Geo sampling

Say you want to learn about the average number of potholes per kilometer of street in a city. Or estimate a similar such quantity. To estimate the quantity, you need to sample locations on the streets. This package helps you sample those locations.


The gisrastertools is a python module that provides a fast and flexible tool to work with GIS raster files. It includes tools to

  • Given a point (lat,lon) find its location in a raster
  • Aggregate rasters to lower resolutions
  • Align two rasters of different sizes to common area and size
  • Get all the geographical information of raster
  • Create GeoTiff files easily
  • Load GeoTiff files as masked numpy rasters


The parser was written to have a clean and complete parser for WKT. Other WKT to python parsers use regular expression to do the same and are more or less complete. I wanted to have a reference implementation that could handle any kind of valid WKT that you throw at it. You can also use it as a reference if you want to write your own parser with grako.


GottenGeography is a GNOME application that aims to make it easy to record geotags into your photographs. If you have a GPS device, GottenGeography can load it’s GPX data and directly compare timestamps between the GPX data and the photos, automatically determining where each photo was taken. If you do not have a GPS device, GottenGeography allows you to manually place photos onto a map, and then record those locations into the photos.


This is a Python rewrite of the code used to create the Visualizing Facebook Friends visualization in 2010.

The original code was written in R and was built specifically around the Facebook dataset. This rewrite is as a Python module and is built to work on top of any dataset.

If you are looking to visualize a few (<10,000) coordinate pairs, matplotlib with basemap will be more flexible. The visualization implemented by this module is useful when the data alone are sufficient to show the geography.

The algorithm uses a heuristic which attempts to visualize the structure of the pairs rather than their relative importance. In interpreting the results, you should not come to any conclusions about the relative importance of different coordinate pairs.

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