GDAL is one of the most frequently used utilities in my toolkit. I am writing this post to make it easier for others to install it from scratch on their macs.
The traditional way has always been to visit the dear old kyngchaos.com, and install “GDAL Complete” Framework via deb installer. Do make sure that GDAL Framework is in your path otherwise something like this always helps
Recently, I have been working on algorithms which need elevation data as well as Land Cover data, with world coverage. Google has an excellent elevation API however free usage comes with a limit.
While searching, I came across a dataset in geotiff format for landcover as well as a processed version of world elevation. Elevation data comes in various resolutions (250m, 500m, 1km), landcover is 500m.
So how do we read it? In python its quite easy to use osgo/gdal library and read all bands. Geotiff is a raster file and values can be packed in every band (which is basically a 2d array).
There are many use cases in GIS world, where the information has to be aggregated, an easy way to achieve this is via gridding or binning, where the area of interest is divided into small sections called grids or bins.
These sections are mostly of rectangular form (which can be easily converted into geotiffs), but in some cases even circles or hexagons are also used.
You can read a good tutorial from mapbox using Qgis with a mmqgis plugin here.
Projections in GIS are commonly referred to by their “EPSG” codes, these are identifiers managed by the European Petroleum Survey Group.
One common identifier is “EPSG:4326”, which describes maps where latitude and longitude are treated as X/Y values.
Spherical Mercator has an official designation of EPSG:3857. However, before this was established, a large amount of software used the identifier EPSG:900913. This is an unofficial code, but is still the commonly used in many GIS systems.
Recently I was trying to build a quick geo lookup service in python, which could be used like an “info tool” in QGIS. This task is trivial in almost all geospatial databases, however I wasn’t able to find much online around querying a shape file.
In this post I will demonstrate a simple python code to query a shape file which contains world countries. The file can be downloaded from here.