By |

QGIS comes bundled with a simplified version of the Natural Earth Countries shapefile that is suitable for quick map-making. The layer can be loaded into your canvas by typing the keyword world in the coordinates bar.

While this is useful, there is no single political map of the world that is accepted by every country of the world. There are many disputed international boundaries, and each country has its own version of accepted international boundaries. To allow mapmakers to adhere to local mapping regulations, Natural Earth also publishes Countries point-of-views shapefiles for many countries that depict the world map according to each country’s law and/or local conventions. We provide a simple script to replace the bundled world map with your country’s point-of-view layer.

Continue reading →

By |

Welcome to #PyQGISChallenge – Master QGIS Python Development in 30 Days! We are launching our PyQGIS Masterclass course on YouTube and have designed this challenge to help you learn how to customize QGIS using Python with scripts, custom algorithms, actions and plugins! Spend 30 minutes each day for the next 30 days to level-up your QGIS skills. This course is the result of my 15+ years of experience doing QGIS development – including building enterprise-grade plugins and deploying QGIS to thousands of users. I am really excited to share this content with you – completely free.

We will be posting short videos everyday and cover the full course material step by step. The material is designed to help you slowly ramp up and learn complex concepts! All you have to do is show up everyday and spend half an hour watching the videos and practicing the exercises. At the end, you can take up a mini-project and apply your newly acquired skills. Ready for #PyQGISChallenge? Read on to know the details.

This is an advanced course that assumes good working knowledge of both Python and QGIS. If you are new to programming, complete our Python Foundation for Spatial Analysis course first.

Continue reading →

By |

K-Means Clustering is a popular algorithm for automatically grouping points into natural clusters. QGIS comes with a Processing Toolbox algorithm ‘K-means clustering’ that can take a vector layer and group features into N clusters. A problem with this algorithm is that you do not have control over how many points end up in each cluster. Many applications require you to segment your data layer into equal sized clusters or clusters having a minimum number of points. Some examples where you may need this

  • When planning for FTTH (Fiber-to-the-Home) network one may want to divide a neighborhood into clusters of at least 250 houses for placement of a node.
  • Dividing a sales territory/ customers equally among sales teams with customers in the same region are assigned to the same team.

There is a variation of the K-means algorithm called Constrained K-Means Clustering that uses graph theory to find optimal clusters with a user supplied minimum number of points belonging to given clusters. Stanislaw Adaszewski has a nice Python implementation of this algorithm that I have adapted to be used as a Processing Toolbox algorithm in QGIS.

Warning!

I have heard feedback from users that this algorithm doesn’t work on all types of point distributions and may get stuck while finding an optimal solution. I am looking into ways to improve the code and will appreciate if you had feedback.

Continue reading →

By |

Google Earth Engine (GEE) is a powerful cloud-based system for analysing massive amounts of remote sensing data. One area where Google Earth Engine shines is the ability to calculate time series of values extracted from a deep stack of imagery. While GEE is great at crunching numbers, it has limited cartographic capabilities. That’s where QGIS comes in. Using the Google Earth Engine Plugin for QGIS and Python, you can combine the computing power of GEE with the cartographic capabilities of QGIS. In this post, I will show how to write PyQGIS code to programmatically fetch time-series data, and render a map template to create an animated maps like below.

Continue reading →

By |

When you want to buffer features that are spread across a large area (such as global layers), there is no suitable projection that can give you accurate results. This is the classic case for needing Geodesic Buffers – where the distances are measured on an ellipsoid or spherical globe. This post explains the basics of geodesic vs. planar buffers well.

Continue reading →