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Installing GDAL for Python with pip , the package installation package, is not as easy as it may seem. If you try to install GDAL to a python environment with a command like python -m pip install gdal it will initially seem like the installation is going to happen. However, you will receive a message…
The Geospatial Data Abstraction Library (GDAL) is a fundamental package for spatial analysis with Python. GDAL is a powerful package with a lot of functionality. However, it is notoriously difficult to install. There are two reliable ways to install the GDAL python package: from the conda-forge channel using the conda installer or using pip to…
Just as spatial joins can be performed in QGIS with the Join Attributes by Nearest tool, the QGIS Python API (PyQGIS) can be used to perform spatial joins in the same manner. This article will demonstrate, with code, how to perform a spatial join with PyQGIS. This example will join the attributes of a line…
A spatial join is operation performed on spatial vector data that links the attributes of one feature set to the attributes of the closest feature in a second set. The result of a spatial join is a new feature set (vector layer) that contains the attributes of both the input feature and the closest feature…
For many GIS analyses, it is necessary to calculate the length, area, or perimeter of line or polygon features (i.e. geometries). These operations are referred to as calculating geometry. The QGIS tool that calculates feature geometries is the Field Calculator tool. There are some nuances and potential pitfalls when calculating geometry in QGIS. This guide…
Attribute tables display information about geographic features and are an essential element of any GIS. It is often necessary to subset, select, or remove features based on attribute data. Learning quick ways to accomplish these tasks can really speed up your GIS efficiency. One common, and very helpful, attribute table view is to show only…
Finding the shape of a nested list or tuple is much more complicated than finding the shape of a single element because each element in a list of lists or tuple of tuples can have a different dimension. Unlike NumPy arrays, lists and tuples aren’t required to have a set number of rows or columns….
Clipping raster layers is a basic operation in many GIS workflows. Since rasters often cover large spatial extents, it is often beneficial to extract (or clip) only the portion of the raster that pertains to a specific study area. This can greatly improve data load and rendering times and decrease the time needed for tools…
With GIS analysis it’s a common occurrence that multiple raster tiles are required to cover a study area. For many analyses, and specifically hydrological analyses, a seamless, single raster is needed. The solution for combining multiple raster layers is to perform a raster merge, also referred to as a raster mosaic. QGIS uses the the…
NumPy is a fundamental module used in many Python programs and analyses because it conducts numerical computations very efficiently. However, for those new to NumPy, it can be difficult to grasp at first. Specifically, understanding array indexing and sorting quickly becomes complex. Fortunately, NumPy has some built-in functions that make performing basic sorting operations quite…
Using attribute fields in calculations is one of the most fundamental components of GIS analysis. Field calculations are what makes it possible to take data linked to spatial features and analyze features based on what they are and where they are. Manually performing field calculations in QGIS can be tedious, especially if you’re working with…
Have you ever run into the problem where default symbology options aren’t enough and you need to create a custom color ramp? If so, you are not alone. I’m pretty picky about my visuals. Everything has to be just right. A big part of that is making sure color ramps are fit for both purpose…
Python tuples and lists are objects that contain multiple values and it is a common task to determine the number of elements (or shape) in a tuple or list object. While this seems simple enough, there is one nuance that makes it a little more complicated. Shape of a One-dimensional Python Tuple or List The…
False-color satellite images can be very useful to visually analyze different landscape characteristics. They also look really cool and are used to make very artistic maps and displays. False-color satellite images are created by displaying different band combinations with different colors. For example, a color near-infrared image is created by displaying data from the near-infrared…
Vegetation indices are a staple remote sensing product and the normalized difference vegetation index (NDVI) may be the most widely used vegetation index. To calculate NDVI you simply need appropriate imagery and a program that allows you to interact with the image data. QGIS is a great, free option for a GIS program that provides…
QGIS is great for analyzing spatial data but it’s often easier to export layer attributes for additional analysis in a program like Excel or R. To do that you need to export the data from a layer’s attribute table to a format that is readable by other programs. To export a QGIS attribute table as…
There’s a good chance you’ve done something today that used a sliding window (also known as a moving window) and you didn’t even know it. Have you done any photo editing? Many editing algorithms are based on moving windows. Do you do terrain analysis in GIS? Most topographic raster metrics (slope, aspect, hillshade, etc.) are…
Filling NumPy arrays with a specific value is a typical task in Python. It’s common to create an array, then initialize or change some values, and later reset the array to a starting value. It’s also common to initialize a NumPy array with a starting value, such as a no data value. These operations may…
When you work with spatial data it’s inevitable that you will need to implement information from both a vector and raster data source for the same location. This task can easily be accomplished manually, but it often becomes quite cumbersome when the process must be automated across a large number of features, time periods, and/or…
Learn to build a simple web map with the OpenLayers JavaScript library. This tutorial will follow the OpenLayers Quick Start guide, with a few additions, to get you started building web maps. By the end of this tutorial you will know how to complete the following three tasks. Display a basic web map on a…
The QGIS Python API (PyQGIS) makes it really simple automate a feature dissolve. PyQGIS gives access to all the QGIS processing tools directly from the Python interface. This tutorial will demonstrate three different dissolve examples with Python in QGIS. Basic Dissolve with Python First, lets dissolve polygon features into a single polygon, this is the…
It is a common need to summarize information from a gridded dataset within an irregularly shaped area. While at first glance this may seem simple, reconciling differences between raster (gridded) and vector (polygon) datatypes can quickly become complicated. This article shows how to implement a zonal statistics algorithm in Python in 4 steps. Load raster…
Everyone is good at something. With creativity and determination (and maybe some marketing) others will pay to learn from you. Teaching online courses is a great way to earn passive income. The barriers for entry are low, and if your online course doesn’t pan out, the only thing you’ll lose is time. Chances are, you’ll…
Gridded, spatial data are commonly stored in NetCDF files. This is especially true for climate data. NetCDF files offer more flexibility and transparency than some traditional raster formats by supporting multiple variables and detailed metadata. Because of the metadata and file structure NetCDF files can be more difficult to access than traditional raster formats. This…
Aerial imagery is used for purposes ranging from military actions to checking out the backyard of a house you might buy. Our human brains can easily identify features in these photographs, but it’s not as simple for computers. Automated analysis of aerial imagery requires classification of each pixel into a land cover type. In other…
Did you know you can write parallel Python code that will run on your laptop and on a super computer? You can, and it’s not as difficult as you might expect. If you already write code for asynchronous parallelization then you won’t even have to do much restructuring. High Performance Computing (HPC) distributes pieces of jobs across…
A flexible method to speed up code on a personal computer Do you wish your Python scripts could run faster? Maybe they can. And you won’t (probably) have to buy a new computer, or use a super computer. Most modern computers contain multiple processing cores but, by default, python scripts only use a single core….
Most popular computer programs have free alternatives. Switching to these free programs could save you some dough and give you earlier access to new features. Open source software is licensed so that its code can be used in other applications without legal violations. This creates an ideal environment for creativity, innovation and improvement in software…
Important information describing the data contained in netCDF files is embedded directly in the netCDF file. Accessing and reading of this information (i.e. metadata) can help you automate tasks, debug code, and validate results easily. This tutorial covers how to access information describing the metadata, dimensions, and variables contained in a netCDF file. For the…
Many geographic data are store in netCDF format. This is especially true for climate data. Interacting with netCDF data files is an important skill for any geographer, physical scientist or data scientist to have. This tutorial will get you started using the netCDF4 Python package to read netCDF data. First, you’ll need to install netCDF4….
Use the random forests algorithm to classify image segments into land cover categories. This post is a continuation of Geographic Object-Based Image Analysis (GeOBIA). Herein, we use data describing land cover types to train and test the accuracy of a random forests classifier. Land cover data were created in the previous post. Step-be-step video instructions…
It is very simple to clip vector layers with PyQGIS. This tutorial will demonstrate how to use Python in QGIS to clip a line layer with a polygon layer. First, import the processing module and set the paths to the line, polygon and output layers. The next step is to run the clip tool. This…
This tutorial will walk you through segmenting and classifying high resolution imagery using Python. Part 1 of this tutorial teaches how to segment images with Python. After you have completed Part 1, Part 2 will teach how to use machine learning methods to classify segments into land cover types with Python. YouTube videos give step-by-step…
This tutorial will demonstrate how to measure the distance between two geometries in QGIS using Python. First we will measure the distance between points in the same layer. Then we will measure the distance between features in a points layer and a lines layer.
Creating a buffer around features is a common spatial analysis task. This blog post demonstrates how to customize and automate buffer operations with Python in QGIS (PyQGIS).
A common operation with vector layers is to select features based on attribute values and save those features to a new vector layer (i.e. shapefile). This tutorial will walk you through how to create a new shapefile from selected features. We’ll select some features, then save those features to a new shapefile. First, we’ll open…
In this tutorial, we’ll walk through how to create a shapefile from scratch with the QGIS Python (PyQGIS) API. You shouldn’t need to do this, but if you’re running into problems where functions don’t show up you can import the qgis module at the beginning. This usually isn’t a problem but there have been a…
With the PyQGIS API you are able to use Python in QGIS to automate the creation of map layouts. There are two reasons this is really awesome! First, once you’ve written the initial code to set up the map, you can change map symbology or map layers and exactly replicate layout. Second, once you have…
This tutorial will teach you how to render (print or save) a QGIS layer as an image using the PyQGIS Python API. Automating layer rendering can be very useful to create and save visualizations that have consistent styling, extent and format. The first thing you need to do is create a QImage. Here we create…
In a previous tutorial I showed you how to access raster values and data with the Geospatial Data Abstraction Library (GDAL). This tutorial will show you how to create a raster with GDAL. Start by importing the gdal and osr Python modules. The osr module is used for handling spatial references. Also import numpy. Now…
PyQGIS has some methods for accessing single values in a raster, but the best way to access raster data is using the Geospatial Data Abstraction Library, or GDAL. GDAL comes pre-installed on the QGIS Python interpreter, so if you access the Python interpreter from QGIS (Plugins > Python Console) you won’t have to install any…
Deleting features is a common operation in QGIS. It is easy to automate this task with Python, allowing you to quickly delete features based on any conditions you set. First, select the layer from which you want to delete features. This line of code will select the first layer with the name ‘layer name’. You…
Geometry is what defines the shape and spatial location of a feature. Features can be points, lines, or polygons, can be anywhere on earth, and can be represented by coordinates in a number of different units (depending on the coordinate reference system, or projection). Points are represented by a single coordinate pair while lines and…
The QGIS Python API (PyQGIS) makes it possible to automate creation of point features. This tutorial will show you how to add features to an existing shapefile. For this example we will assume the layer containing the point features is already opened in the QGIS table of contents. First, we save all layers with a…
Sometimes certain analysis may require data in a tabular format (as opposed to the gridded format of NetCDF). This tutorial will take you through how to convert a NetCDF (or other raster/gridded data) to an R data frame, which can then used for analysis or saved as a CSV file. Read the NetCDF file First,…
It is tedious, and not practical, to manually type in values for array creation. In this tutorial we will go through methods for automating array creation and importing tabular data into numpy arrays. Creating empty arrays With numpy you don’t actually create an ‘empty’ array. But you can create an array without intializing specific values. This can be useful…
We’ve gone through the basics to manually create arrays of different dimensions and shapes. There will be times that you will want to query array shapes, or automatically reshape arrays. This tutorial will show you how to use numpy.shape and numpy.reshape to query and alter array shapes for 1D, 2D, and 3D arrays. Different methods are required to find…
Creating a simple array After numpy is installed, we can begin to create arrays. First, we’ll need to import numpy into our python project. Here I use the statement, import numpy as np, to limit my typing later. This code will allow me to use np in my script to represent instead of typing the full numpy everytime. Then, we can create a simpy…
Check Installation First, check to see if you already have numpy installed. From the terminal, you can use pip to do this. If numpy is installed you will get output similar to this. If numpy is not installed no output will be shown. Install numpy numpy can be installed simply using pip. Check version You can check the numpy version using pip show as demonstrated above. Video tutorial This…
Requirements: Python version 2.7 or greater Beginner skills with python Desire to learn numpy Objectives: Teach the basics of using numpy in python Provide data science applications of numpy Introduce advanced numpy concepts What is numpy? numpy is a powerful python package for handling arrays. It uses C libraries to optimize procedures that would otherwise be slow/computationally expensive in python. What can I do…