Get more data in less time — We live in a world of big data. Often, big data is organized as a large collection of small datasets (i.e., one large dataset comprised of multiple files). Obtaining these data is often frustrating because of the download (or acquisition burden). …

Use NumPy’s built-in functions to make sorting simple — 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. …

Art and data all-in-one — 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…

This could be the most-used remote sensing index — 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. …

Whether photography, topography, or something else it can probably be analyzed with a sliding window — 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…

Scale-up your geoprocessing workflows with Python — 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…

How to summarize raster data for polygon zones — 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. …

All you need to be successful is time — 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. …

A powerful data format once you break the learning curve — 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…