Joe McGlinchyJoe McGlinchy has contributed to the materials listed below. Joe is a Remote Sensing Specialist with the Analytics Hub at Earth Lab.
Course lessons are developed as a part of a course curriculum. They teach specific learning objectives associated with data and scientific programming. Joe McGlinchy has contributed to the following lessons:
Designing and developing data workflows can help you complete your work more efficiently by allowing you to repeat and automate data tasks. Learn how to design and develop efficient workflows to automate data analyses in Python.
Identifying aspects of a workflow that can be modularized and tested can help you design efficient and effective data workflows. Learn best practices for designing efficient data workflows.
Designing and developing data workflows can complete your work more efficiently by allowing you to repeat and automate data tasks. Learn how to design and develop efficient workflows to automate data analyses in Python.
This lesson covers how to crop a raster dataset and export it as a new raster in Python
This lesson introduces what vector data are and how to open vector data stored in shapefile format in Python.
Sometimes you need to manipulate multiple rasters to create a new raster output data set in Python. Learn how to create a CHM by subtracting an elevation raster dataset from a surface model dataset in Python.
This lesson introduces the raster geotiff file format - which is often used to store lidar raster data. You will learn the 3 key spatial attributes of a raster dataset including Coordinate reference system, spatial extent and resolution.
There are a suite of powerful open source python libraries that can be used to work with spatial data. Learn how to use geopandas, rasterio and matplotlib to plot and manipulate spatial data in Python.