- Writing Scripts
- Writing Plugins
- Breakdown of the KML Plugin (one of the most advanced export plugins)
Tips and Tricks
- Saved Specifications: Using saved specification strings
- Differences since last import: find all the differences in a pocket-query (or whatever) since your last import
- Multiple Top10 Analysis: analyze your search results for breakdown in information
- Scripting Examples for Mulitple Top10 Outputs
- Using SQLite to do your own analysis