This tutorial demonstrates how to access and visualize crime data for Denver, Colorado.
Data Intensive Tutorials
Scientific programming can be used to efficiently work with many different types of data. Rather than performing tasks manually, you can write code that opens, cleans and processes your data. However, often figuring out how to perform a specific task in
Python or another programming language can be tricky. In the tutorials below, you will learn how to use
If there is a tutorial you’d like to see covered, reach out to us on Twitter @EarthLabCU.
Tutorials that cover data intensive topics
This tutorial outlines the process of installing the Google Earth Engine Python API client.
This tutorial demonstrates how to convert Modis sinusoidal tile grid positions to latitude and longitude in Python.
This tutorial demonstrates how to convert Landsat 8 path/row coordinates to latitude and longitude in Python.
This tutorial shows how to make interactive maps in Python with folium.
This tutorial shows how to compute and plot contour lines for elevation from a raster DEM (digital elevation model).
This tutorial shows how to compute the slope and aspect from a digital elevation model in Python.
This tutorial outlines how to use PySAL to perform spatial regression in Python.
This tutorial shows how to compute raster statistics like the mean and variance around buffered spatial points in Python.
This tutorial demonstrates how to use climata to acquire streamflow data in and around Boulder, Colorado.
This tutorial demonstrates how to compute 2d spatial density and visualize the result using storm event data from NOAA.
This tutorial shows how to use rpy2 in a Jupyter notebook to run both R and Python.
This tutorial outlines the use of the Cenpy package to search for, and acquire specific census data.
This tutorial demonstrates how to access SMAP data, and how to generate raster output from this data.