Modern versions of QGIS comes with a handy command-line utility called qgis_process. This allows you to access and run any Processing Tool, Script or Model from the Processing Toolbox on a terminal. This is very useful for automation since it doesn’t require you to open QGIS or manually click buttons. You can run the algorithms in a headless-mode and even schedule them to run them at specific times.
This post covers the following topics
How to launch qgis_process command on Windows, Mac and Linux.
How to find the parameters and values for each algorithm and build your command
Example showing how to do a spatial join on the command-line using the Join Attributes by Location algorithm
Example showing how to run a model on the command line to automate a complex workflow
Want to follow along? You can download the data package containing all the datasets used in this post. Before running each command, make sure to replace the paths in the commands with the paths on your computer.
In this post, I describe how we can use built-in QGIS processing tools to create a workflow to split polygons into equal parts. Using a clever algorithm and Feature Iterator tool in the Processing Framework, we can easily split all features in a given polygon layer into equal parts.
The algorithm for splitting any polygon shape into equal parts is described in this post PostGIS Polygon Splitting by Paul Ramsey. We will see how this can be implemented in QGIS.
In this post, I will outline techniques for computing weighted-centroids in both QGIS and Google Earth Engine. For a polygon feature, the centroid is the geometric center. It can also be thought of as the average coordinate of all points within the polygon. There are some uses cases where you may want to compute a weighted-centroid where some parts of the polygon gets higher ‘weight’ than others. The main use-case is to calculate a population-weighted centroid. One can also use Night Lights data as a proxy for urbanized population and calculate a nightlights-weighted centroid. Some applications include:
Regional Planning: Locate the population-weighted centroid to know the most accessible location from the region.
Network Analysis: For generating demand points in location-allocation analysis, you need to convert demands from regions to points. It preferable to compute populated-weighted centroids for a more accurate analysis.
Do check out this twitter-thread by Raj Bhagat P for more discussion on weighted centroids.
I recently taught a 1-month long course on GIS Applications in Urban and Regional Planning. We explored how GIS can be applied to solve problems in 6 different thematic areas. In this post, I will outline different applications and show concrete examples of using open-datasets and open-source GIS software QGIS.
The full course material – including data packages and PDF handouts – is now available for free download. Scroll down and find the download link at the end of the post.
Rainfall is arguably the most frequently measured hydro-meteorological variable. It is a required input for many hydrological applications like runoff computations, flood forecasting as well as engineering design of structures. However, rainfall data in its raw form contain many gaps and inconsistent values. Therefore it is important to do rigorous validation of rain-gauge observation before incorporating them into analysis.
World Bank’s National Hydrology Project (NHP) prescribes a set of primary and secondary validation methods in the Manual of Rainfall Data Validation. Of particular interest to me are the spatial methods aimed to identify suspect values by comparison with neighboring stations. This spatial homogeneity test requires complex spatial and statistical data processing that can be quite challenging. I got an opportunity to work on a project that required automating the entire process of identifying and testing suspect stations. I ended up implementing it in QGIS using just Expressions and Processing Modeler. The whole solution required no custom code and was easily usable by an analyst in the QGIS environment. In this post, I will explain the details of the test and show you how you can use similar techniques for your own analysis.
This workflow was presented as a live session on QGIS Open Day. You can watch the recording to understand the concepts and implementation.
When working with raster data, you may sometimes need to deal with data gaps. These could be the result of sensor malfunction, processing errors or data corruption. Below is an example of data gap (i.e. no data values) in aerial imagery.
In a previous post, I showed how to use the aggregate function to find neighbor polygons using QGIS. Using aggregate functions on the same layer allows us to easily do geoprocessing operations between features of a layer. This is very useful in many analysis that would typically require writing custom python scripts.
Here I demonstrate another powerful function array_foreach that allows one to iterate over other features in QGIS expressions – enabling even more powerful analysis by writing just a single expression.
Generating pseudo-random data is important for many aspects of research work. QGIS provides for many methods of generating random points to facilitate this.
Recently, I ran into a problem where I wanted to generate random points inside a polygon – but I wanted the random points to have a certain distribution. I wanted to generate a dataset showing employee home locations for a company. Given a city boundary and the location of office, I wanted to have a point layer that showed where the employees lived. A simple ‘Random points within Polygon’ algorithm would not work here, since the distribution of points would not be uniform within the city.
Table Joins are a way to join 2 separate layers based on a common attribute value. QGIS has a Join Attributes By Field Value algorithm that allows you to table joins. A limitation of this algorithm is that the field values must match exactly. If the values differ slightly – the join will fail. There are many times where you are trying to join 2 layers from different sources and they contain values which are similar but may not match exactly. Fortunately QGIS now has built-in fuzzy string matching functions that can be used – along with Aggregate function – to do table join based on fuzzy matches.