Matplotlib has functionality to created animations and can be used to create dynamic visualizations. In this post, I will explain the concepts and techniques for creating animated charts using Python and Matplotlib.
I find this technique very helpful in creating animations showing how certain algorithms work. This post also contains Python implementations of two common geometry simplification algorithms and they will used to create animations showing each step of the algorithm. Since both of these implementations use a recursive function, the technique shown in the post can be extended to visualize other recursive functions using matplotlib. You will learn how to create animated plots like below.
Many applications require replacing missing pixels in an image with an interpolated value from its temporal neighbours. This gap-filling technique is used in several applications, including:
Replacing Cloudy Pixels: You may want to fill gap in an image with the best-estimated value from the before and after cloud-free pixel.
Estimating Intermediate Values: You can use this technique to compute an image for a previously unknown time-step. If you had population rasters at 2 different years and want to compute a population raster for an intermediate year using pixel-wise linear interpolation.
Preparing Data for Regression: All of your independent variables may not be available at the same temporal resolution. You can harmonize various dataset by generating interpolated raster at uniform or fixed time-steps.
Google Earth Engine can be used effectively for gap-filling time-series datasets. While the logic for linear interpolation is fairly straightforward, data preparation for this in GEE can be quite challenging. It involves use of Joins, Masks and Advanced Filters. This post explains the steps with code snippets and builds a fully functioning script that can be applied on any time-series data.
Most optical satellite imagery products come with one or more QA-bands that allows the user to assess quality of each pixel and extract pixels that meet their requirements. The most common application for QA-bands is to extract information about cloudy pixels and mask them. But the QA bands contain a wealth of other information that can help you remove low quality data from your analysis. Typically the information contained in QA bands is stored as Bitwise Flags. In this post, I will cover basic concepts related to Bitwise operations and how to extract and mask with specific quality indicators using Bitmasks.
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.
Google Earth Engine makes it easy to compute statistics on gridded raster datasets. While calculating statistics on imagery datasets is easy, special care must be taken when working with population datasets. In this post, I will outline the correct technique for computing statistics for population rasters and aggregating pixels.
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.
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.
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.
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.
Many useful climate and weather datasets come as gridded rasters. The techniques for working with them is slightly different than other remote sensing datasets. In this post, I will show how to work with gridded rainfall data in Google Earth Engine. This post also serves an an example of how to use the map/reduce programming style to efficiently work with such large datasets.