# Data Intensive Tutorials

## Tutorials

Tutorials that cover data intensive topics

## Visualizing hourly traffic crime data for Denver, Colorado using R, dplyr, and ggplot

This tutorial demonstrates how to access and visualize crime data for Denver, Colorado.

## Calculating the area of polygons in Google Earth Engine

This tutorial demonstrates polygon creation, perimeter and area calculations, and visualization using the JavaScript interface to Google Earth Engine.

This tutorial shows how to acquire Modis satellite data using Python and the pyModis package.

## Introduction to the Google Earth Engine Python API

This tutorial outlines the process of installing the Google Earth Engine Python API client.

## Introduction to the Google Earth Engine code editor

This tutorial introduces the code editor in Google Earth Engine and shows how to use LandSat imagery using the JavaScript API.

## Get Modis sinusoidal tile grid positions from latitude and longitude coordinates in Python

This tutorial demonstrates how to convert Modis sinusoidal tile grid positions to latitude and longitude in Python.

## Convert Landsat 8 path/row to lat/lon coordinates in Python

This tutorial demonstrates how to convert Landsat 8 path/row coordinates to latitude and longitude in Python.

## Running Parallel Jobs on JupyterHub with ipyparallel

This tutorial shows how to run simple parallel jobs on JupyterHub with Python.

## Using Leaflet and Folium to make interactive maps in Python

This tutorial shows how to make interactive maps in Python with folium.

## Getting started with the PetaLibrary

This tutorial explains how members of Earth Lab can gain access to the PetaLibrary at the University of Colorado Boulder. It also outlines the process for setting up Globus to transfer files between endpoints (e.g., your local machine and the PetaLibrary).

## Visualizing elevation contours from raster digital elevation models in Python

This tutorial shows how to compute and plot contour lines for elevation from a raster DEM (digital elevation model).

## Calculating slope and aspect from a digital elevation model in Python

This tutorial shows how to compute the slope and aspect from a digital elevation model in Python.

## Introduction to spatial regression in Python

This tutorial outlines how to use PySAL to perform spatial regression in Python.

## Computing raster statistics around buffered spatial points Python

This tutorial shows how to compute raster statistics like the mean and variance around buffered spatial points in Python.

## Running Parallel Jobs on JupyterHub in R

This tutorial will demonstrate how to use the parallel R package to run simple parallel jobs within the R kernel on JupyterHub.

## Acquiring streamflow data from USGS with climata and Python

This tutorial demonstrates how to use climata to acquire streamflow data in and around Boulder, Colorado.

## Computing and plotting 2d spatial point density in R

This tutorial demonstrates how to compute 2d spatial density and visualize the result using storm event data from NOAA.

## Coloring lidar point clouds with RGB imagery in R

This tutorial shows how to color lidar point clouds with RGB imagery, using freely available data from the National Ecological Observatory Network (NEON).

## Using R and Python in the same Jupyter notebook

This tutorial shows how to use rpy2 in a Jupyter notebook to run both R and Python.

## Acquiring U.S. census data with Python and cenpy

This tutorial outlines the use of the Cenpy package to search for, and acquire specific census data.

## Create rasters from HDF5 SMAP data in Python

This tutorial demonstrates how to access SMAP data, and how to generate raster output from an HDF5 file.