Today, I take a look at the Python Libraries useful when dealing with Geospatial Data. The most popular GIS; QGIS and ArcGIS are developed on Python thus giving us the power to extend their tools to suit our needs in the organization.
For Instance, QGIS offers the “Plugin Builder” tool that is focused on personal tool creation by individuals or organization to do specific tasks as required. To do this, there are some Python Libraries that are required. Anyone who has worked with scripts in either QGIS, ArcGIS,Jupyter or GeoDjango for geospatial data analysis must have interacted with some, if not all, of these libraries.
My experience with most applications more so QGIS, Jupyter and GeoDjango, has enabled me draft this post as most of these libraries seem confusing to many and can be a ‘nightmare’ when developing a GIS system or Portal.
One great resources that these Python libraries have are their documentation and the vibrant online community supporting the development and maintenance of these libraries.
These Libraries include but not limited to the following.
- GEOJSON – This is a Python Library and also a data format. Its one of the most popular data format being used in the Geospatial industry today.
- GEODJANGO – This is a module found in Django for handling Geospatial data in Django projects.Its commonly used with PostgreSQL and PostGIS
- PYPROJ – This is a wrapper around the PROJ.4 library that works with projections and performs transformations in Python.
- NUMPY – is used to power some geospatial libraries such as GDAL and has the capability to handle large arrays, matrices and math functions needed in spatial analysis.
- MATPLOTLIB – is used in plotting graphs from data for display on screens or displays in formats such as images and SVG. This Library is so wide with extensive functionality. Matplotlib is widely used in Jupyter and MATLAB for its powerful functionality.
- SHAPELY – is a Python library used for spatial analysis of geometries and uses the Cartesian coordinate system. It works hand-in-hand with the GEOS library. Mostly evident in CAD systems.
- DESCARTES – This library provides a nicer integration of Shapely geometry objects with Matplotlib.
- PANDAS – is a data analysis library that structures your data in a spreadsheet-like manner for further computations. Pandas simplifies working with data from different sources during analysis.
- PYSHP – This is pure Python and is used to import and export shapefiles. Its works almost similar to GDAL but makes it easier for operations.
- Python – is a Python interpreter for a console that replaces the normal Python console when running and testing Python code from your terminal. This is an advanced interpreter with some cool features, such as Tab completion for commands instead of typing them manually.
- PySAL – is a geospatial computing library that’s used for spatial analysis. Creating models and running simulations directly from Python code are some of the many library functions that PySAL offers. PySAL enhances visualization of spatial data and works very well when combined with tools such as Matplotlib.
- GDAL/OGR – (Geospatial Data Abstraction Library) – This is a translator Python library that deals with a number of out-of the-box tasks such as format transformations, identifying projections in data and operations such as the generation of contours from elevation data. Its a key library used for Raster and Vector data analysis.Used in most GIS platforms such as ArcGIS, QGIS, GeoDjango, GRASS GIS, gvSIG,MangoMap, MapServer and Envi
The installation and use of these libraries can be found on respective websites on the internet.
Some of the libraries discussed above work on the background when performing spatial analysis on data.They work hand-in-hand with each other.
For more details on these libraries don’t hesitate to leave your comment below.
Note: Training on the use of these libraries for your geospatial data is also available. Leave a comment for more info.